WO2019079829A9 - Method of preprocessing and screening auscultatory sound signals - Google Patents

Method of preprocessing and screening auscultatory sound signals Download PDF

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Publication number
WO2019079829A9
WO2019079829A9 PCT/US2018/056956 US2018056956W WO2019079829A9 WO 2019079829 A9 WO2019079829 A9 WO 2019079829A9 US 2018056956 W US2018056956 W US 2018056956W WO 2019079829 A9 WO2019079829 A9 WO 2019079829A9
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Prior art keywords
auscultatory
auscultatory sound
signal
sound
data
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PCT/US2018/056956
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French (fr)
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WO2019079829A1 (en
Inventor
Sergey A. Telenkov
Robin F. Castelino
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Ausculsciences, Inc.
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Application filed by Ausculsciences, Inc. filed Critical Ausculsciences, Inc.
Priority to CA3079686A priority Critical patent/CA3079686A1/en
Priority to EP18803820.2A priority patent/EP3700427A1/en
Publication of WO2019079829A1 publication Critical patent/WO2019079829A1/en
Publication of WO2019079829A9 publication Critical patent/WO2019079829A9/en
Priority to US16/854,894 priority patent/US11284827B2/en
Priority to US17/509,018 priority patent/US20220061797A1/en
Priority to US17/679,072 priority patent/US11896380B2/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/02—Stethoscopes
    • A61B7/04—Electric stethoscopes

Definitions

  • FIG. 1 illustrates a block diagram of a coronary-artery-disease detection system
  • FIG. 2 illustrates a first aspect of a data recording module and a first aspect of an associated docking system, in accordance with a first aspect of the coronary-artery-disease detection system illustrated in FIG. 1;
  • FIG. 3 illustrates a fragmentary view of a human thorax and associated prospective locations of auscultatory sound sensors associated right, sternum and left, second, third, fourth and fifth, inter-costal spaces, left posterior locations at the second and third inter-costal spaces, and locations proximate to the heart apex;
  • FIG. 4 illustrates a second aspect of a data recording module and a second aspect of an associated docking system, in accordance with a second aspect of the coronary-artery- disease detection system illustrated in FIG. 1;
  • FIG. 5a illustrates an auscultatory sound sensor coupled to the skin of a test-subject, by bonding via associated adhesive layers or surfaces on both sides of an adhesive interface;
  • FIGS. 5b and 5c each illustrate an auscultatory sound sensor that is detached, and therefore fully decoupled, from the skin of a test-subject, wherein FIG. 5b illustrates the associated adhesive interface detached from the skin of the test-subject, and FIG. 5c illustrates the associated adhesive interface detached from the auscultatory sound sensor;
  • FIGS. 5d through 5g each illustrate an auscultatory sound sensor that is partially coupled to, but debonded from, the skin of a test-subject;
  • FIG. 6 illustrates a test-subject reclined on a surface, with their torso inclined while capturing auscultatory sound signals from a plurality of auscultatory sound sensors attached to the thorax of the test-subject;
  • FIG. 7 illustrates a flowchart of a first aspect of an associated auscultatory-sound sensing process that incorporates a process for detecting a decoupling of the associated auscultatory sound sensors from the skin of the thorax of a test-subject being diagnosed for a prospective abnormal cardiovascular condition, wherein the decoupling-detection process occurs after each block of breath-held auscultatory sound time-series data is acquired, and is based upon scaled time-series data and responsive to an associated pre-determined debond- detection threshold;
  • FIG. 8 illustrates a flowchart of a first aspect of a process for acquiring auscultatory sound signals from the associated auscultatory sound sensors coupled to the skin of the thorax of the test-subject being diagnosed for a prospective abnormal cardiovascular condition;
  • FIG. 9 illustrates a plurality of six blocks of breath-held, auscultatory-sound-sensor time-series data recorded from an auscultatory sound sensor coupled to the skin of the thorax of a test-subject being diagnosed for a prospective abnormal cardiovascular condition;
  • FIGS. 10a- lOf respectively illustrate a simulation of successively recorded blocks of breath-held, sensor time-series data illustrated in FIG. 9, each illustrated with an expanded time scale, wherein FIGS. lOa-lOe illustrates a condition for which the auscultatory sound sensor is coupled to the skin of the test-subject, and FIG. lOf illustrates a condition for which the auscultatory sound sensor is decoupled from the skin of the test-subject;
  • FIG. 11 illustrates a flowchart of a process for determining a scale factor used to scale auscultatory-sound-sensor time-series data, the latter of which is analyzed to detect whether or not the associated auscultatory sound sensor is decoupled from the skin of the test-subject, wherein the scale factor provides for directly determining if the associated auscultatory sound sensor is detached from the skin of the test-subject;
  • FIGS. 12a-12f respectively illustrate time-series of the absolute values of the corresponding time-series data illustrated in FIGS. 10a- lOf, further illustrating a division of the block of breath-held, sensor time-series data into a plurality of associated data segments, with each data segment of sufficient width to nominally include sound from a single heartbeat, and with the peak values in each data segment marked, wherein FIGS. 12a-12e illustrates a condition for which the auscultatory sound sensor is coupled to the skin of the test-subject, and FIG. 12f illustrates a condition for which the auscultatory sound sensor is decoupled from the skin of the test-subject;
  • FIG. 13 illustrates an accelerometer on the thorax of a test-subject during a respiration cycle of the test-subject
  • FIG. 14 illustrates a breath-hold detection process
  • FIG. 15 illustrates a flowchart of a first aspect of a process for detecting whether or not an auscultatory sound sensor is debonded from the skin of a test-subject;
  • FIG. 16a illustrates an organization of data from an auscultatory sound sensor recorded by an auscultatory coronary-artery-disease detection system from a test subject
  • FIG. 16b illustrates a flow charge of a process for detecting noise in breath-held segments of data
  • FIG. 17 illustrates a flowchart of a noise detection process
  • FIG. 18 illustrates a flowchart of a process for generating a matched noise filter
  • FIG. 19 illustrates a flowchart of a process for evaluating the noise content in a spectral signal of an auscultatory sound signal
  • FIG. 20 illustrates a flowchart of a process for logging results from the noise- evaluation process of FIG. 19;
  • FIG. 21 illustrates a block diagram of a process for preprocessing and screening auscultatory sound signals
  • FIG. 22a illustrates a process for pre-processing auscultatory sound signals from auscultatory sound-or-vibration sensors
  • FIG. 22b illustrates a process for pre-processing electrographic signals from an ECG sensor
  • FIG. 23 illustrates a process for segmenting auscultatory sound signals from auscultatory sound-or-vibration sensors, by heart cycle based upon an electrographic signal from an ECG sensor, and by heat phase based upon the auscultatory sound signals;
  • FIG. 24 illustrates an auscultatory sound signal from an auscultatory sound-or- vibration sensor
  • FIG. 25 illustrates a corresponding electrographic signal from an ECG sensor, in correspondence with the auscultatory sound signal illustrated in FIG. 24;
  • FIG. 26 illustrates a process for identifying heart-cycle boundaries in an electrographic signal from an ECG sensor
  • FIG. 27 illustrates a first process for generating an envelope of a signal, which is called from the process illustrated in FIG. 26;
  • FIG. 28 illustrates an envelope, and associated peaks, of a portion of the electrographic signal illustrated in FIG. 25, generated in accordance with the process illustrated in FIGS. 26 and 27;
  • FIG. 29 illustrates a process for validating the peaks of an electrographic signal, which is called from the process illustrated in FIG. 26;
  • FIG. 30 illustrates a plot of the auscultatory sound signal illustrated in FIG. 24 together with plot of an envelope of the corresponding electrographic signal illustrated in FIG. 25;
  • FIG. 31 illustrates a plot of the auscultatory sound signal illustrated in FIG. 24, but over a relatively greater total period of time, with the auscultatory sound signal segmented based upon the peaks of the envelope of the associated electrographic signal, and presented as a beat stack, each illustrating a corresponding associated heart beat;
  • FIG. 32 illustrates a second process for generating an envelope of a signal, which is called from the process illustrated in FIG. 23;
  • FIG. 33 illustrates a rectified auscultatory sound signal and an associate envelope thereof in accordance with the process illustrated in FIG. 32;
  • FIG. 34 illustrates a plot of the auscultatory sound signal that is shown rectified in FIG. 33, together with plots of the associated quadratic models of the associated envelopes in proximity to the associated SI and S2 heart sounds, illustrating time-points associated with zero-amplitude roots of the associated quadratic models that are used to locate associated heart phases;
  • FIG. 35 illustrates the auscultatory sound signal beat stack illustrated in FIG. 31, together with indicia showing the locations of the roots of the quadratic models associated with the SI and S2 heart sounds that are used to locate associated heart phases, with the auscultatory sound signals of the beat stack aligned with one another based upon the means of the roots of the S2 heart sounds;
  • FIG. 36 illustrates a process for identifying outliers in a diastole region of an ausculatory sound signal
  • FIG. 37 illustrates a flow chart of a process for selecting valid heart cycles and analyzing the results therefrom
  • FIG. 38 illustrates a portion an auscultatory sound signal during diastole, and associated noise power thresholds
  • FIG. 39a illustrates a auscultatory sound signals during diastole for a plurality of heart cycles
  • FIG. 39b illustrates the auscultatory sound signals during diastole for the plurality of heart cycles illustrated in FIG. 39a, with the heart cycles aligned with respect to the mean times of the S2 heart sound;
  • FIG. 40a illustrates the auscultatory sound signals during diastole for a plurality of heart cycles illustrated in FIG. 39b, including indications of the start of the Sl heart sound and the end of diastole;
  • FIG. 40b illustrates the auscultatory sound signals during diastole for the plurality of heart cycles illustrated in FIG. 40a, with the heart cycles temporally normalized and resampled;
  • FIG. 41 illustrates a cross-correlation process
  • FIG. 42 illustrates a image of a plurality of localize cross-correlation signals associated with auscultatory sound signals from a plurality of heart cycles
  • FIG. 43 illustrates a time-frequency analysis based on a continuous wavelet transform of an auscultatory sound signal during diastole
  • FIG. 44 illustrates a process for detecting coronary artery disease from features of cross-correlation images generated from analysis of auscultatory sound signals during diastole for a plurality of heart cycles;
  • FIG. 45 illustrates a display of results in a heart/arteries view and textual view
  • FIG. 46a illustrates a displace of a Receiver Operating Characteristic (ROC) curve
  • FIG. 46b illustrates line graph display of true positives, true negatives, false negatives, and false positives
  • FIG. 47 illustrates a display of a Heartbeat View comprising a graphical plot of the systolic and diastolic intervals of each heartbeat captured
  • FIG. 48 illustrated a display of a Stacked Heartbeat View
  • FIG. 49 illustrates a Bruit Identification View in a Line Graphs with Underfill mode
  • FIG. 50 illustrates a display of a Bruit Identification View in a Spectrogram mode
  • FIG. 51 illustrates a display of a Bruit Analysis View
  • FIG. 52 illustrates a comparison of a current test with a previous test, using the Bruit Identification View, Spectrogram mode, to confirm success of PCI;
  • FIG. 53 illustrates a comparison of a current test with a previous test, using the Bruit Identification View, Line Graph with Underfill mode, to confirm success of PCI.
  • an auscultatory coronary-artery-disease detection system 10 incorporates at least one auscultatory sound sensor 12 that is operatively coupled to a recording module 13 running at least a first portion 14.1 of a Data Recording Application (DRA) 14, 14.1 on a first specialized computer or electronic system comprising a first computer processor or FPGA (Field Programmable Gate Array) 15 and first memory 17 powered by a first power supply 19, which provides for recording and preprocessing auscultatory sound signals 16 from the at least one auscultatory sound sensor 12.
  • DRA Data Recording Application
  • the at least one auscultatory sound sensor 12 comprises a first group 12’ of three auscultatory sound sensors 12, 12 1 ’, 12 r , 12 3 ’ physically interconnected end-to-end with one another, and physically and electrically interconnected with a first wireless transceiver 18; and a second group 12” of three auscultatory sound sensors 12, 12 1 ”, 12 2 ”, 12 3 ” physically interconnected end-to-end with one another, and physically and electrically interconnected with the first wireless transceiver 18, with both groups 12% 12” of auscultatory sound sensors placed on the skin of the thorax 20 of a test-subject 22, in acoustic communication therewith.
  • the placement of the first group of auscultatory sound sensors 12’ in FIG. 1 is illustrated with the respective associated auscultatory sound sensors 12, 12 1 ’, 12 r , 12 3 ’ in substantial alignment with the corresponding respective third R3, fourth R4 and fifth R5, inter-costal spaces on the right side 20 R of the thorax 20, and the placement of the second group of auscultatory sound sensors 12” in FIG. 1 is illustrated with the respective associated auscultatory sound sensors 12, 12 1 ”, 12 2 ”, 12 3 ” in substantial alignment with the corresponding respective third L3, fourth L4 and fifth L5, inter-costal spaces on the left side 20 L of the thorax 20.
  • prospective left-side posterior sensor locations LP2 and LP3 illustrated in FIG. 3 respectively refer to at the second LP2 and third LP3 intercostal spaces locations on the posterior of the thorax 20.
  • prospective sensor locations HA-1 and HA-2 are proximate to the apex of the heart, either on the anterior or the posterior of the thorax 20.
  • the auscultatory sound sensors 12, 12 1 ’, 12 2 ’, 12 3 ”, 12 1 ”, 12 2 ”, 12 3 ” are located at the second S2, L2, third S3, L3 and fourth S4, L4 inter-costal spaces at the sternum S2-S4 and leftside L2-L4 of the thorax 20.
  • the term “auscultatory sound” is intended to mean a sound originating from inside a human or animal organism as a result of the biological functioning thereof, for example, as might be generated by action of the heart, lungs, other organs, or the associated vascular system; and is not intended to be limited to a particular range of frequencies— for example, not limited to a range of frequencies or sound intensities that would be audible to a human ear,— but could include frequencies above, below, and in the audible range, and sound intensities that are too faint to be audible to a human ear.
  • the term“auscultatory-sound sensor” is intended to mean a sound sensor that provides for transducing auscultatory sounds into a corresponding electrical or optical signal that can be subsequently processed.
  • the auscultatory sound sensors 12, 12 1 ’, 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” provide for transducing the associated sounds received thereby into corresponding auscultatory sound signals 16 that are preprocessed and recorded by an associated hardware-based signal conditioning/preprocessing and recording subsystem 25, then communicated to the first wireless transceiver 18, and then wirelessly transmitted thereby to an associated second wireless transceiver 26 of an associated wireless interface 26’ of an associated docking system 27, possibly running a second portion 14.2 of the Data Recording Application (DRA) 14, 14.2 on a corresponding second specialized computer or electronic system comprising an associated second computer processor or FPGA (Field Programmable Gate Array) 28 and second memory 30, both of which are powered by an associated second power supply 32, which together provide for recording and preprocessing the associated auscultatory sound signals 16 from the auscultatory sound sensors 12, 12 1 ’, 12 , 12 3 , 12 1 ”, 12 2 ”, 12 3 .
  • DPA Data Recording Application
  • the hardware-based signal conditioning/preprocessing and recording subsystem 25 includes an amplifier— either of fixed or programmable gain, — a filter and an analog-to-digital converter (ADC).
  • the analog-to-digital converter (ADC) is a 16- bit analog-to-digital converter (ADC) that converts a -2.25 to +2.25 volt input to a corresponding digital value of - 32, 768 to + 32, 767.
  • the amplifier gain is programmable to one of sixteen different levels respectively identified as levels 0 to 15, with corresponding, respective gain values of 88, 249, 411, 571, 733, 894, 1055, 1216, 1382, 1543, 1705, 1865, 2027, 2188, 2350 and 2510, respectively for one set of embodiments.
  • the amplifier gain is fixed at the lowest above value, i.e., for this example, 88, so as to provide for avoiding the relative degradation of the associated signal-to-noise ratio (SNR) that naturally occurs with the relatively high gain levels of the programmable-gain set of embodiments.
  • SNR signal-to-noise ratio
  • DISEASE DETECTION SYSTEM CORONARY ARTERY DISEASE DETECTION SYSTEM
  • the auscultatory coronary-artery-disease detection system 10 may further incorporate an ECG sensor 34, for example, in one set of embodiments, an ECG sensor 34’ comprising a pair of electrodes incorporated in a corresponding pair of auscultatory sound sensors 12, wherein the signal from the ECG sensor 34’ is also preprocessed and recorded by a different signal channel of the same hardware-based signal conditioning/preprocessing and recording subsystem 25 of the recording module 13 that is used to preprocess the signals from the one or more auscultatory sound sensors 12.
  • an ECG sensor 34 comprising a pair of electrodes incorporated in a corresponding pair of auscultatory sound sensors 12, wherein the signal from the ECG sensor 34’ is also preprocessed and recorded by a different signal channel of the same hardware-based signal conditioning/preprocessing and recording subsystem 25 of the recording module 13 that is used to preprocess the signals from the one or more auscultatory sound sensors 12.
  • the ECG sensor 34 may comprise a separate set of a pair or plurality of electrodes that are coupled to the skin of the test subject, for example, in one set of embodiments, a pair of signal electrodes 35, 35 +/ in cooperation with a ground electrode 35°, wherein, referring to FIG. 3 (illustrating the locations of the electrodes 35, 35 +/ , 35°), in accordance with one non-limiting embodiment, the signal electrodes 35, 35 +/ span the heart from diametrically-opposed quadrants of the torso 44, and the ground electrode 35° is located in a different quadrant, orthogonally displaced from a midpoint of a baseline connecting the signal electrodes 35, 35 +/ .
  • the signal electrodes 35, 35 +/ could span across the top of the top of the thorax 20, or at any pair of locations commonly used for conventional ECG tests.
  • the recording module 13 and docking system 27 may each incorporate a corresponding respective USB interface 36.1, 36.2 to provide for transferring corresponding auscultatory sound signals 16 and or an electrographic signal 37 - from an associated ECG sensor 34, 34’— from the recording module 13 to the docking system 27, for example, rather than relying upon the first 18 and second 26 wireless transceivers of an associated wireless interface 26’.
  • data may be transferred from the recording module 13 to the docking system 27 via a portable memory element, for example, either an SD memory card or a Micro SD memory card.
  • a portable memory element for example, either an SD memory card or a Micro SD memory card.
  • the functionality of the Data Recording Application (DRA) 14 is distributed across the recording module 13 and the docking system 27.
  • the Data Recording Application (DRA) 14 spans across the recording module 13 and the docking system 27, with a first portion 14.1 comprising the hardware- based signal conditioning/preprocessing and recording subsystem 25 operative on the recording module 13, and a remaining second portion 14.2 operative on the docking system 27.
  • the Data Recording Application (DRA) 14 is operative entirely on the recording module 13
  • the auscultatory sound sensor 12 provides for sensing sound signals that emanate from within the thorax 20 of the test-subject 22 responsive to the operation of the test- subject’s heart, and the resulting flow of blood through the arteries and veins, wherein an associated build-up of deposits therewithin can cause turbulence in the associated blood flow that can generate associated cardiovascular-condition-specific sounds, the latter of which can be detected by a sufficiently-sensitive auscultatory sound sensor 12 that is acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22.
  • the sound level of these cardiovascular-condition-specific sounds can be below a level that would be detectable by a human using a conventional stethoscope.
  • these sound levels are susceptible to detection by sufficiently sensitive auscultatory sound sensor 12 that is sufficiently acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22.
  • the auscultatory sound sensor 12 may be constructed in accordance with the teachings of U.S. Provisional Application No. 62/568,155 filed on 04 October 2017, entitled AUSCULTATORY SOUND SENSOR.
  • the auscultatory sound sensor 12 may be constructed in accordance with the teachings of U.S. Pat. Nos. 6,050,950, 6,053,872 or 6,179,783, which are incorporated herein by reference.
  • the auscultatory sound sensors 12, 12 1 ’, 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” are acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22 via an acoustic interface 40, for example, via a hydrogel layer 40’, that also functions as an associated adhesive interface 42that is attached to the associated auscultatory sound sensor 12, 12 1 ’, 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” with a first adhesive layer 42.1, for example, either a first surface 40.1’ of the hydrogel layer 40’ or a first layer of double-sided tape 42.1’ on a first side of the acoustic/adhesive interface 40, 42, and that is attached to the skin 38 of the thorax 20 of the test-subject 22 with a second adhesive layer 42.2, for example, either a second surface 40.2’ of the hydrogel layer 40’ or a second
  • the auscultatory sound sensor 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 is fully attached to the acoustic/adhesive interface 40, 42 via the first adhesive layer 42.1, 42.1% 40.1% and the acoustic/adhesive interface 40, 42 is fully attached to the skin 38 of the thorax 20 of the test-subject 22 via the second adhesive layer 42.2, 42.2% 40.2% so that sound signals from within the thorax 20 of the test-subject 22 can propagate otherwise unimpeded to the auscultatory sound sensor 12, 12 1 ’, 12 , 12 3 ’ , 12' " , 12 2 ”, 12 3 ”, thereby providing for a maximum achievable level of the corresponding associated auscultatory sound signals 16, and thereby improving the prospect of detecting an associated abnormal cardiovascular condition - if present - therefrom.
  • the auscultatory sound sensor 12, 12 r , 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 is partially attached to the skin 38 of the thorax 20 of the test-subject 22, and thereby partially decoupled therefrom - i.e., in a condition referred to herein as being debonded therefrom— the resulting auscultatory sound sensor 12, 12 1 ’, 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” would be only partially responsive to sound signals from within the thorax 20 of the test-subject 22, but not sufficiently responsive to provide for an associated auscultatory sound signal 16 of sufficient amplitude to provide for reliably detecting a prospective associated abnormal cardiovascular condition.
  • FIGS. 5d and 5e respectively illustrate an acoustic/adhesive interface 40, 42 partially detached from skin 38, and an acoustic/adhesive interface 40, 42 partially detached from an auscultatory sound sensor 12, respectively.
  • FIG. 5f illustrates an auscultatory sound sensor 12 attached to a wrinkled acoustic/adhesive interface 40, 42
  • FIG. 5g illustrates an acoustic/adhesive interface 40, 42 attached to wrinkled skin 38.
  • the Data Recording Application (DRA) 14 is provided with a means - described more fully hereinbelow— for detecting if one or more auscultatory sound sensors 12, 12 r , 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” is, or are, either detached or debonded from the skin 38 of the thorax 20 of the test-subject 22, so as to provide for excluding data from auscultatory sound sensors 12, 12 r , 12 r , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” that are either detached or debonded, from the skin 38 of the thorax 20 of the test-subject 22 from being used to diagnose a prospective abnormal cardiovascular condition.
  • the adhesive interface 42 could comprise either a hydrogel layer 40’, for example, P-DERM® Hydrogel; a silicone material, for example, a P-DERM® Silicone Gel Adhesive; an acrylic material, for example, a P-DERM® Acrylic Adhesive; a rubber material; a synthetic rubber material; a hydrocolloid material; or a double-sided tape, for example, with either rubber, acrylic or silicone adhesives.
  • the quality of the auscultatory sounds acquired from a test-subject 34 can be improved if the torso 44 of the test-subject 34 is inclined, for example, in one set of embodiments, at an inclination angle Q of about 30 degrees above horizontal— but generally, as close to upright (i.e.
  • an auscultatory- sound-sensing process 700 provides for first determining a scale factor SF from an initially- acquired block of auscultatory-sound-sensor time-series data S, and initially determining from the scale factor SF whether one or more of the auscultatory sound sensors 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” is detached from the skin 38 of the thorax 20 of the test-subject 22, wherein when multiplied by the scale factor SF, the values of the associated auscultatory-sound-sensor time-series data S are nominally within a range that is a predetermined percentage of the dynamic range of the associated data acquisition system (for example, that provides 16- bit signed digital values).
  • the auscultatory-sound-sensing process 700 provides for acquiring successive blocks of auscultatory-sound-sensor time-series data S while the test-subject 22 is holding their breath, and determining from each block of auscultatory-sound-sensor time-series data S - using an associated predetermined debond-detection threshold - whether or not one or more auscultatory sound sensors 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” is debonded from the skin 38 of the thorax 20 of the test-subject 22, or whether there is excessive noise in the auscultatory-sound-sensor time-series data S.
  • the auscultatory-sensor time-series data S is rejected if excessive noise is detected, and the test is aborted if one or more auscultatory sound sensors 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” has become decoupled from the skin 38 of the thorax 20.
  • the first aspect 700 of the auscultatory- sound-sensing process 700 commences with step (702) by initializing a data-block counter i to a value of zero, and then, in step (704), acquiring a block of Ns contiguous samples of auscultatory-sound-sensor time-series data S in accordance with a first aspect 800 of a data acquisition process 800.
  • This initially-acquired data is then used to determine a scale factor SF that is used to determine whether or not one or more auscultatory sound sensors 12, 12 1 ’, 12 r , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” is/are detached from the skin 38 of the thorax 20 of the test-subject 22, and then subsequently to scale subsequent blocks of time-series data S.
  • the initial block of auscultatory-sound-sensor time-series data S may be acquired either with, or without, the test-subject 22 holding their breath, but typically with the test-subject 22 allowed to breath normally— for their comfort and convenience.
  • the number of samples Ns to be acquired is given by the product of an acquisition period Si in seconds, times a sampling frequency Fs in Hz.
  • the data acquisition process 800 commences with step (802) by pre-filtering the electronic signal from the associated auscultatory sound sensor 12, 12 1 ’, 12 r , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” with an analog anti-aliasing filter, for example, an analog second-order band-pass filter having a pass band in the range of 3 Hz to 2.5 KHz, for which the upper-cutoff frequency is sufficiently below the sampling frequency (i.e. no more than half) so as to prevent high frequency components in the signal being sampled from appearing as low frequency components of the sampled signal.
  • an analog anti-aliasing filter for example, an analog second-order band-pass filter having a pass band in the range of 3 Hz to 2.5 KHz, for which the upper-cutoff frequency is sufficiently below the sampling frequency (i.e. no more than half) so as to prevent high frequency components in the signal being sampled from appearing as low frequency components of the sampled signal.
  • step (804) the test-subject 22 need not necessarily hold their breath - as is the case for the initially-acquired block of auscultatory-sound-sensor time-series data S,— then, in step (806), the pre-filtered auscultatory sound signal 16 is sampled at the sampling frequency Fs and converted to digital form by the associated analog-to-digital (ADC) converter.
  • ADC analog-to-digital
  • FIGS. 9 and 10a each illustrate a first block of auscultatory-sound-sensor time- series data S of duration So that was recorded from one of the auscultatory sound sensors 12, 12 1 ’, 12 2 ’, 12 3 , 12 1 ”, 12 2 ”, 12 3 ”
  • the scale factor SF is determined from the initially-acquired block of auscultatory-sound-sensor time-series data S, in accordance with an associated scale-factor-determination process 1100. More particularly, referring to FIGS. 11 and 12a, the scale-factor-determination process 1100 commences with step (1102), wherein the block of auscultatory-sound-sensor time-series data S is divided into a plurality of data segments 46, for example, each of the same data- segment duration S D that nominally spans a single heartbeat, for example, about one second. For example, in FIG.
  • FIG. 12a illustrates a time series
  • step (1104) the median of these maximum values is determined, as given by
  • step (1106) the scale factor SF is determined, as given by:
  • DRo-p is the nominal zero-to-peak dynamic range of the auscultatory-sound-sensor time-series data S after scaling, i.e. after multiplying the acquired values by the scale factor SF.
  • the nominal zero-to-peak dynamic range is set to be about 80 percent - more broadly, but not limiting, 80 percent plus or minus 15 percent - - of the zero-to-peak range of the associated analog-to-digital converter— for example, in one set of embodiments, a 16-bit signed analog-to-digital converter— used to digitize the auscultatory-sound-sensor time-series data S in step (806).
  • the scale factor SF is integer-valued that, for an attached and bonded auscultatory sound sensor 12, 12 1 ’, 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ”, ranges in value between 1 and 28.
  • the associated level of the auscultatory sound signals 16 will be low - for example, at a noise level - resulting in a relatively large associated scale factor SF from step (1106).
  • step (1108) the scale factor SF is in excess of an associated threshold SFMAX
  • the Data Recording Application (DRA) 14 is aborted in step (1110), and the operator 48 is alerted that the one or more auscultatory sound sensors 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” is/are detached, so that this can be remedied.
  • the value of the threshold SFMAX is 28 for the above-described fixed-gain embodiment, i.e.
  • step (1108) if the value of the scale factor SF does not exceed the associated threshold SFMAX, in step (1112), the scale factor SF is returned to step (706) for use in scaling subsequently-recorded breath-held auscultatory sound signals 16.1.
  • ADC analog-to-digital converter
  • step (708) the value of the data- block counter i is incremented, so as to point to the next block of auscultatory-sound- sensor time-series data S to be recorded. If, while this next block is recorded, the auscultatory sound sensors 12, 12 1 ’, 12 r , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” remain attached and bonded to the skin 38 of the thorax 20 of the test-subject 22, and the associated breath-held auscultatory sound signals 16.1 are not corrupted by excessive noise, then this next block of auscultatory-sound-sensor time-series data S will then be subsequently used to detect a prospective abnormal cardiovascular condition therefrom.
  • the auscultatory sound signals 16 used to detect prospective abnormal cardiovascular conditions are recorded while the test- subject 22 holds their breath, the latter to prevent the resulting cardiovascular-based auscultatory sound signals 16 from being overwhelmed by breathing-related sounds that are substantially louder than cardiovascular-based sounds, thereby providing for improving the associated signal-to-noise ratio of the cardiovascular-based auscultatory sound signals 16.
  • a next block of Ns contiguous samples of auscultatory- sound-sensor time-series data S is acquired over an acquisition period Si in accordance with a first aspect 800 of a data acquisition process 800, during which time the test-subject 22 is instructed to hold their breath.
  • the data acquisition process 800 commences with step (802) by pre-filtering the electronic signal from the associated auscultatory sound sensor 12, 12 1 ’, 12 2 ’, 12 3 , 12 1 ”, 12 2 ”, 12 3 ” with the above-described analog anti-aliasing filter. Then, from step (804), because breath-held data is to be acquired, in step (812), the test-subject 22 is instructed by the operator 48 to hold their breath.
  • ADC analog-to-digital
  • step (820) if one or more addition samples remain to be acquired, and if the operator 48 continues to observe that the test-subject 22 is holding their breath, or, additionally or alternatively, if this is confirmed by a below-described breath-hold detection process 1400, then, in step (822) the sample counter j is incremented, and the next sample is acquired in step (818).
  • the pre-filtered auscultatory sound signals 16 are also separately-recorded while waiting for the test-subject 22 to hold their breath, or resume doing so.
  • the auscultatory sound signals 16 typically continue to be recorded between breath-held segments, that latter of which are identified by associated recorded start and stop times with respect to the associated continuous recording.
  • the auscultatory coronary-artery-disease detection system 10 may further incorporate an accelerometer 50 operatively coupled to the thorax 20 of the test-subject 22 to provide an associated acceleration signal responsive to the motion of the thorax 20.
  • an accelerometer 50 operatively coupled to the thorax 20 of the test-subject 22 to provide an associated acceleration signal responsive to the motion of the thorax 20.
  • the associated acceleration signal - operatively coupled to recording module 13 and possibly transmitted to the docking system 27— may be twice integrated either in recording module 13 or the docking system 27 to generate a measure of the peak-to-peak displacement of the thorax 20, which if greater than a threshold would be indicative of breathing by the test-subject 22.
  • an acceleration signal 52 therefrom may alternatively or additionally be processed by an associated breath-hold detection process 1400 to provide for automatically determining - for example, in step (814) of the data acquisition process 800 illustrated in FIG. 8 - whether or not the test-subject 22 is breathing, responsive to the determination, from the acceleration signal 52, of a peak-to-peak displacement of the thorax 20 of the test-subject 22.
  • the respective previous/initial values of thorax displacement Yo and thorax velocity Vo are each initialized to values of zero; a sample counter i is initialized to an initial value, for example, zero; the respective minimum Y MIN and maximum Y MAX values of thorax displacement are each set equal to the (initial) value of thorax displacement Yo, the values of the sample counter I MIN and I MAX at which the corresponding minimum Y MIN and maximum Y MAX values of thorax displacement occur are set equal to the initial value of the sample counter i, and a BreathingFlag is set to indicate that the test-subject 22 is breathing.
  • step (1404) the current sample of thorax acceleration A is acquired.
  • step (1412) the current value of thorax velocity V is calculated by integrating the previous Ao and current A measurements of thorax acceleration, for example, using a trapezoidal rule, as follows:
  • step (1414) the current value of thorax displacement Y is calculated by integrating the above-calculated previous Vo and current V values of thorax velocity, for example, again using a trapezoidal rule, as follows:
  • step (1416) the respective previous values of thorax acceleration Ao, thorax displacement Yo and thorax velocity Vo are respectively set equal to the corresponding current values of thorax acceleration A, thorax velocity V and thorax displacement Y, respectively, that will each be used in subsequent iterations of steps (1412) and (1414).
  • step (1418) if the current value of thorax displacement Y is greater than then current maximum value of thorax displacement TMAX - for example, as would result during a phase of chest expansion by the test-subject 22, - then, in step (1420), the current maximum value of thorax displacement YMAX is set equal to the current value of thorax displacement Y and the corresponding value of the sample counter IMAX associated therewith is set to the current value of the sample counter i.
  • step (1422) if, in step (1422), the amount by which the current value of the sample counter i exceeds the value of the sample counter IMAX associated with the maximum value of thorax displacement YMAX is not equal to a threshold value D (the relevance of which is described more fully hereinbelow), then in step (1424), if the current value of thorax displacement Y is less than then current minimum value of thorax displacement YMIN - for example, as would result during a phase of chest contraction by the test-subject 22,— then, in step (1426), the current minimum value of thorax displacement YMIN is set equal to the current value of thorax displacement Y and the corresponding value of the sample counter IMIN associated therewith is set to the current value of the sample counter i.
  • D the relevance of which is described more fully hereinbelow
  • step (1428) if the amount by which the current maximum value of thorax displacement YMAX is greater the current minimum value of thorax displacement YMIN meets or exceeds a displacement threshold DUMAC, then, in step (1430), the BreathingFlag is set to indicate that the test-subject 22 is breathing, after which, in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404). Similarly, from step (1428), if the displacement threshold D YMAX is not exceeded, in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404).
  • step (1424) for example, as would result from other than a phase of chest contraction by the test-subject 22,— if, in step (1432), the amount by which the current value of the sample counter i exceeds the value of the sample counter IMIN associated with the minimum value of thorax displacement YMIN is not equal to the threshold value D. in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404).
  • step (1432) If, from step (1432), the amount by which the current value of the sample counter i exceeds the value of the sample counter IMIN associated with the minimum value of thorax displacement YMIN is equal to the threshold value D— following a minimum chest contraction of the test-subject 22, in anticipation of subsequent chest expansion, wherein the threshold value D is greater than or equal to one,— then, in step (1434), the peak-to-peak thorax displacement AY is calculated as the difference between the current maximum YMAX and minimum YMIN values of thorax displacement, and, in step (1436), the maximum value of thorax displacement YMAX is set equal to the current value of thorax displacement F, and the value of the sample counter IMAX at which the corresponding maximum value of thorax displacement YMAX occurred is set equal to the current value of the sample counter i, in anticipation of subsequently increasing magnitudes of the current value of thorax displacement F to be tracked in steps (1418) and (1420).
  • step (1422) the amount by which the current value of the sample counter i exceeds the value of the sample counter IMAX associated with the maximum value of thorax displacement YMAX is equal to the threshold value D— following a maximum chest expansion of the test-subject 22, in anticipation of subsequent chest contraction, wherein the threshold value D is greater than or equal to one,— then, in step (1438), the peak-to-peak thorax displacement DU is calculated as the difference between the current maximum YMAX and minimum YMIN values of thorax displacement, and, in step (1440), the minimum value of thorax displacement YMIN is set equal to the current value of thorax displacement F, and the value of the sample counter IMIN at which the corresponding minimum value of thorax displacement YMIN occurred is set equal to the current value of the sample counter i, in anticipation of subsequently decreasing magnitudes of the current value of thorax displacement F to be tracked in steps (1424) and (1426).
  • step (1442) if the amount of the peak-to-peak thorax displacement DU calculated in steps (1434) or (1438), respectively, meets or exceeds the displacement threshold D YMAX. then, in step (1444), the BreathingFlag is set to indicate that the test-subject 22 is breathing. Otherwise, from step (1442), if the amount of the peak- to-peak thorax displacement DU calculated in steps (1434) or (1438), respectively, does not exceed the displacement threshold DU MAC , then, in step (1446), the BreathingFlag is reset to indicate that the test-subject 22 is not breathing. Following either step (1444) or (1446), in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404).
  • a corresponding block of scaled auscultatory-sound-sensor time-series data S is calculated by multiplying the acquired block of auscultatory-sound-sensor time-series data S by the scale factor SF, and in step (714), the scaled auscultatory-sound-sensor time-series data S is analyzed by an associated debond detection process 1500 to determine whether or not any of the auscultatory sound sensors 12, 12 1 ’, 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” was debonded from skin 38 of the thorax 20 of the test-subject 22 during the associated data acquisition process 800.
  • the debond detection process 1500 commences with step (1502) by initializing a sample counter j to a value of one, and initializing a threshold counter TC to a value of zero, wherein the threshold counter TC is a count of the number of contiguous successive samples for which the value of the scaled auscultatory-sound-sensor time-series data S is less than an associated predetermined debond-detection threshold DT.
  • the debond- detection threshold DT is set to a value that is about 20% of the achievable maximum value of the output from the analog-to-digital converter (ADC).
  • step (1504) if the absolute value of the sample of scaled auscultatory-sound-sensor time-series data S, i.e.
  • step (1510) if the sample counter j does not exceed the number of samples Ns in the block of scaled auscultatory-sound-sensor time-series data S, then, in step (1512), the sample counter j is incremented, and the process continues again with step (1504). Otherwise, from step (1504), if the absolute value of the current sample of scaled auscultatory-sound-sensor time-series data S, i.e.
  • step (1510) if the sample counter j exceeds the number of samples Ns in the block of scaled auscultatory-sound-sensor time- series data S or auscultatory-sound-sensor time-series data S— indicating that the entire block of scaled auscultatory-sound-sensor time-series data S or auscultatory-sound- sensor time-series data S has been screened,— then the debond detection process 1500 is terminated with step (1516) by returning an indication to step (714) of the auscultatory- sound-sensing process 700 that the associated auscultatory sound sensor 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” is not debonded from the skin 38 of the thorax 20 of the test-subject 22.
  • step (1518) the debond detection process 1500 is terminated with step (1518) by returning an indication to step (714) of the auscultatory- sound-sensing process 700 that the associated auscultatory sound sensor 12, 12 1 ’, 12 r , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” is debonded from the skin 38 of the thorax 20 of the test-subject 22.
  • the value of N D is equal to 4
  • the value of & is equal to 1 second.
  • step (716) if, from step (714), the debond detection process 1500 detected that the associated auscultatory sound sensor 12, 12 1 ’, 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” was debonded while acquiring the block of auscultatory-sound-sensor time-series data S, then the Data Recording Application (DRA) 14 is aborted in step (718), and the operator 48 is alerted that one or more auscultatory sound sensors 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” are debonded, so that this can be remedied.
  • DRA Data Recording Application
  • step (720) if, from step (714), the debond detection process 1500 did not detect that the associated auscultatory sound sensor 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” was debonded while acquiring the block of auscultatory-sound-sensor time-series data S, then, in step (720), if sufficient noise- screened data has not been gathered - for example, in one set of embodiments, a total duration of at least 65 seconds of recorded data,— then the auscultatory-sound-sensing process 700 continues with step (708).
  • an associated noise detection (i.e. noise-screening) process - operating on either the block of scaled auscultatory-sound-sensor time-series data S, or the block of auscultatory-sensor time-series data S, in parallel with the debond detection process 1500 - provides for detecting if the block of auscultatory-sound-sensor time-series data S has been corrupted by excessive noise, and if so, from step (726), that block of auscultatory- sound-sensor time-series data S is ignored, and the auscultatory-sound-sensing process 700 continues by repeating step (710) to acquire a new block of auscultatory-sound-sensor time-series data S. Otherwise, from step (726), if the block auscultatory-sound-sensor time-series data S has not been corrupted by excessive noise, the process continues with the above-described step (720).
  • noise detection i.e. noise-screening
  • step (720) if sufficient noise-screened data has been gathered for which the associated one or more auscultatory sound sensors 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” were not debonded from the skin 38 of the thorax 20 of the test-subject 22 - for example, in one set of embodiments, a total duration of at least 65 seconds of recorded data,— then, in step (722), at least the composite set of blocks of breath-held auscultatory-sound-sensor time- series data S acquired in step (710) are subsequently analyzed by an associated Data Analysis Application (DAA) 54 operative on the docking system 27 - as illustrated in FIGS.
  • DAA Data Analysis Application
  • DAA Data Analysis Application
  • all data may be recorded and provided to the Data Analysis Application (DAA) 54, along with an associated index that provides for identifying the corresponding associated breath-held portions thereof for which the associated auscultatory sound sensors 12, 12 1 ’, 12 , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” were neither detached nor debonded from the skin 38 of the thorax 20 of the test-subject 22, nor corrupted by noise.
  • DAA Data Analysis Application
  • FIGS. 9 and lOa-lOf illustrate a simulation of six blocks of breath-held auscultatory-sound-sensor time-series data S recorded in accordance with the first aspect 700 of auscultatory-sound-sensing process 700, with respective durations of di. ⁇ %, ⁇ %, d 4 , ds, and db during which time periods the test-subject 22 was holding their breath, separated by periods Di, D2. A3, D4, and As of normal breathing, wherein FIGS.
  • FIGS. 10a- lOe illustrate breath-held auscultatory sound signals 16.1, 16.1’ from a normally -bonded auscultatory sound sensor 12, 12 1 ’, 12 r , 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ” as illustrated in FIG. 5a
  • FIG. lOf illustrates a breath-held auscultatory sound signal 16.1, 16.1” from a debonded auscultatory sound sensor 12, 12 1 ’, 12 2 ’, 12 3 ’ , 12 1 ”, 12 2 ”, 12 3 ”, for example, as illustrated in any of FIGS.
  • FIGS. 10b- lOe are identical to FIG. 10a. However, it should be understood that typically the amplitude of the auscultatory sound signals 16, 16.1 varies from heartbeat to heartbeat, and from one breath-held segment to another.
  • one or more of the auscultatory-sound-sensing process 700, the data acquisition process 800, the scale-factor-determination process 1300, or the de-bond detection process 1500 could be implemented with corresponding alternative processes disclosed in U.S. Application Serial No. 16/136,015 filed on 19 September 2018 - with particular reference to FIGS. 16 through 22 - which is incorporated by reference herein in its entirety.
  • index pointer arrays io[] and ii[] are used to identify locations of associated events, for example, index pointer arrays io[] and ii[] to store the sample locations at the beginning and end of breath-held data segments of the corresponding sampled auscultatory sound data S m [] from the m th auscultatory sound sensor 12, and later-used index pointer arrays isi[] and is 2 [] to store the sample locations of the Sl sound at the beginning of each heart cycle, and the S2 sound at the beginning of diastole respectively, wherein in FIG.
  • a status array Status [m, k] indicates the measurement status of the k th breath-held data segment of the m th auscultatory sound signal 16, i.e. the sampled auscultatory sound data S m () from the m th auscultatory sound sensor 12. Accordingly, step (728) that provides for ignoring data may be implemented by setting the corresponding value of the status array Status(m, k) to a value of IGNORE.
  • a noise detection process 1700 called from step (724) provides for determining whether or not a particular segment of breath-held auscultatory sound signal 16.1 is corrupted by excessive noise, and if so, provides for flagging that segment as being excessively noisy so as to be prospectively excluded from subsequent processing.
  • the noise detection process 1700 generates, in step (1714), frequency-domain noise filters FH[] responsive to cross-correlations of frequency spectra of pairs of adjacent auscultatory sound sensors 12. Accordingly, sensor-index pointers ml[p] and m2[p] provide for identifying the associated auscultatory sound sensors 12, ml[p], m21 p
  • the noise detection process 1700 For each pair of adjacent auscultatory sound sensors 12 in a set of adjacent pairs— selected by sensor- index pointers ml[p] and m2[p] in steps (1706) and (1710), respectively, wherein p is a pair-pointer initialized to 1 in step (1704) - the noise detection process 1700 generates, in step (1714), a frequency-domain noise filter FH[] by cross-correlating in step (1802) the frequency spectra FS A [] and FS B [] (generated in steps (1708) and (1712) of each associated breath-held auscultatory sound signal 16.1: S A [] and S B [], wherein the values of the frequency-domain noise filter FH[] are generated by normalizing the frequency spectra of the cross-correlation function to a range of 0 to 1 in step (1804), then subtracting these values from unity, and then setting resulting values that are less than a noise floor to the value of the noise floor in step (1806).
  • the frequency-domain noise filter FH[] provides for attenuating the components of the breath-held auscultatory sound signal 16.1: S A [] and S B [] that are correlated with one another.
  • STFT short-time Fourier transform
  • NFFT 12 after which the associated average spectral power array FX[] is calculated in dB in step (1920). Then, , the average powers PLOW, PMID, PHIGH in three respective frequency ranges 20 Hz to 200 Hz, 200 Hz to 800 Hz, and 800 Hz to 1,500 Hz, respectively, is calculated in steps (1922), (1924) and (1926), respectively, wherein each average power PLOW, PMID, PHIGH is compared with a corresponding threshold— for example, in one set of embodiments, -20 dB, -50 dB and -30 dB, respectively - in step (1928).
  • the corresponding breath-held auscultatory sound signal 16.1: S A [] or S B [] is then flagged in steps (1930) as being noisy if any of the associated noise power thresholds are exceeded.
  • S A [] or S B [] is then flagged in steps (1930) as being noisy if any of the associated noise power thresholds are exceeded.
  • one auscultatory sound sensor 12 exceeds the noise threshold in a given kth breath-held segment of breath- held sampled auscultatory sound data S m [ io [k]: ii [k]], that auscultatory sound sensor 12, m is ignored for that kth breath-held segment.
  • a particular auscultatory sound sensor 12 m exceeds the noise threshold for more than one breath-held segment of breath- held sampled auscultatory sound data S m [ io [k]: ii [k]], then that auscultatory sound sensor 12, m is flagged as being bad.
  • the process of steps (1706) through (1722) repeats for each of NPAIRS pairs of adjacent auscultatory sound sensors. For example, referring to FIG. 3, in one set of embodiments, there are three pairs of adjacent auscultatory sound sensors, i.e.
  • the process of steps (1704) through (1726) is repeated until all the breath-held segments k of data have been processed.
  • the noise detection process 1700 commences with step (1702) by initializing a breath-held-segment pointer A to a value of 1, so as to provide for pointing to the first breath-held segment of data.
  • the breath-held- segment pointer k provides for pointing to the kth breath-held segment of data, of duration 5 k , extending between sample locations io[k] and ii [k], as illustrated in FIG. 16.
  • the pair pointer p is initialized to a value of 1, and a noise counter NNOISY is initialed to a value of 0.
  • step (1714) the frequency-domain noise filter FH[] generated by a matched-noise-filter generation process 1800 that — referring also to FIG. 18 — commences in step (1802) with the cross correlation of the frequency spectra FS A [], FS B [] of the sampled auscultatory sound data S A [], S B [] of the first ml[p] and second ui2[p] auscultatory sound sensors 12 of the pair p, wherein the resulting cross-correlation is stored in array FH[] and then normalized to a range of 0 to 1 in step (1804), and then inverted in step (1806), wherein each element of the normalized array FH[] is subtracted from 1, and if the result is less than a noise floor, is set to the value of the noise floor NoiseFloor.
  • step (1808) the resulting frequency-domain noise filter FH[] is returned and subsequently used in steps (1716) and (1718) of the noise detection process 1700 to evaluate the noise in the frequency spectra FS A [], FS B [] of the sampled auscultatory sound data S A [], S B [] of the first ml[p] and second m2[p] auscultatory sound sensors 12 of the pair p, respectively, in accordance with an associated noise-content-evaluation process 1900, the latter of which is called from step (1716) to evaluate the noise content of the frequency spectrum FS A [] of the sampled auscultatory sound data S A [] of the first auscultatory sound sensor 12, ml[p] of the pair p, and which is called from step (1718) to evaluate the noise content of the frequency spectrum FS A [] of the sampled auscultatory sound data S A [] of the second auscultatory sound sensor 12, m2[p] of the pair p.
  • the noise-content-evaluation process 1900 commences with step (1902) with receipt of the index m of the associated auscultatory sound sensor 12, m, the associated frequency spectrum FS[] of the associated auscultatory sound sensor 12, m, and the associated frequency-domain noise filter FH[] Then, in step (1904), the time domain noise signal SN[] - containing a total of NPTS data points, i.e.
  • the number of data points in the breath-held sampled auscultatory sound data S m [ io [k]: ii [k]]— is given by the inverse Fourier Transform of the product of the associated frequency spectrum FS[] with the associated frequency-domain noise filter FH[], corresponding to a time-domain cross-correlation of the corresponding associated time-domain signals.
  • each of the NFFT summation data points of a frequency-domain summation array FXSUM[] is initialized to zero, as is an associate summation counter NSUM, wherein the number of summation data points NFFT is a power of 2, for example, 1024, and substantially less than the total number of data points NPTS in the time domain noise signal SN[]
  • an index j MiN — to the first sample of an N FFT -point window of samples to be analyzed from the time domain noise signal SN[] - is initialized to a value of 1.
  • step (1910) an index jMAx — to the last sample of the N FFT -point window of samples to be analyzed from the time domain noise signal SN[] - is set to the value of jnii N + NFFT - 1. Then, in step (1912), if the end of the time domain noise signal SN[] has not been reached, then, in step (1914), the square of values - i.e.
  • step (1916) the summation counter NSUM is incremented, and in step (1918), the index j MiN is incremented by half the width of the N FFT -point window, i.e. by a value of NFFT 12, so as the provide for the next N FFT -point window to be analyzed to overlap the current N FFT -point window by half the window width.
  • the above noise-content-evaluation process 1900 repeats beginning with step (1910), until, in step (1912), the index jMAx exceeds the end of the time domain noise signal SN[], after which, in step (1920), each of the values of the frequency-domain summation array FXSUM[] is divided by the value of the summation counter NSUM SO as to calculate an associated average noise power FX[] Then, in steps (1922), (1924) and (1926), respectively, the noise power is summed in three different corresponding respective frequency ranges, for example, 20 Hz to 200 Hz, 200 Hz to 800 Hz, and 800 Hz to 1,500 Hz, respectively, to give three corresponding respective power levels, PLOW, PMID and PHIGH, respectively.
  • step (1928) if any of the power levels, PLOW, PMID or PHIGH exceeds a corresponding respective power threshold value ThresholdLow, ThresholdMiD or Threshold HiGH , then, in step (1930), the noise threshold is considered to have been exceed for the particular auscultatory sound sensor 12, m and the particular associated segment k of breath-held sampled auscultatory sound data S m [ io [k]: ii [k]] with indication of an associated status in a NoiseStatus flag.
  • step (1932) the noise threshold is considered to have not been exceed for the particular auscultatory sound sensor 12, m and the particular associated segment of breath-held sampled auscultatory sound data S m [ io [k]: ii [k]], with indication of an associated status in the NoiseStatus flag.
  • the results from either steps (1930) or (1932) are then logged by an associated results-logging process 2000 which is called from step (1934). Referring to FIG.
  • the results-logging process 2000 commences with receipt in step (2002) of the index m of the associated auscultatory sound sensor 12, m and the associated NoiseStatus flag. If, in step (2004), the NoiseStatus flag indicates a noisy auscultatory sound sensor 12, m, then, in step (2006), the noise counter NNOISY is incremented, and, in step (2008), the corresponding element of the status array Status [m, k] for the associated auscultatory sound sensor 12, m and breath-held segment k is updated to activate an associate noisy flag, so as to indicate the associated auscultatory sound sensor 12, m being noisy for the associated breath-held segment k.
  • step (2010) if the value of the noise counter NNOISY is greater than 1, then, step (2012), the corresponding elements of the status array Status [m, k] for each of the auscultatory sound sensors 12, m is updated to activate the Ignore flag, so as to provide for ignoring each of the auscultatory sound sensors 12, m for the associated breath-held segment k.
  • step (2014) if the noisy flag of the status array Status[m, k] is activated for at least NFAIL breath-held segments k for the associated auscultatory sound sensor 12, m, then, in step (2016), status array Status [m] for the associated auscultatory sound sensor 12, m is updated to activate the Bad flag for the associated auscultatory sound sensor 12, m, so as to indicate that the associated auscultatory sound sensor 12, m is bad. Then, or otherwise from either step (2004) or step (2014), results-logging process 2000 terminates by returning to the noise detection process 1700.
  • step (1720) if all of the pairs of adjacent auscultatory sound sensors 12 have not been processed, then, in step (1722), the pair pointer p is incremented, and the noise detection process 1700 is repeated for the next pair of auscultatory sound sensors 12, ml[p], m2[p], beginning with step (1706) for the same breath-held segment k.
  • step (1724) If in step (1724), additional segments of breath-held sampled auscultatory sound data S m [ io [k]: ii [k]] remain to be processed, then, in step (1726), the breath-held-segment pointer k is incremented, and the noise detection process 1700 is repeated beginning with step (1704) for the next segment of breath-held sampled auscultatory sound data S m [ io [k]: ii [k]]. Otherwise, from step (1724), the noise detection process 1700 terminates with step (1728).
  • the results from the docking system 27 may be transferred to a server computer system 56, for example, for subsequent transfer to an external data storage or processing system 58, for example, to provide for either viewing or analyzing the results at a remote location.
  • a server computer system 56 for example, for subsequent transfer to an external data storage or processing system 58, for example, to provide for either viewing or analyzing the results at a remote location.
  • the composite set of blocks of breath-held auscultatory-sound-sensor time-series data S are screened prior to analysis by the associated Data Analysis Application (DAA) 54, for example, by first segmenting the set of blocks of breath-held auscultatory-sound-sensor time-series data S by heart cycle with an associated segmentation process 60, and then validating the associated heart cycle data with an associated heart cycle validation process 62, for example, to provide for additional screening for heart-phase associated noise.
  • DAA Data Analysis Application
  • an auscultatory sound signal preprocessing and screening process 2100 provides for preprocessing breath-held auscultatory sound signals 16.1 and an associated electrographic signal 37 from an ECG sensor 34, 34’, for example, as acquired, for example, in accordance with the above-described auscultatory-sound-sensing process 700, with concurrent recording of an associated electrographic signal 37 from an ECG sensor 34, 34’.
  • a good beat counter GB is initialized to zero in step (2102).
  • a segment of breath-held auscultatory sound signals 16.1 is acquired and preprocessed by an associated auscultatory sound signal acquisition and filtering process 2200, wherein, in step (2202), for each auscultatory sound sensor 12, in step (2104), a block of data of associated breath-held auscultatory sound signals 16.1 is acquired, for example, in accordance with the auscultatory-sound-sensing process 700, or as an associated segment k of a previously acquired and recorded breath-held sampled auscultatory sound data S m [ io [k]: ii [k] for the m th auscultatory sound sensor 12.
  • the sampling rate may be reduced to 2 kHz.
  • step (2206) the breath-held auscultatory sound signal 16.1 is filtered with a fourth order Type II Chebyshev filter low-pass filter with cut-off 500 Hz to avoid aliasing, and then, in step (2208), decimated to a 2 kHz sampling rate using a phase invariant method that also involves low-pass filtering, so as to generate corresponding filtered-decimated breath-held sampled auscultatory sound signal 64.
  • the breath-held auscultatory sound signal 16.1 in some cases can include very low frequency - for example, around 10 Hz— vibrations that are believed to be associated with movements of the entire sensor stimulated by chest motion.
  • vibrations can be amplified by resonance characteristics of the tissue-sensor interface.
  • vibrations may decay very slowly extending well into diastolic interval of heart beat, contaminating the signal of interest with large amplitude unwanted interference. The net effect of such interference is an unstable signal baseline and distortion of the actual underlying heart sounds.
  • noise relevant to digitized acquisition of acoustic signals include: electric circuit thermal noise, quantisation noise of AD converter, electro-magnetic interference, power line 60 Hz interference and acoustic noises relevant to human physiology and the environment where recording is done (ambient noise).
  • thermal noise power and ADC quantization noise are very low for the bandwidth of interest and may be important only for the signals with amplitude in microvolt region.
  • recording artifacts may significantly reduce visibility of the signal of interest since they may have high amplitude and overlap in spectral content. These artifacts are due to uncontrolled patient movements, signals related to respiration and sensor vibrations produced by oscillating mass attached to elastic tissue surface (cardio seismographic waves).
  • the latter type of artifact may be caused by the inertial mass of sensor casing and can be high in amplitude due to resonance properties of the sensor- tissue interface. Although, frequency of such vibrations is relatively low (around 10 Hz), their high amplitude results in unstable signal baseline which complicates detection of target signals.
  • cardiac activity may produce low frequency signals as well - for example, as a result of contraction of heart muscle, as a result of valvular sounds—that may have valuable diagnostic information.
  • the spectrum of the artifacts may overlap with acoustic spectrum of cardiac signals such as myocardium vibrations. Therefore, it can be beneficial to reduce baseline instability so as to provide for recording only acoustic signals originating from cardiac cycle.
  • these very low frequency artifacts may be rejected by using additional signal filtering to suppress the associated characteristic vibration frequencies, for example, using a software-implement high-pass filter 66 having a 3 dB cut off frequency above 10 Hz, for example, as provided for by a Savitzky-Golay-based high- pass filter 66’, wherein, in step (2212), the flltered-decimated breath-held sampled auscultatory sound signal 66 is smoothed by a Savitzky-Golay (SG) smoothing filter 68 to generated a smoothed breath-held sampled auscultatory sound signal 70, the latter of which, in step (2212), is subtracted from the flltered-decimated breath-held sampled auscultatory sound signal 64 to then generate the corresponding resulting high-pass- filtered breath-held sampled auscultatory sound signal 72 with good signal-to-noise ration (SNR) without significant distortions of original flltered-decimated breath-held sampled
  • the digital Savitzky-Golay smoothing filter 68 is useful in stabilizing baseline wandering (for example, as may be exhibited in ECG signals) and provides for removing the low-frequency signal components without causing significant ringing artifacts.
  • the Savitzky- Golay smoothing filter 68 employs a least squares approximation of a windowed signal using a polynomial function.
  • the associated parameters of the Savitzky-Golay smoothing filter 68 include the window size M in samples that defines the associated cut-off frequency, and the polynomial degree N used for the approximation.
  • the associated roll-off range is fairly wide and the cut-off frequency is somewhat arbitrary.
  • the Savitzky-Golay smoothing filter 68 is defined by a least-squares fit of windowed original samples with an N th degree polynomial,
  • the frequency response of the Savitzky-Golay smoothing filter 68 depends strongly on the window size M and polynomial order N.
  • step (2214) the auscultatory sound signal acquisition and filtering process 2200 is repeated beginning with step (2202) until each of the auscultatory sound sensors 12, after which, from step (2216), the resulting blocks of high-pass-filtered breath-held sampled auscultatory sound signals 72— for each of the auscultatory sound sensors 12— are returned to step (2104). Then, also from step (2104), referring to FIG.
  • a segment of breath-held auscultatory sound signals 16.1 is acquired and preprocessed by an associated electrographic signal acquisition and filtering process 2200’, wherein, in step (2204’), a block of electrographic data 74 of the corresponding electrograhic signal 37 from the ECG sensor 34, 34’ - in correspondence with the blocks of high-pass-filtered breath-held sampled auscultatory sound signals 72 returned by the above-described auscultatory sound signal acquisition and filtering process 2200 - is acquired, for example, in accordance with the above-described data acquisition process 800, or as an associated segment k of previously acquired and recorded electrographic data 74.
  • step (2206') the electrographic data 74 is filtered with a fourth order Type II Chebyshev filter low-pass filter with cut-off 40 Hz, and then, in step (2208’) , and decimated by a factor of ten, so as to generate corresponding filtered- decimated electrographic signal 76, which, in step (2216'), is returned to step (2104).
  • step (2106) the high-pass-filtered breath-held sampled auscultatory sound signals 72, or alternatively, the breath-held auscultatory sound signals 16.1, are segmented in step (2106) to identify the starting and ending points of each heart cycle, and to identify the starting and ending points of each associated diastole region or phase, thereof.
  • this may be accomplished using the breath-held auscultatory sound signals 16.1— or the corresponding associated high- pass-filtered breath-held sampled auscultatory sound signals 72 - alone, without relying upon the associated electrograhic signal 37 from the ECG sensor 34, 34’, or upon the corresponding associated filtered-decimated electrographic signal 76, for example, in accordance with the following portions of the disclosure and drawings of U.S. Patent No. 9,364,184: Abstract, FIGS. 1-50, Col. 1, Line 1 through Col. 3, line 59 (indicated), Col. 5, line 1 through Col. 34, line 55 (indicated), and the Claims, which are incorporated herein by reference.
  • the electrograhic signal 37 from the ECG sensor 34, 34’, and particularly, the corresponding associated filtered-decimated electrographic signal 76 responsive thereto provide an effective basis for segmenting the breath-held auscultatory sound signals 16.1, 72 by heart cycle, after which the high-pass- filtered breath-held sampled auscultatory sound signal 72 may be used to locate the associated dominant SI and S2 heart sounds that provide for located the associated heart phases of systole and diastole, data from the latter of which provides for detecting coronary artery disease responsive to information in the breath-held auscultatory sound signals 16.1, 72.
  • a heart-phase identification process 2300 is called from step (2106) of the auscultatory sound signal preprocessing and screening process 2100 to locate the heart cycles responsive to the flltered-decimated electrographic signal 76, to then segment the high-pass-filtered breath-held sampled auscultatory sound signal 72 by heart cycle responsive thereto, and to then identify the region of diastole within each heart cycle (an implicitly, to therefor also identify the remaining region systole) from an analysis of the high-pass-filtered breath-held sampled auscultatory sound signal 72 alone.
  • Normal human cardiac cycle consist of four major intervals associated with different phases of heart dynamics that generate associated audible sounds: 1) the first sound (Sl) is produced by closing of mitral and tricuspid valves at the beginning of heart contraction, 2) the systolic interval when heart contracts and pushes blood from ventricle to the rest of the body, 3) the second sound (S2) originated from closing of aortic and pulmonary valves and 4) the diastolic interval is when the heart is relaxed and ventricles are filled with oxygenated blood.
  • each QRS complex 78 illustrated in FIG. 25 is generated by an associated ventricular depolarization that precedes cardiac contraction.
  • the SI heart sound resulting from the closing of mitral and tricuspid valves is observed immediately after R-peak
  • S2 heart sound resulting from the closing of aortic and pulmonary valves is observed as the end of T-wave of ECG cycle.
  • This relative timing of the heart sounds SI, S2 and the QRS complex 78 of the associated electrographic signal 37, 76 provides for using the electrograhic signal 37 to locate the beginning and end of each heart cycle without relying upon the breath-held auscultatory sound signal 16.1, 72 to do so.
  • the SI heart sound— marking the start of systole - follows shortly after the R-peak
  • the S2 heart sound - marking the end of systole and the beginning of diastole - follows thereafter, with diastole continuing until the next R-peak.
  • QRS complex 78 is the most prominent feature, the electrographic signal 37, 76 may be distorted by low frequency baseline wandering, motion artefacts and power line interference.
  • a Savitzky-Golay-based high-pass filter 66’ similar to that used in steps (2210/2212) when filtering the breath-held auscultatory sound signal 16.1— may used to cancel low-frequency drift prior to the subsequent low-pass filtering with the 40 Hz cut-off frequency in step (2206’) and decimation of the sampling by factor of 10 in step (2208’) to provide for reducing high frequency noise and provide for emphasizing QRS complexes 78.
  • the heart-phase identification process 2300 commences in step (2302) by calling an electrographic segmentation process 2600 that provides for identifying the locations of the R-peaks. More particularly, in step (2602), a block of electrographic data x[] is received, and in step (2604) normalized to have a maximum absolute value of unity. Then, in step (2606), beginning with step (2702) of an associated first envelope generation process 2700 with reference to FIG.
  • the block of flltered-normalized electrographic data x[] is used to generate an associated electrographic envelope waveform 80, Fs[] that emphasizes the R-peaks in the flltered-normalized electrographic data x[], wherein, for each value of index k— set in step (2702)— the corresponding value of the electrographic envelope waveform 80, Fs[k] is set in step (2704) responsive to a sum of values with in a sliding window containing a subset of Nw points, as follows:
  • Equation (9) is similar to a Shannon energy function but discrete signal values are raised to the fourth power to emphasize difference between R-peaks 80’ and baseline noise.
  • the value of the electrographic envelope waveform 80, Fs[] is calculated for each of the NPTS values of index k until, in step (2706), all points have been calculated, after which, in step (2708), the electrographic envelope waveform 80, Fs[] is returned to step (2606) of the electrographic segmentation process 2600.
  • the peaks of the electrographic envelope waveform 80, Fs[] are located, for example, by finding indices k for which the associated value of the electrographic envelope waveform 80, Fs[k] is a local maximum exceeding a fixed, predetermined threshold.
  • FIG. 28 illustrates the electrographic envelope waveform 80, Fs[] and associated R-peaks 80’ thereof, for a portion of a 10- second recording of a filtered-decimated electrographic signal 76.
  • the electrographic segmentation process 2600 for finding R-peaks is quite simple and stable when signal-to-noise ratio (SNR) of recording is sufficiently high. Otherwise, when the electrographic data 74 is excessively noisy, additional signal processing such as discrete wavelet decomposition may be used to further enhance R-peaks and facilitate QRS detection. Occasional high amplitude transients due to patient movements or ambient interference may produce false peaks unrelated to QRS complex and complicate segmentation. Referring to FIG. 29, in step (2610), the R-peaks 80’ of the electrographic envelope waveform 80, Fs[] are then validated by a peak validation process 2900, both with respect to temporal location - i.e. relative spacing— and magnitude - i.e.
  • SNR signal-to-noise ratio
  • step (2902) for each of the detected R-peaks 80’, in step (2904), if the difference in temporal locations of each pair of R-peaks 80’ is less than a time threshold TMIN, or if, in step (2906), the difference between the magnitude P(tpEAK(i)) of each R-peak 80’and the median value of the magnitudes of all R-peaks 80’ is greater than a magnitude threshold VMAX, then, in step (2908), the status of the R-peak 80’ being tested is set to IGNORE, in order to prevent the associated R-peak 80’ from being used for subsequent segmentation of the breath-held auscultatory sound signal 16.1, 72.
  • step (2910) the peak validation process 2900 terminates in step (2912) and returns to step (2612) of the electrographic segmentation process 2600, which in turn returns the locations tpEAK[] to step (2302) of the heart-phase identification process 2300, wherein valid R-peaks 80’ satisfy both of the following conditions:
  • FIG. 30 illustrates a correspondence between the breath-held auscultatory sound signal 16.1, 72 and the corresponding electrographic envelope waveform 80, Fs[], wherein the locations of the R-peaks 80’ in the latter are used to segment the former by heart cycle. More particularly referring again to FIG.
  • the associated high-pass- filtered breath-held sampled auscultatory sound signal 72 thereof is segmented based upon the locations of the associated R-peaks 80’ in the corresponding flltered-decimated electrographic signal 76.
  • FIG. 31 illustrates a stack of segments of the high- pass-filtered breath-held sampled auscultatory sound signal 72, each segment corresponding to a different corresponding heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9— each resulting from a single corresponding heart beat— underlying the corresponding continuous high-pass-filtered breath-held sampled auscultatory sound signal 72 illustrated in FIG. 30.
  • first SI and second S2 heart sounds produced by the respective closing of atrioventricular and semilunar valves is somewhat ambiguous, and can be particularly difficult to locate in situations when the associated breath-held auscultatory sound signals 16.1 are recorded in an environment with relatively high level ambient noise, or if there are associated heart murmurs resulting from turbulent blood flow.
  • first SI and second S2 heart sounds of the cardiac cycle remain the most prominent acoustic features.
  • a Shannon energy envelope is generated from the corresponding associated portion of the high-pass-filtered breath-held sampled auscultatory sound signal 72 in accordance with a corresponding second envelope generation process 3200, which is similar to the above-described first envelope generation process 2700 except for the associated envelope function. More particularly, referring to FIG.
  • step (3202) of an associated second envelope generation process 3200 for the k th of NSAMPLES samples of a block of high-pass-filtered breath-held sampled auscultatory sound data 72, s[] of the corresponding associated heart cycles 82, a corresponding value of an associated acoustic envelope waveform 84, Es[k] is generated in step (3204), responsive to a sum of values within a short-time sliding window - with different windows overlapping one another - containing a subset of Nw points, as follows in accordance with a canonical Shannon energy function Es[k] that provides a measure of the energy of the underlying high-pass- filtered breath-held sampled auscultatory sound signal 72, s[] :
  • the value of the acoustic envelope waveform 84, Es[k] is calculated for each of the NSAMPLES values of index k until, in step (3206), all points have been calculated, after which, in step (3208), the acoustic envelope waveform 84, Es[k] is returned to step (2310) of the heart-phase identification process 2300.
  • the associated signal x[], s[], from which the corresponding envelope Fs[], Es[] is generated is zero-padded at the beginning and at the end with (Nw - l)/2 zeros, assuming Nw is an odd integer, wherein the first data point in the padded signal x[], s[] would then begin at index l+(Nw - l)/2.
  • FIG. 33 illustrates an example of an acoustic envelope waveform 84, Es[] in correspondence with a rectified version 86 of the associated high-pass-filtered breath-held sampled auscultatory sound data 72, s[] from which the acoustic envelope waveform 84, Es[k] was generated (i.e. containing an absolute value thereof), wherein the respective envelope peaks 88 S1 , 88 s2 associated with the corresponding SI and S2 heart sounds can be readily identified in the acoustic envelope waveform 84, Es[].
  • the locations of the envelope peaks 88 S1 , 88 s2 associated with the corresponding SI and S2 heart sounds are identified using an adaptive threshold method that iteratively adjusts an associated threshold value down from a maximum value until the two most prominent envelope peaks 88 S1 , 88 s2 emerge within the associated heart cycle 82, above the a particular threshold limit.
  • the final position of envelope peaks 88 S1 , 88 s2 associated with the corresponding SI and S2 heart sounds is found as the apex of a local quadratic model 90.1, 90.2 of the acoustic envelope waveform 84, Es[] .
  • Adaptive thresholding provides for accommodating substantial variation in the relative magnitude of the envelope peaks 88 S1 , 88 s2 that occur either from one heart beat to another, from one patient to another, or from one recording site to another.
  • step (2316) the locations of the envelope peaks 88 S1 , 88 s2 associated with the corresponding SI and S2 heart sounds are validated using a normalized acoustic envelope waveform 84, Es[], i.e. normalized to a range of 0 and 1, and the associated local quadratic models 90.1, 90.2 thereof, in accordance with a minimum-time-spacing criteria used to remove or ignore spurious transient peaks unrelated to heart sounds, similar to equation 10 above that is associated with step (2904) of the above-described peak validation process 2900 used to validate the electrographic envelope waveform 80, Fs[] .
  • the acoustic envelope waveform 84, Es[] is searched about the associated indices ksi PEAK, ks2j>EAK respectively associated with the corresponding respective envelope peaks 88 S1 , 88 s2 to find adjacent data points therein, i.e. associated indices ksi-, ksi+, ks2-, ks2+ for which the corresponding values of the acoustic envelope waveform 84, Es(ksi-), Es(ksi+), Es(ks2-), Es(ks2+) are each about 5 percent down, i.e. 95 percent of, the corresponding values Es(ksi_PEAK), Es(ks2_PEAK) of the associated envelope peaks 88 S1 , 88 s2
  • step (2320) a respective local quadratic models ESl(k), 90.1’ and ES2(k), 90.2’ is fitted - for example, by least-squares approximation— to the three points associated with each of the corresponding respective envelope peaks 88 S1 , 88 s2 as follows:
  • ES l(k) Quadratic Fit ( ⁇ ksi-. Es(ksi-) ⁇ , ⁇ ksi_PEAK, Es(ksi) ⁇ , ⁇ ksi+, Es(ksi+) ⁇ ) (l2a)
  • ES2(k) Quadratic Fit ( ⁇ ks2-, Es(ks2-) ⁇ , ⁇ ks2_PEAK, Es(ks2) ⁇ , ⁇ ks2+, Es(ks2+) ⁇ ) (l2b)
  • step (2324) If, in step (2324), all heart cycles 82 in the high-pass-filtered breath-held sampled auscultatory sound signal 72 have not been processed, then the heart-phase identification process 2300 is repeated beginning with step (2308). Otherwise, from step (2326), if all auscultatory sound sensors 12 have not been processed, then the heart-phase identification process 2300 is repeated beginning with step (2304).
  • the heart-phase identification process 2300 returns either the mean values ksi, ks2 of the corresponding root locations ⁇ ksi START , ksi E ND ⁇ , ⁇ ks2_ START , ks2_E ND ⁇ associated with the corresponding SI and S2 heart sounds, or the corresponding mean values tsi, ts2 of the associated times, i.e.
  • segmented high-pass-filtered breath-held sampled auscultatory sound data 72, s[] from each of the heart cycles 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9 may be synchronized with respect to the mean time ts2 associated with the S2 heart sound of each heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9, or alternatively, may be synchronized with respect to the mean time tsi associated with the SI heart sound of each heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9, wherein the region of diastole extends from the time associated with the second root ks2_E ND , i.e. t(ks2_E ND ), associated with the S2 heart sound and the end of the associated heart cycle 82, 82.1, 82.2, 82.3
  • a beat outlier detection process 3600 which, referring to FIG. 36, in steps (3606) through (3618) provides for repetitively calculating, in step (3614), a standard deviation D[] of the sample values in a plurality of windows of NWINDOW samples, each window shifted by one sample with respect to the next, resulting in a standard deviation array D[] containing KSTDDEV standard deviation values.
  • NWINDOW is equal to 128.
  • the standard deviation array D[] is rank ordered, and the median value thereof, medianStd, is used in step (3622) to calculate a standard deviation compactness metric STDDEVCM, as follows:
  • step (3624) if the standard deviation compactness metric STDDEVCM exceeds a threshold, for example, in one set of embodiments, equal to 6, but generally between 1 and 10, the particular region of diastole for the particular breath-held segment from the particular auscultatory sound sensor 12 designated as m, is flagged as an outlier.
  • a threshold for example, in one set of embodiments, equal to 6, but generally between 1 and 10.
  • step (2112) of the auscultatory sound signal screening process 2100 if an outlier was detected in step (3624) and flagged in step (3626) of the beat outlier detection process 3600, then in step (2120), if the end of the breath-held segment has not been reached, the auscultatory sound signal preprocessing and screening process 2100 repeats beginning with step (2108) for the next heart cycle 82.
  • the above-described noise detection process 1700 may be called from step (2114) to provide for determining whether or not a particular region of diastole for a particular heart cycle 82 of either the breath-held auscultatory sound signal 16.1, or the corresponding high-pass-filtered breath-held sampled auscultatory sound signal 72, is corrupted by excessive noise, and if so, provides for flagging that region of diastole as being excessively noisy so as to be prospectively excluded from subsequent processing, wherein above references to breath-held auscultatory sound signal 16.1 in the description of the associated noise detection process 1700 should be interpreted as referring to the corresponding region of diastole for a particular heart cycle 82 of either the breath-held auscultatory sound signal 16.1, or the corresponding high-pass-filtered breath-held sampled auscultatory sound signal 72, and references to the breath-held segment k in the description of the associated noise detection process 1700 should be interpreted as referring
  • step (2120) if a noise threshold was exceeded, in step (2116), if the end of the breath-held segment has not been reached, the process repeats beginning with step (2108) for the next heart cycle. Otherwise, from step (2116), the good beat counter GB is incremented in step (2118) before continuing with step (2120) and proceeding therefrom as described hereinabove. Otherwise, from step (2120) if the end of the breath-held segment has been reached, in step (2122), if a threshold number of valid heart cycles has not been recorded, the process repeats with step (2104). Otherwise, the recording process ends with step (2124).
  • Breath holding interval is segmented into individual heart beats and the diastolic interval is analysed with respect to noise level.
  • Quality control of the recorded signals is required in order detect weak signals that may indicate health problems but can be blocked by strong noise or unwanted interference.
  • the present method is developed for quantitative control of signal quality and can be deployed on a recording unit for real-time quality monitoring or can be used at the post recording stage to extract only low-noise heart beats that satisfy specific quality condition.
  • Practical implementation of the beat selection algorithm is aimed at collecting heart beats that have diastolic noise level below the specific threshold is outlined below. Referring to FIG, 37, the beat selection algorithm proceeds through a number of steps.
  • the recording proceeds through sequential acquisition of heart sound intervals with duration between 5 and 15 sec (for example, breath holding intervals).
  • the recorded data chunk is passed through beat segmentation procedure that identifies the beginning and the end of each heart beats using synchronized ECG recording or another signal processing code that identifies timing of the first (Sl) and second (S2) heart sounds.
  • the algorithm input parameters include the noise threshold mean power level and the required number of high quality heart beats.
  • a two-dimensional array of heart beats is created.
  • the segmentation code also identifies timing of diastolic interval of each heart beat.
  • heart beats are normalized with respect to absolute value of the S2 peak and the mean noise power is computed within the time window T w .
  • the time-window is sliding along the diastole with the 50% overlap to compute array of localized signal power. Then the maximum power for the diastole is determined and compared against the selected threshold, for instance - 20 dB. Details of signal noise analysis are shown in FIG. 38. If noise level in one of the windows is greater then Po then it may indicate excessive noise or presence of a large amplitude transient outlier. If diastolic signal power exceeds the threshold that it is labeled as nosy beat and is not counted in the overall tally of heart beats. The algorithm exits when number of low-noise heart beats reaches Nmax. If this number beats can not be reached within reasonable time, then the system informs user that recording is noisy and additional actions should be performed to improve signal quality.
  • FIGS. 39a and 39b respectively show the signal stack before and after S2 alignment.
  • the next preprocessing step deals with the variable heart beat rate and as a result variable length of diastolic intervals.
  • the method of signal processing that presents data in uniform way on the same time scale is developed to synchronize diastolic features of the time-variable heart beats.
  • the heart beat rate is always changing and never remains the same during recording time of several minutes. It creates serious problems when specific signal features should be identified in diastole, for example using cross-correlation method.
  • Heart beat segmentation allows one to align heart beat starting moments but due to difference in heart beat rate those features appear shifted and out of sync with respect to each other.
  • FIGS. 40a and 40b respectively show stack of heart beats before and after the time normalization.
  • the final stage of signal pre-processing is needed to extract acoustic features from the recorded heart beats.
  • feature extraction typically involves certain transformation of raw data to low-dimensional or sparse representation that uniquely characterizes the given recording.
  • the raw data can be transformed in a set of images and some image recognition algorithm like convolutional neural net can be employed for automatic feature selection.
  • the current data processing algorithm relies on cross-correlation (CC) of multiple heart beats to identify high-pitch signal in the diastolic phase of heart beat that occurs in multiple beats and can be linked to blood turbulent flow.
  • CC cross-correlation
  • the 2D stack of heart beats is high-pass filtered and then each pair of signals is processed to compute cross-correlation function Rxixj.
  • This computation is accomplished by segmenting each signal using sliding short-time window with Nw samples (typically 128) which is advancing one sample per each iteration of CC computation.
  • Cross-correlation is computed without the time lag, resulting in an array of the same size as the original signals that can be expressed by the following equation:
  • xi and xj are the diastolic sounds of two heart beats and Nb is the total number of heart beats.
  • Nb is the total number of heart beats.
  • the cross-correlation matrix Nb x Nt is obtained and displayed as a 2D image.
  • the similar signal pattern that is present in majority of heart beats will produce a localized cross-correlation peak in the diastole. Since micro-bruit signal is expected to occur approximately at the same fraction of diastole interval, the all cross-correlation peaks produce distinct bands across the image illustrated in FIG. 42.
  • the cross-correlation operation will emphasize signal features that are coherent within the current time window and suppress uncorrelated noise increasing the signal-to- noise ratio.
  • acoustic features in diastolic interval of heart beat can be visualized using continuous wavelet transform (CWT).
  • the wavelet transform processing is similar to the short-time cross-correlation but instead of correlating two similar signals the signal of interest is correlated using a wavelet function with limited support to facilitate temporal selectivity.
  • mother wavelet y is a continuous function in time and frequency domains.
  • the wavelet family is typically chosen empirically using specifics of the signal under consideration.
  • the family of Morlet wavelets appears as an appropriate choice for analysis of heart sounds.
  • Output of the wavelet transform is a two-dimensional time-frequency representation of signal power
  • Example of a wavelet transform is illustrated in FIG. 43, where color map represents distribution of the signal power in time-frequency plane.
  • the wavelet representation can be reduced further by dividing the time axis into discrete intervals and computing overall signal power within such interval and specific frequency bandwidth.
  • the wavelet image may be subdivided in time intervals of 200 ms and two frequency bands 10 Hz - 40 Hz and 40 Hz - 100 Hz.
  • the output vector of the signal power can be use as an input for a neural network classifier.
  • a convolutional neural network is employed to analyze cross-correlation images of acoustic signals.
  • the network input consists of the three-dimensional array of cross-correlation data recorded at the patient left or right positions and combined into a single array representing three individual channels.
  • the CNN is built of three convolutional layers that use a 5x5 kernel array to scan input data to build a structure of acoustic features with increasing complexity.
  • Neuron activation uses ReLU nonlinearity to produce output data.
  • Final stage of the network classifier is a fully connected neural net with an additional clinical information (age, gender, blood pressure, etc.) merged with the acoustic features identified by CNN.
  • the network output is a two-node layer implementing binary classification via softmax function applied to the incoming data.
  • Reported performance metrics include prediction accuracy, sensitivity, specificity, negative prediction value and positive prediction value. New patient data are classified by passing through the same pre-processing stages and feeding the computed cross-correlation image to CNN classifier.
  • the system can present various views of the acoustic data (both unprocessed and processed) that was captured during the test for review by a clinician.
  • the clinician may strengthen his case for a particular diagnosis, either in agreement with or disagreement with the result produced by the system. Additionally, particular visualizations may help reassure the patient that the diagnosis is the correct one.
  • the system presents heart/arteries view comprising a graphical representation of the heart and coronary artery tree, highlighting the data in accordance with a Textual View.
  • the clinician is presented with the option to highlight the blockage in either: a Stacked Heartbeat View; a Bruit Identification View (either mode); a Bruit Analysis View; or a Receiver Operating Characteristic Curve.
  • FIG. 45 illustrates test results showing severity of obstructions and zonal location of each obstruction within coronary artery tree.
  • the tree diagram also indicates which arteries are critical, and which are not, with associated color coding in accordance with the amount of occlusion, for example, with the illustrated region of 50 occlusion highlighted in a reddish hue, and the region of 30% occlusion highlighted in a yellowish hue.
  • the system presents, in a textual manner, the system’s interpretation of the acoustic data, including: whether or not the patient has a clinical level of CAD; the count of obstructions detected, for each obstruction: the location (zone) of the obstruction; the percentage occlusion of the obstruction; or the type of blockage (soft plaque, hard plaque).
  • the system provides for presenting an associated confidence level, which indicates how confident the system is of each specific element of information presented, or, based on the patient’s demographic data (age, sex, BMI, medical and family history), the percentage of other patients who have a similar count, severity, and position of obstructions. From this view, the clinician may switch to any other view listed in this document for more information.
  • the system presents a Receiver Operating Characteristic
  • ROC CAD detection algorithm sensitivity as a function of its false positive rate (l-specificity).
  • Curve (AUC) based on the patient’s demographics, and the system’s clinical trial results.
  • the coordinate that corresponds to the current positivity criterion will be highlighted.
  • the clinician will be able to display a modified graph if he commands the system to exclude specific parts of the patient’s demographic data.
  • the graph may or may not contain gridlines and/or data points defining the ROC curve, and it may or may not fill in the AUC.
  • the data may be visualized as a line graph (with or without underfill), where true positives, true negatives, false negatives, and false positives are displayed, with a line indicating the currently -used cutoff value.
  • the system provides for visualizing the acoustic data resulting from an analysis of the breath-held auscultatory sound signals 16.1 from the auscultatory sound sensors 12.
  • the system presents a heartbeat view comprising a graphical plot of the systolic and diastolic intervals of each heartbeat captured.
  • the horizontal axis of the graph represents captured heartbeats. This representation is a subset of the acoustic capture time, as some acoustic data acquired is discarded (poor quality, etc.) during the system’s analysis process. The duration of the Sl and S2 sounds are also highlighted on this axis.
  • the vertical axis may comprise acoustic data captured from each of the system’s sensors - both acoustic and ECG.
  • a correlation procedure is performed to ensure that the data captured from each of the system’s sensors is aligned to one another.
  • the clinician has the option to highlight a subset of the data on the horizontal axis, and command the system to zoom into the selected section. In this way, the clinician can perform a deeper analysis on one or more sections of the acoustic capture data as he so chooses, and explore any discrepancies between the data captured by the ECG and acoustic sensors for any particular heartbeat.
  • FIG. 47 illustrates an example of the Heartbeat View wherein the acoustic view has been limited by the clinician to Sensor#3 only, and zoomed in to see only 2 heartbeats, with the ECG data also displayed.
  • the system presents a graphical plot of the systolic and diastolic intervals of each heartbeat captured.
  • the horizontal axis of the graph represents time (in seconds or milliseconds) from the beginning of the systolic interval to the end of the diastolic interval.
  • the duration of the Sl and S2 sounds are highlighted on this axis.
  • the vertical axis is comprised of the acoustic data captured for each of the heartbeats, in either ascending or descending order of capture, where each heartbeat is itself a graph, with a vertical axis representing intensity.
  • the system highlights any unexpected acoustic signals captured, as such signals may be an indication of an obstruction or other cardiac condition. For example, in FIG. 48, unexpected acoustic signals are highlighted in red.
  • the clinician has the option to highlight a subset of the data on the horizontal axis, and command the system to zoom into the selected section. In this way, the clinician can perform a deeper analysis on one or more sections of the acoustic capture data as he so chooses, especially so that he may explore more deeply any unexpected acoustic signals.
  • the system presents a graphical plot of unexpected acoustic signals captured during the diastolic interval by means of a line graph with underfill.
  • the horizontal axis of the graph represents time (in seconds or milliseconds) from the beginning of the diastolic interval to the end of the diastolic interval.
  • the duration of the S2 sound is also highlighted on this axis.
  • the vertical axis is comprised of the acoustic data captured for each of the heartbeats, in either ascending or descending order of capture, where each heartbeat is itself a graph, with a vertical axis representing intensity.
  • An underfill is used to visually highlight deviation from the baseline.
  • the clinician has the option to highlight a subset of the data on the horizontal axis, and command the system to zoom into the selected section. In this way, the clinician can perform a deeper analysis on one or more sections of the acoustic capture data as he so chooses, especially so that he may explore more deeply any unexpected acoustic signals.
  • the system presents a graphical plot of unexpected acoustic signals captured during the diastolic interval by means of a spectrogram.
  • the horizontal axis of the graph represents time (in seconds or milliseconds) from the beginning of the diastolic interval to the end of the diastolic interval.
  • the duration of the S2 sound is also highlighted on this axis.
  • the vertical axis is comprised of the acoustic data captured for each of the heartbeats, in either ascending or descending order of capture.
  • the system For each diastole displayed, the system highlights any unexpected acoustic signals captured, as such signals may be an indication of an obstruction or other cardiac condition. Such highlighted areas indicate the intensity of the unexpected signal. Highlights are in the form of color, which indicate varying intensity of the signal.
  • CAD non-obstructive coronary artery disease
  • the clinician has the option to highlight a subset of the data on the horizontal axis, and command the system to zoom into the selected section. In this way, the clinician can perform a deeper analysis on one or more sections of the acoustic capture data as he so chooses, especially so that he may explore more deeply any unexpected acoustic signals.
  • the system presents a graphical time/frequency plot of unexpected acoustic signals captured.
  • the horizontal axis of the graph represents time (in seconds or milliseconds) from the beginning of the systolic interval to the end of the diastolic interval. The duration of the Sl and S2 sounds are highlighted on this axis.
  • the vertical axis represents the frequency (in Hz or kHz) of any unexpected acoustic signals, averaged from all captured data.
  • the color on the graph represents the intensity of that signal.
  • the graph could be represented in monochrome as a contour plot.
  • two fingers moving apart from one another can be used to zoom in to any area on any graph, and two fingers moving together can be used to zoom out.
  • a single finger can be used to highlight any areas on the horizontal axis, and the graph can be zoomed in to that highlighted area by touching a button.
  • Touching any area of the graph provides information to the user (either in a pop-up window or beside/below the graph) on the values of the horizontal and vertical axes at that point, as well as the“height” information of that point if available (e.g. in the Matrix View). For example, in Stacked Heartbeat View, touching on an individual heartbeat would cause the system to provide the heartbeat number, the maximum intensity of that heartbeat (and the time on the horizontal axis at which the maximum intensity occurs), and the time on the horizontal axis corresponding to the touch. In the case of the Matrix View, touching on any area of the graph would cause the system to provide the frequency, time, and intensity corresponding to the coordinate on the graph that was touched.
  • zoom can be accomplished through a common desktop/laptop user interface paradigm, such as dedicated zoom in/out buttons in the UI, or mouse wheel scrolling.
  • FIG. 52 illustrates a comparison of a current test with a previous test, using the Bruit Identification View, Spectrogram mode, to confirm success of PCI.
  • FIG. 532 illustrates a comparison of a current test with a previous test, using the Bruit Identification View, Line Graph with Underfill mode, to confirm success of PCI.
  • the ability to compare the current test with a previous test is critical to the clinicians understanding of the progression of a particular cardiovascular condition (either worsening, or improved as in the case of a PCI procedure). This capability is relevant to the Stacked Heartbeat View, the Bruit Identification View (both modes), and the Bruit Analysis View.
  • the graphs may be rendered locally or remotely (server-based) or both, depending on the capabilities desired by the clinician and the organization to which he belongs. In most use cases (tablet, phone, desktop, or laptop), the graph rendering will be done locally, either through a web-browser (full or embedded) on the client, or through a graphics library optimized for each specific supported client platform.
  • the rendering may be done on the server side - graphs may be generated and exported to JPEG (or other similar) format so that they can be emailed or sent via instant message to interested parties.
  • any reference herein to the term“or” is intended to mean an“inclusive or” or what is also known as a “logical OR”, wherein when used as a logic statement, the expression“A or B” is true if either A or B is true, or if both A and B are true, and when used as a list of elements, the expression“A, B or C” is intended to include all combinations of the elements recited in the expression, for example, any of the elements selected from the group consisting of A, B, C, (A, B), (A, C), (B, C), and (A, B, C); and so on if additional elements are listed.
  • indefinite articles “a” or “an”, and the corresponding associated definite articles“the’ or“said”, are each intended to mean one or more unless otherwise stated, implied, or physically impossible.
  • expressions“at least one of A and B, etc.”,“at least one of A or B, etc.”, “selected from A and B, etc.” and“selected from A or B, etc.” are each intended to mean either any recited element individually or any combination of two or more elements, for example, any of the elements from the group consisting of “A”,“B”, and“A AND B together”, etc..

Abstract

An auscultatory sound signal from at least one auscultatory sound-or-vibration sensor is segmented into a plurality of associated heart cycle segments responsive to associated R-peak locations of an electrographic envelope signal representing an envelope response to an even power of an associated electrographic signal from an ECG sensor. An representing an envelope responsive to an even power of said auscultatory sound signal within said at least one heart cycle is locally modeled about at least a second peak to provide for locating the start of diastole of said at least one heart cycle.

Description

METHOD OF PREPROCESSING AND SCREENING AUSCULTATORY SOUND
SIGNALS
CROSS-REFERENCE TO RELATED APPLICATIONS
The instant application claims benefit of the following: U.S. Provisional Application Serial No. 62/575,390 filed on 21 October 2017, U.S. Provisional Application Serial No. 62/575,397 filed on 21 October 2017, and U.S. Provisional Application Serial No. 62/575,399 filed on 21 October 2017, each of which is incorporated herein by reference in its entirety.
BRIEF DESCRIPTION OF THE DRAWINGS
In the accompanying drawings:
FIG. 1 illustrates a block diagram of a coronary-artery-disease detection system;
FIG. 2 illustrates a first aspect of a data recording module and a first aspect of an associated docking system, in accordance with a first aspect of the coronary-artery-disease detection system illustrated in FIG. 1;
FIG. 3 illustrates a fragmentary view of a human thorax and associated prospective locations of auscultatory sound sensors associated right, sternum and left, second, third, fourth and fifth, inter-costal spaces, left posterior locations at the second and third inter-costal spaces, and locations proximate to the heart apex;
FIG. 4 illustrates a second aspect of a data recording module and a second aspect of an associated docking system, in accordance with a second aspect of the coronary-artery- disease detection system illustrated in FIG. 1;
FIG. 5a illustrates an auscultatory sound sensor coupled to the skin of a test-subject, by bonding via associated adhesive layers or surfaces on both sides of an adhesive interface;
FIGS. 5b and 5c each illustrate an auscultatory sound sensor that is detached, and therefore fully decoupled, from the skin of a test-subject, wherein FIG. 5b illustrates the associated adhesive interface detached from the skin of the test-subject, and FIG. 5c illustrates the associated adhesive interface detached from the auscultatory sound sensor;
FIGS. 5d through 5g each illustrate an auscultatory sound sensor that is partially coupled to, but debonded from, the skin of a test-subject;
FIG. 6 illustrates a test-subject reclined on a surface, with their torso inclined while capturing auscultatory sound signals from a plurality of auscultatory sound sensors attached to the thorax of the test-subject; FIG. 7 illustrates a flowchart of a first aspect of an associated auscultatory-sound sensing process that incorporates a process for detecting a decoupling of the associated auscultatory sound sensors from the skin of the thorax of a test-subject being diagnosed for a prospective abnormal cardiovascular condition, wherein the decoupling-detection process occurs after each block of breath-held auscultatory sound time-series data is acquired, and is based upon scaled time-series data and responsive to an associated pre-determined debond- detection threshold;
FIG. 8 illustrates a flowchart of a first aspect of a process for acquiring auscultatory sound signals from the associated auscultatory sound sensors coupled to the skin of the thorax of the test-subject being diagnosed for a prospective abnormal cardiovascular condition;
FIG. 9 illustrates a plurality of six blocks of breath-held, auscultatory-sound-sensor time-series data recorded from an auscultatory sound sensor coupled to the skin of the thorax of a test-subject being diagnosed for a prospective abnormal cardiovascular condition;
FIGS. 10a- lOf respectively illustrate a simulation of successively recorded blocks of breath-held, sensor time-series data illustrated in FIG. 9, each illustrated with an expanded time scale, wherein FIGS. lOa-lOe illustrates a condition for which the auscultatory sound sensor is coupled to the skin of the test-subject, and FIG. lOf illustrates a condition for which the auscultatory sound sensor is decoupled from the skin of the test-subject;
FIG. 11 illustrates a flowchart of a process for determining a scale factor used to scale auscultatory-sound-sensor time-series data, the latter of which is analyzed to detect whether or not the associated auscultatory sound sensor is decoupled from the skin of the test-subject, wherein the scale factor provides for directly determining if the associated auscultatory sound sensor is detached from the skin of the test-subject;
FIGS. 12a-12f respectively illustrate time-series of the absolute values of the corresponding time-series data illustrated in FIGS. 10a- lOf, further illustrating a division of the block of breath-held, sensor time-series data into a plurality of associated data segments, with each data segment of sufficient width to nominally include sound from a single heartbeat, and with the peak values in each data segment marked, wherein FIGS. 12a-12e illustrates a condition for which the auscultatory sound sensor is coupled to the skin of the test-subject, and FIG. 12f illustrates a condition for which the auscultatory sound sensor is decoupled from the skin of the test-subject;
FIG. 13 illustrates an accelerometer on the thorax of a test-subject during a respiration cycle of the test-subject;
FIG. 14 illustrates a breath-hold detection process; FIG. 15 illustrates a flowchart of a first aspect of a process for detecting whether or not an auscultatory sound sensor is debonded from the skin of a test-subject;
FIG. 16a illustrates an organization of data from an auscultatory sound sensor recorded by an auscultatory coronary-artery-disease detection system from a test subject;
FIG. 16b illustrates a flow charge of a process for detecting noise in breath-held segments of data;
FIG. 17 illustrates a flowchart of a noise detection process;
FIG. 18 illustrates a flowchart of a process for generating a matched noise filter;
FIG. 19 illustrates a flowchart of a process for evaluating the noise content in a spectral signal of an auscultatory sound signal;
FIG. 20 illustrates a flowchart of a process for logging results from the noise- evaluation process of FIG. 19;
FIG. 21 illustrates a block diagram of a process for preprocessing and screening auscultatory sound signals;
FIG. 22a illustrates a process for pre-processing auscultatory sound signals from auscultatory sound-or-vibration sensors;
FIG. 22b illustrates a process for pre-processing electrographic signals from an ECG sensor;
FIG. 23 illustrates a process for segmenting auscultatory sound signals from auscultatory sound-or-vibration sensors, by heart cycle based upon an electrographic signal from an ECG sensor, and by heat phase based upon the auscultatory sound signals;
FIG. 24 illustrates an auscultatory sound signal from an auscultatory sound-or- vibration sensor;
FIG. 25 illustrates a corresponding electrographic signal from an ECG sensor, in correspondence with the auscultatory sound signal illustrated in FIG. 24;
FIG. 26 illustrates a process for identifying heart-cycle boundaries in an electrographic signal from an ECG sensor;
FIG. 27 illustrates a first process for generating an envelope of a signal, which is called from the process illustrated in FIG. 26;
FIG. 28 illustrates an envelope, and associated peaks, of a portion of the electrographic signal illustrated in FIG. 25, generated in accordance with the process illustrated in FIGS. 26 and 27; FIG. 29 illustrates a process for validating the peaks of an electrographic signal, which is called from the process illustrated in FIG. 26;
FIG. 30 illustrates a plot of the auscultatory sound signal illustrated in FIG. 24 together with plot of an envelope of the corresponding electrographic signal illustrated in FIG. 25;
FIG. 31 illustrates a plot of the auscultatory sound signal illustrated in FIG. 24, but over a relatively greater total period of time, with the auscultatory sound signal segmented based upon the peaks of the envelope of the associated electrographic signal, and presented as a beat stack, each illustrating a corresponding associated heart beat;
FIG. 32 illustrates a second process for generating an envelope of a signal, which is called from the process illustrated in FIG. 23;
FIG. 33 illustrates a rectified auscultatory sound signal and an associate envelope thereof in accordance with the process illustrated in FIG. 32;
FIG. 34 illustrates a plot of the auscultatory sound signal that is shown rectified in FIG. 33, together with plots of the associated quadratic models of the associated envelopes in proximity to the associated SI and S2 heart sounds, illustrating time-points associated with zero-amplitude roots of the associated quadratic models that are used to locate associated heart phases;
FIG. 35 illustrates the auscultatory sound signal beat stack illustrated in FIG. 31, together with indicia showing the locations of the roots of the quadratic models associated with the SI and S2 heart sounds that are used to locate associated heart phases, with the auscultatory sound signals of the beat stack aligned with one another based upon the means of the roots of the S2 heart sounds;
FIG. 36 illustrates a process for identifying outliers in a diastole region of an ausculatory sound signal;
FIG. 37 illustrates a flow chart of a process for selecting valid heart cycles and analyzing the results therefrom;
FIG. 38 illustrates a portion an auscultatory sound signal during diastole, and associated noise power thresholds;
FIG. 39a illustrates a auscultatory sound signals during diastole for a plurality of heart cycles;
FIG. 39b illustrates the auscultatory sound signals during diastole for the plurality of heart cycles illustrated in FIG. 39a, with the heart cycles aligned with respect to the mean times of the S2 heart sound; FIG. 40a illustrates the auscultatory sound signals during diastole for a plurality of heart cycles illustrated in FIG. 39b, including indications of the start of the Sl heart sound and the end of diastole;
FIG. 40b illustrates the auscultatory sound signals during diastole for the plurality of heart cycles illustrated in FIG. 40a, with the heart cycles temporally normalized and resampled;
FIG. 41 illustrates a cross-correlation process;
FIG. 42 illustrates a image of a plurality of localize cross-correlation signals associated with auscultatory sound signals from a plurality of heart cycles;
FIG. 43 illustrates a time-frequency analysis based on a continuous wavelet transform of an auscultatory sound signal during diastole;
FIG. 44 illustrates a process for detecting coronary artery disease from features of cross-correlation images generated from analysis of auscultatory sound signals during diastole for a plurality of heart cycles;
FIG. 45 illustrates a display of results in a heart/arteries view and textual view;
FIG. 46a illustrates a displace of a Receiver Operating Characteristic (ROC) curve;
FIG. 46b illustrates line graph display of true positives, true negatives, false negatives, and false positives;
FIG. 47 illustrates a display of a Heartbeat View comprising a graphical plot of the systolic and diastolic intervals of each heartbeat captured;
FIG. 48 illustrated a display of a Stacked Heartbeat View;
FIG. 49 illustrates a Bruit Identification View in a Line Graphs with Underfill mode,
FIG. 50 illustrates a display of a Bruit Identification View in a Spectrogram mode;
FIG. 51 illustrates a display of a Bruit Analysis View;
FIG. 52 illustrates a comparison of a current test with a previous test, using the Bruit Identification View, Spectrogram mode, to confirm success of PCI; and
FIG. 53 illustrates a comparison of a current test with a previous test, using the Bruit Identification View, Line Graph with Underfill mode, to confirm success of PCI.
DESCRIPTION OF EMBODIMENT(S)
Referring to FIGS. 1 and 2, an auscultatory coronary-artery-disease detection system 10 incorporates at least one auscultatory sound sensor 12 that is operatively coupled to a recording module 13 running at least a first portion 14.1 of a Data Recording Application (DRA) 14, 14.1 on a first specialized computer or electronic system comprising a first computer processor or FPGA (Field Programmable Gate Array) 15 and first memory 17 powered by a first power supply 19, which provides for recording and preprocessing auscultatory sound signals 16 from the at least one auscultatory sound sensor 12. For example, in one set of embodiments, the at least one auscultatory sound sensor 12 comprises a first group 12’ of three auscultatory sound sensors 12, 121’, 12r, 123’ physically interconnected end-to-end with one another, and physically and electrically interconnected with a first wireless transceiver 18; and a second group 12” of three auscultatory sound sensors 12, 121”, 122”, 123” physically interconnected end-to-end with one another, and physically and electrically interconnected with the first wireless transceiver 18, with both groups 12% 12” of auscultatory sound sensors placed on the skin of the thorax 20 of a test-subject 22, in acoustic communication therewith. Referring also to FIG. 3, the placement of the first group of auscultatory sound sensors 12’ in FIG. 1 is illustrated with the respective associated auscultatory sound sensors 12, 121’, 12r, 123’ in substantial alignment with the corresponding respective third R3, fourth R4 and fifth R5, inter-costal spaces on the right side 20R of the thorax 20, and the placement of the second group of auscultatory sound sensors 12” in FIG. 1 is illustrated with the respective associated auscultatory sound sensors 12, 121”, 122”, 123” in substantial alignment with the corresponding respective third L3, fourth L4 and fifth L5, inter-costal spaces on the left side 20L of the thorax 20. Prospective sensor locations R2-R5, S2-S5, and L2-L5 illustrated in FIG. 3 respectively refer to the second R2 through fifth R5 inter-costal spaces on the right side 20R of the thorax 20, the second S2 through fifth S5 inter-costal spaces at the center/sternum 20s of the thorax 20, and the second L2 through fifth L5 inter-costal spaces on the left side 20L of the thorax 20. Furthermore, prospective left-side posterior sensor locations LP2 and LP3 illustrated in FIG. 3 respectively refer to at the second LP2 and third LP3 intercostal spaces locations on the posterior of the thorax 20. Yet further, prospective sensor locations HA-1 and HA-2 are proximate to the apex of the heart, either on the anterior or the posterior of the thorax 20. For example, in one set of embodiments, The auscultatory sound sensors 12, 121’, 122’, 123”, 121”, 122”, 123” are located at the second S2, L2, third S3, L3 and fourth S4, L4 inter-costal spaces at the sternum S2-S4 and leftside L2-L4 of the thorax 20.
As used herein, the term “auscultatory sound” is intended to mean a sound originating from inside a human or animal organism as a result of the biological functioning thereof, for example, as might be generated by action of the heart, lungs, other organs, or the associated vascular system; and is not intended to be limited to a particular range of frequencies— for example, not limited to a range of frequencies or sound intensities that would be audible to a human ear,— but could include frequencies above, below, and in the audible range, and sound intensities that are too faint to be audible to a human ear. Furthermore, the term“auscultatory-sound sensor” is intended to mean a sound sensor that provides for transducing auscultatory sounds into a corresponding electrical or optical signal that can be subsequently processed.
The auscultatory sound sensors 12, 121’, 12 , 123’ , 121”, 122”, 123” provide for transducing the associated sounds received thereby into corresponding auscultatory sound signals 16 that are preprocessed and recorded by an associated hardware-based signal conditioning/preprocessing and recording subsystem 25, then communicated to the first wireless transceiver 18, and then wirelessly transmitted thereby to an associated second wireless transceiver 26 of an associated wireless interface 26’ of an associated docking system 27, possibly running a second portion 14.2 of the Data Recording Application (DRA) 14, 14.2 on a corresponding second specialized computer or electronic system comprising an associated second computer processor or FPGA (Field Programmable Gate Array) 28 and second memory 30, both of which are powered by an associated second power supply 32, which together provide for recording and preprocessing the associated auscultatory sound signals 16 from the auscultatory sound sensors 12, 121’, 12 , 123 , 121”, 122”, 123 .
For example, in accordance with one set of embodiments, the hardware-based signal conditioning/preprocessing and recording subsystem 25 includes an amplifier— either of fixed or programmable gain, — a filter and an analog-to-digital converter (ADC). For example, in one set of embodiments, the analog-to-digital converter (ADC) is a 16- bit analog-to-digital converter (ADC) that converts a -2.25 to +2.25 volt input to a corresponding digital value of - 32, 768 to + 32, 767. Furthermore, in accordance with one set of embodiments of the amplifier, the amplifier gain is programmable to one of sixteen different levels respectively identified as levels 0 to 15, with corresponding, respective gain values of 88, 249, 411, 571, 733, 894, 1055, 1216, 1382, 1543, 1705, 1865, 2027, 2188, 2350 and 2510, respectively for one set of embodiments. In accordance with another set of embodiments of the amplifier, the amplifier gain is fixed at the lowest above value, i.e., for this example, 88, so as to provide for avoiding the relative degradation of the associated signal-to-noise ratio (SNR) that naturally occurs with the relatively high gain levels of the programmable-gain set of embodiments.
It should be understood that the associated processes of the Data Recording Application (DRA) 14 could be implemented either in software-controlled hardware, hardware, or a combination of the two. For example, in one set of embodiments, either or both the recording module 13 or docking system 27 may be constructed and operated in accordance with the disclosure of U.S. Provisional Application No. 62/575,364 filed on 20 October 2017, entitled CORONARY ARTERY DISEASE DETECTION SYSTEM, which is incorporated by reference in its entirety. Furthermore, in accordance with one set of embodiments, the auscultatory coronary-artery-disease detection system 10 may further incorporate an ECG sensor 34, for example, in one set of embodiments, an ECG sensor 34’ comprising a pair of electrodes incorporated in a corresponding pair of auscultatory sound sensors 12, wherein the signal from the ECG sensor 34’ is also preprocessed and recorded by a different signal channel of the same hardware-based signal conditioning/preprocessing and recording subsystem 25 of the recording module 13 that is used to preprocess the signals from the one or more auscultatory sound sensors 12. Alternatively, the ECG sensor 34 may comprise a separate set of a pair or plurality of electrodes that are coupled to the skin of the test subject, for example, in one set of embodiments, a pair of signal electrodes 35, 35+/ in cooperation with a ground electrode 35°, wherein, referring to FIG. 3 (illustrating the locations of the electrodes 35, 35+/ , 35°), in accordance with one non-limiting embodiment, the signal electrodes 35, 35+/ span the heart from diametrically-opposed quadrants of the torso 44, and the ground electrode 35° is located in a different quadrant, orthogonally displaced from a midpoint of a baseline connecting the signal electrodes 35, 35+/ . Alternatively the signal electrodes 35, 35+/ could span across the top of the top of the thorax 20, or at any pair of locations commonly used for conventional ECG tests. Furthermore, in one set of embodiments, the recording module 13 and docking system 27 may each incorporate a corresponding respective USB interface 36.1, 36.2 to provide for transferring corresponding auscultatory sound signals 16 and or an electrographic signal 37 - from an associated ECG sensor 34, 34’— from the recording module 13 to the docking system 27, for example, rather than relying upon the first 18 and second 26 wireless transceivers of an associated wireless interface 26’. Further alternatively, either instead of, or in addition to, the wireless interface 26’ or the USB interface 36.1, 36.2, data may be transferred from the recording module 13 to the docking system 27 via a portable memory element, for example, either an SD memory card or a Micro SD memory card.
The functionality of the Data Recording Application (DRA) 14 is distributed across the recording module 13 and the docking system 27. For example, referring to FIG. 2, in accordance with a first aspect 10’ of an auscultatory coronary-artery-disease detection system 10, 10’, the Data Recording Application (DRA) 14 spans across the recording module 13 and the docking system 27, with a first portion 14.1 comprising the hardware- based signal conditioning/preprocessing and recording subsystem 25 operative on the recording module 13, and a remaining second portion 14.2 operative on the docking system 27. Alternatively, as another example, referring to FIG. 4, in accordance with a second aspect 10” of an auscultatory coronary-artery-disease detection system 10, 10”, the Data Recording Application (DRA) 14 is operative entirely on the recording module 13
The auscultatory sound sensor 12 provides for sensing sound signals that emanate from within the thorax 20 of the test-subject 22 responsive to the operation of the test- subject’s heart, and the resulting flow of blood through the arteries and veins, wherein an associated build-up of deposits therewithin can cause turbulence in the associated blood flow that can generate associated cardiovascular-condition-specific sounds, the latter of which can be detected by a sufficiently-sensitive auscultatory sound sensor 12 that is acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22. For some cardiovascular- conditions associated with, or predictive of, a cardiovascular disease, the sound level of these cardiovascular-condition-specific sounds can be below a level that would be detectable by a human using a conventional stethoscope. However, these sound levels are susceptible to detection by sufficiently sensitive auscultatory sound sensor 12 that is sufficiently acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22. For example, in one of embodiments, the auscultatory sound sensor 12 may be constructed in accordance with the teachings of U.S. Provisional Application No. 62/568,155 filed on 04 October 2017, entitled AUSCULTATORY SOUND SENSOR. Furthermore, in another set of embodiments, the auscultatory sound sensor 12 may be constructed in accordance with the teachings of U.S. Pat. Nos. 6,050,950, 6,053,872 or 6,179,783, which are incorporated herein by reference.
Referring also to FIG. 5a, the auscultatory sound sensors 12, 121’, 12 , 123’ , 121”, 122”, 123” are acoustically coupled to the skin 38 of the thorax 20 of the test-subject 22 via an acoustic interface 40, for example, via a hydrogel layer 40’, that also functions as an associated adhesive interface 42that is attached to the associated auscultatory sound sensor 12, 121’, 12 , 123’ , 121”, 122”, 123” with a first adhesive layer 42.1, for example, either a first surface 40.1’ of the hydrogel layer 40’ or a first layer of double-sided tape 42.1’ on a first side of the acoustic/adhesive interface 40, 42, and that is attached to the skin 38 of the thorax 20 of the test-subject 22 with a second adhesive layer 42.2, for example, either a second surface 40.2’ of the hydrogel layer 40’ or a second layer of double-sided tape 42.2’ on the opposing, second side of the acoustic/adhesive interface 40, 42. When fully coupled - as illustrated in FIG. 5a,— the auscultatory sound sensor 12, 121’, 122’, 123’ , 121”, 122”, 123” is fully attached to the acoustic/adhesive interface 40, 42 via the first adhesive layer 42.1, 42.1% 40.1% and the acoustic/adhesive interface 40, 42 is fully attached to the skin 38 of the thorax 20 of the test-subject 22 via the second adhesive layer 42.2, 42.2% 40.2% so that sound signals from within the thorax 20 of the test-subject 22 can propagate otherwise unimpeded to the auscultatory sound sensor 12, 121’, 12 , 123’ , 12'", 122”, 123”, thereby providing for a maximum achievable level of the corresponding associated auscultatory sound signals 16, and thereby improving the prospect of detecting an associated abnormal cardiovascular condition - if present - therefrom. Referring to FIGS. 5b and 5c - with the acoustic/adhesive interface 40, 42 respectively detached from the skin 38 or detached from the auscultatory sound sensor 12, respectively,— if the auscultatory sound sensor 12, 121’, 12r, 123’ , 121”, 122”, 123” is completely detached from the skin 38 of the thorax 20 of the test-subject 22, and thereby fully decoupled therefrom, the resulting auscultatory sound sensor 12, 121’, 12r, 123’ , 121”, 122”, 123” would be substantially non- responsive to sound signals from within the thorax 20 of the test-subject 22. Referring to FIGS. 5d to 5g, if the auscultatory sound sensor 12, 12r, 12 , 123’ , 121”, 122”, 123” is partially attached to the skin 38 of the thorax 20 of the test-subject 22, and thereby partially decoupled therefrom - i.e., in a condition referred to herein as being debonded therefrom— the resulting auscultatory sound sensor 12, 121’, 12 , 123’ , 121”, 122”, 123” would be only partially responsive to sound signals from within the thorax 20 of the test-subject 22, but not sufficiently responsive to provide for an associated auscultatory sound signal 16 of sufficient amplitude to provide for reliably detecting a prospective associated abnormal cardiovascular condition. More particularly, FIGS. 5d and 5e respectively illustrate an acoustic/adhesive interface 40, 42 partially detached from skin 38, and an acoustic/adhesive interface 40, 42 partially detached from an auscultatory sound sensor 12, respectively. Furthermore, FIG. 5f illustrates an auscultatory sound sensor 12 attached to a wrinkled acoustic/adhesive interface 40, 42, and FIG. 5g illustrates an acoustic/adhesive interface 40, 42 attached to wrinkled skin 38. In anticipation of prospective problems with nature of the attachment of the acoustic/ adhesive interface 40, 42, the Data Recording Application (DRA) 14 is provided with a means - described more fully hereinbelow— for detecting if one or more auscultatory sound sensors 12, 12r, 12 , 123’ , 121”, 122”, 123” is, or are, either detached or debonded from the skin 38 of the thorax 20 of the test-subject 22, so as to provide for excluding data from auscultatory sound sensors 12, 12r, 12r, 123’ , 121”, 122”, 123” that are either detached or debonded, from the skin 38 of the thorax 20 of the test-subject 22 from being used to diagnose a prospective abnormal cardiovascular condition. Generally, the adhesive interface 42 could comprise either a hydrogel layer 40’, for example, P-DERM® Hydrogel; a silicone material, for example, a P-DERM® Silicone Gel Adhesive; an acrylic material, for example, a P-DERM® Acrylic Adhesive; a rubber material; a synthetic rubber material; a hydrocolloid material; or a double-sided tape, for example, with either rubber, acrylic or silicone adhesives.
Referring to FIG. 6, it has been found that the quality of the auscultatory sounds acquired from a test-subject 34 can be improved if the torso 44 of the test-subject 34 is inclined, for example, in one set of embodiments, at an inclination angle Q of about 30 degrees above horizontal— but generally, as close to upright (i.e. q = 90 degrees) as can be accommodated by the associated adhesive interface(s) 42 of the associated auscultatory sound sensors 12, 121’, 12 , 123’ , 121”, 122”, 123” - which imposes a transverse component of gravitational force on each of the auscultatory sound sensors 12, 121’, 12r, 123’ , 121”, 122”, 123” that is resisted by the associated adhesive interface(s) 42.
Referring to FIGS. 7-15, in accordance with a first aspect 700, an auscultatory- sound-sensing process 700 provides for first determining a scale factor SF from an initially- acquired block of auscultatory-sound-sensor time-series data S, and initially determining from the scale factor SF whether one or more of the auscultatory sound sensors 12, 121’, 122’, 123’ , 121”, 122”, 123” is detached from the skin 38 of the thorax 20 of the test-subject 22, wherein when multiplied by the scale factor SF, the values of the associated auscultatory-sound-sensor time-series data S are nominally within a range that is a predetermined percentage of the dynamic range of the associated data acquisition system (for example, that provides 16- bit signed digital values). If there is no detachment, the first aspect 700, the auscultatory-sound-sensing process 700 provides for acquiring successive blocks of auscultatory-sound-sensor time-series data S while the test-subject 22 is holding their breath, and determining from each block of auscultatory-sound-sensor time-series data S - using an associated predetermined debond-detection threshold - whether or not one or more auscultatory sound sensors 12, 121’, 122’, 123’ , 121”, 122”, 123” is debonded from the skin 38 of the thorax 20 of the test-subject 22, or whether there is excessive noise in the auscultatory-sound-sensor time-series data S. The auscultatory-sensor time-series data S is rejected if excessive noise is detected, and the test is aborted if one or more auscultatory sound sensors 12, 121’, 122’, 123’ , 121”, 122”, 123” has become decoupled from the skin 38 of the thorax 20.
More particularly, referring to FIG. 7, the first aspect 700 of the auscultatory- sound-sensing process 700 commences with step (702) by initializing a data-block counter i to a value of zero, and then, in step (704), acquiring a block of Ns contiguous samples of auscultatory-sound-sensor time-series data S in accordance with a first aspect 800 of a data acquisition process 800. This initially-acquired data is then used to determine a scale factor SF that is used to determine whether or not one or more auscultatory sound sensors 12, 121’, 12r, 123’ , 121”, 122”, 123” is/are detached from the skin 38 of the thorax 20 of the test-subject 22, and then subsequently to scale subsequent blocks of time-series data S. The initial block of auscultatory-sound-sensor time-series data S may be acquired either with, or without, the test-subject 22 holding their breath, but typically with the test-subject 22 allowed to breath normally— for their comfort and convenience. The number of samples Ns to be acquired is given by the product of an acquisition period Si in seconds, times a sampling frequency Fs in Hz. For example, in one set of embodiments, in step (704), the initially-acquired block of auscultatory-sound-sensor time-series data S typically contains 10 seconds of data, which at a sampling frequency Fs of 24 KHz, results in Ns = Si *Fs = 240,000 samples.
More particularly, referring to FIG. 8, the data acquisition process 800 commences with step (802) by pre-filtering the electronic signal from the associated auscultatory sound sensor 12, 121’, 12r, 123’ , 121”, 122”, 123” with an analog anti-aliasing filter, for example, an analog second-order band-pass filter having a pass band in the range of 3 Hz to 2.5 KHz, for which the upper-cutoff frequency is sufficiently below the sampling frequency (i.e. no more than half) so as to prevent high frequency components in the signal being sampled from appearing as low frequency components of the sampled signal. Following step (802), if, in step (804), the test-subject 22 need not necessarily hold their breath - as is the case for the initially-acquired block of auscultatory-sound-sensor time-series data S,— then, in step (806), the pre-filtered auscultatory sound signal 16 is sampled at the sampling frequency Fs and converted to digital form by the associated analog-to-digital (ADC) converter. Then, from step (808), the auscultatory sound signal 16 continues to be sampled in step (806) until Ns samples of the block of auscultatory-sound-sensor time-series data S have been acquired, after which, in step (810), the Ns samples of auscultatory-sound-sensor time- series data S are returned to step (704) of the auscultatory-sound-sensing process 700. For example, FIGS. 9 and 10a each illustrate a first block of auscultatory-sound-sensor time- series data S of duration So that was recorded from one of the auscultatory sound sensors 12, 121’, 122’, 123 , 121”, 122”, 123”
Referring again to FIG. 7, following step (704), in step (706), the scale factor SF is determined from the initially-acquired block of auscultatory-sound-sensor time-series data S, in accordance with an associated scale-factor-determination process 1100. More particularly, referring to FIGS. 11 and 12a, the scale-factor-determination process 1100 commences with step (1102), wherein the block of auscultatory-sound-sensor time-series data S is divided into a plurality of data segments 46, for example, each of the same data- segment duration SD that nominally spans a single heartbeat, for example, about one second. For example, in FIG. 12a, the So = 10 second block of auscultatory-sound-sensor time- series data S is divided into Nseg = 10 data segments 46, each of SD =one second duration. FIG. 12a illustrates a time series |S| containing the absolute values of the auscultatory- sound-sensor time-series data S illustrated in FIG.lOa. As indicated by X’s in FIG. 12a, for each data segment 46, m spanning the range k=kMiN(m) to k=kMAx(m) of the auscultatory-sound-sensor time-series data S(k), the corresponding maximum absolute value of the auscultatory-sound-sensor time-series data S(k) is determined, as given by:
Figure imgf000015_0001
Then, in step (1104), the median of these maximum values is determined, as given by
Figure imgf000015_0002
Finally, in step (1106), the scale factor SF is determined, as given by:
Figure imgf000015_0003
wherein DRo-p is the nominal zero-to-peak dynamic range of the auscultatory-sound-sensor time-series data S after scaling, i.e. after multiplying the acquired values by the scale factor SF. For example, in one set of embodiments, the nominal zero-to-peak dynamic range is set to be about 80 percent - more broadly, but not limiting, 80 percent plus or minus 15 percent - - of the zero-to-peak range of the associated analog-to-digital converter— for example, in one set of embodiments, a 16-bit signed analog-to-digital converter— used to digitize the auscultatory-sound-sensor time-series data S in step (806). In one set of embodiments, the scale factor SF is integer-valued that, for an attached and bonded auscultatory sound sensor 12, 121’, 12 , 123’ , 121”, 122”, 123”, ranges in value between 1 and 28.
If one or more of the associated auscultatory sound sensors 12, 121’, 12r, 123’ , 121”, 122”, 123” is detached from the skin 38 of the thorax 20 of the test-subject 22, then the associated level of the auscultatory sound signals 16 will be low - for example, at a noise level - resulting in a relatively large associated scale factor SF from step (1106). Accordingly, if, in step (1108), the scale factor SF is in excess of an associated threshold SFMAX, then the Data Recording Application (DRA) 14 is aborted in step (1110), and the operator 48 is alerted that the one or more auscultatory sound sensors 12, 121’, 122’, 123’ , 121”, 122”, 123” is/are detached, so that this can be remedied. For example, in one set of embodiments, the value of the threshold SFMAX is 28 for the above-described fixed-gain embodiment, i.e. for which the associated amplifier has a fixed gain of 88, feeding a 16- bit analog-to-digital converter (ADC) that provides for converting a +/- 5 volt input signal to +/- 32,767. Otherwise, from step (1108), if the value of the scale factor SF does not exceed the associated threshold SFMAX, in step (1112), the scale factor SF is returned to step (706) for use in scaling subsequently-recorded breath-held auscultatory sound signals 16.1.
Referring again to FIG. 7, following step (706), in step (708), the value of the data- block counter i is incremented, so as to point to the next block of auscultatory-sound- sensor time-series data S to be recorded. If, while this next block is recorded, the auscultatory sound sensors 12, 121’, 12r, 123’ , 121”, 122”, 123” remain attached and bonded to the skin 38 of the thorax 20 of the test-subject 22, and the associated breath-held auscultatory sound signals 16.1 are not corrupted by excessive noise, then this next block of auscultatory-sound-sensor time-series data S will then be subsequently used to detect a prospective abnormal cardiovascular condition therefrom. The auscultatory sound signals 16 used to detect prospective abnormal cardiovascular conditions are recorded while the test- subject 22 holds their breath, the latter to prevent the resulting cardiovascular-based auscultatory sound signals 16 from being overwhelmed by breathing-related sounds that are substantially louder than cardiovascular-based sounds, thereby providing for improving the associated signal-to-noise ratio of the cardiovascular-based auscultatory sound signals 16.
Accordingly, in step (710), a next block of Ns contiguous samples of auscultatory- sound-sensor time-series data S is acquired over an acquisition period Si in accordance with a first aspect 800 of a data acquisition process 800, during which time the test-subject 22 is instructed to hold their breath. For example, in one set of embodiments, the nominal acquisition period Si is 10 seconds - but at least 5 seconds,— which, at a sampling frequency Fs of 24 KHz, results in Ns = Si *Fs = 240,000 samples.
More particularly, referring again to FIG. 8, the data acquisition process 800 commences with step (802) by pre-filtering the electronic signal from the associated auscultatory sound sensor 12, 121’, 122’, 123 , 121”, 122”, 123” with the above-described analog anti-aliasing filter. Then, from step (804), because breath-held data is to be acquired, in step (812), the test-subject 22 is instructed by the operator 48 to hold their breath. In step (814), if the operator 48 observes that the test-subject 22 is compliant in holding their breath, or, additionally or alternatively, if this is confirmed by a below-described breath- hold detection process 1400, then upon manual initiation by the operator 48, in step (816), a sample counter j is initialized to a value of one, and, in step (818), the next sample of pre- filtered auscultatory sound signal 16 is sampled at the sampling frequency Fs and converted to digital form by an associated analog-to-digital (ADC) converter. This process continues until either Ns = Si *Fs samples have been acquired, or the test-subject 22 resumes breathing. More particularly, in step (820), if one or more addition samples remain to be acquired, and if the operator 48 continues to observe that the test-subject 22 is holding their breath, or, additionally or alternatively, if this is confirmed by a below-described breath-hold detection process 1400, then, in step (822) the sample counter j is incremented, and the next sample is acquired in step (818). Otherwise, from step (820), if either all Ns = Si *Fs samples have been acquired, or if either the operator 48 observes that the test-subject 22 has resumed breathing, or, additionally or alternatively, if this is confirmed by a below-described breath-hold detection process 1400, or, if the test-subject 22 indicates by their own separate manual switch input that they will resume breathing, then, in step (824), the data acquisition process 800 terminates, and the block of breath-held auscultatory-sound-sensor time-series data S containing Ns =./ samples is returned to step (710). In one set of embodiments, if, following step (814), the test-subject 22 is not holding their breath, the pre-filtered auscultatory sound signals 16 are also separately-recorded while waiting for the test-subject 22 to hold their breath, or resume doing so. In practice, the auscultatory sound signals 16 typically continue to be recorded between breath-held segments, that latter of which are identified by associated recorded start and stop times with respect to the associated continuous recording.
Referring also to FIG. 13, alternatively, or additionally, in step (814), the auscultatory coronary-artery-disease detection system 10 may further incorporate an accelerometer 50 operatively coupled to the thorax 20 of the test-subject 22 to provide an associated acceleration signal responsive to the motion of the thorax 20. With the test- subject 22 lying on their back at an inclined angle, for example, at about 30 degrees above horizontal, for example, as illustrated in FIG. 4 of U.S. Provisional Application No. 62/568,155 filed on 04 October 2017, entitled AUSCULTATORY SOUND SENSOR, which has been incorporated by reference herein in its entirety, the associated acceleration signal - operatively coupled to recording module 13 and possibly transmitted to the docking system 27— may be twice integrated either in recording module 13 or the docking system 27 to generate a measure of the peak-to-peak displacement of the thorax 20, which if greater than a threshold would be indicative of breathing by the test-subject 22.
More particularly, referring also to FIG. 14, for an auscultatory coronary-artery- disease detection system 10 incorporating an accelerometer 50 operatively coupled to the thorax 20 of the test-subject 22, an acceleration signal 52 therefrom may alternatively or additionally be processed by an associated breath-hold detection process 1400 to provide for automatically determining - for example, in step (814) of the data acquisition process 800 illustrated in FIG. 8 - whether or not the test-subject 22 is breathing, responsive to the determination, from the acceleration signal 52, of a peak-to-peak displacement of the thorax 20 of the test-subject 22. More particularly, beginning with, and in, step (1402), the respective previous/initial values of thorax displacement Yo and thorax velocity Vo, respectively, are each initialized to values of zero; a sample counter i is initialized to an initial value, for example, zero; the respective minimum YMIN and maximum YMAX values of thorax displacement are each set equal to the (initial) value of thorax displacement Yo, the values of the sample counter IMIN and IMAX at which the corresponding minimum YMIN and maximum YMAX values of thorax displacement occur are set equal to the initial value of the sample counter i, and a BreathingFlag is set to indicate that the test-subject 22 is breathing. Then, in step (1404), the current sample of thorax acceleration A is acquired. Then, in step (1406), if the sample counter i is equal to the initial value, i.e. i == 0, then, in step (1408), the previous value of thorax acceleration Ao (i.e. initially, the initial value thereof) is set equal to the value of the current sample of thorax acceleration ,4, i.e. Ao A, and then, in step (1410), the sample counter i is incremented, after which the breath- hold detection process 1400 repeats with step (1404).
Otherwise, from step (1406), if the current sample of thorax acceleration A is not the first sample, then, in step (1412), the current value of thorax velocity V is calculated by integrating the previous Ao and current A measurements of thorax acceleration, for example, using a trapezoidal rule, as follows:
Figure imgf000018_0001
wherein dt is the time period between samples, i.e. dt=l/Fs. Then, in step (1414), the current value of thorax displacement Y is calculated by integrating the above-calculated previous Vo and current V values of thorax velocity, for example, again using a trapezoidal rule, as follows:
U - ΰ .L + Ua (5,
Then, in step (1416), the respective previous values of thorax acceleration Ao, thorax displacement Yo and thorax velocity Vo are respectively set equal to the corresponding current values of thorax acceleration A, thorax velocity V and thorax displacement Y, respectively, that will each be used in subsequent iterations of steps (1412) and (1414). Then, in step (1418), if the current value of thorax displacement Y is greater than then current maximum value of thorax displacement TMAX - for example, as would result during a phase of chest expansion by the test-subject 22, - then, in step (1420), the current maximum value of thorax displacement YMAX is set equal to the current value of thorax displacement Y and the corresponding value of the sample counter IMAX associated therewith is set to the current value of the sample counter i. Otherwise, from step (1418) - for example, as would result from other than a phase of chest expansion by the test-subject 22,— if, in step (1422), the amount by which the current value of the sample counter i exceeds the value of the sample counter IMAX associated with the maximum value of thorax displacement YMAX is not equal to a threshold value D (the relevance of which is described more fully hereinbelow), then in step (1424), if the current value of thorax displacement Y is less than then current minimum value of thorax displacement YMIN - for example, as would result during a phase of chest contraction by the test-subject 22,— then, in step (1426), the current minimum value of thorax displacement YMIN is set equal to the current value of thorax displacement Y and the corresponding value of the sample counter IMIN associated therewith is set to the current value of the sample counter i. From either steps (1420) or (1426), in step (1428), if the amount by which the current maximum value of thorax displacement YMAX is greater the current minimum value of thorax displacement YMIN meets or exceeds a displacement threshold DUMAC, then, in step (1430), the BreathingFlag is set to indicate that the test-subject 22 is breathing, after which, in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404). Similarly, from step (1428), if the displacement threshold D YMAX is not exceeded, in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404). Further similarly, from step (1424) - for example, as would result from other than a phase of chest contraction by the test-subject 22,— if, in step (1432), the amount by which the current value of the sample counter i exceeds the value of the sample counter IMIN associated with the minimum value of thorax displacement YMIN is not equal to the threshold value D. in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404).
If, from step (1432), the amount by which the current value of the sample counter i exceeds the value of the sample counter IMIN associated with the minimum value of thorax displacement YMIN is equal to the threshold value D— following a minimum chest contraction of the test-subject 22, in anticipation of subsequent chest expansion, wherein the threshold value D is greater than or equal to one,— then, in step (1434), the peak-to-peak thorax displacement AY is calculated as the difference between the current maximum YMAX and minimum YMIN values of thorax displacement, and, in step (1436), the maximum value of thorax displacement YMAX is set equal to the current value of thorax displacement F, and the value of the sample counter IMAX at which the corresponding maximum value of thorax displacement YMAX occurred is set equal to the current value of the sample counter i, in anticipation of subsequently increasing magnitudes of the current value of thorax displacement F to be tracked in steps (1418) and (1420).
Similarly, if, from step (1422), the amount by which the current value of the sample counter i exceeds the value of the sample counter IMAX associated with the maximum value of thorax displacement YMAX is equal to the threshold value D— following a maximum chest expansion of the test-subject 22, in anticipation of subsequent chest contraction, wherein the threshold value D is greater than or equal to one,— then, in step (1438), the peak-to-peak thorax displacement DU is calculated as the difference between the current maximum YMAX and minimum YMIN values of thorax displacement, and, in step (1440), the minimum value of thorax displacement YMIN is set equal to the current value of thorax displacement F, and the value of the sample counter IMIN at which the corresponding minimum value of thorax displacement YMIN occurred is set equal to the current value of the sample counter i, in anticipation of subsequently decreasing magnitudes of the current value of thorax displacement F to be tracked in steps (1424) and (1426).
From either steps (1436) or (1440), in step (1442), if the amount of the peak-to-peak thorax displacement DU calculated in steps (1434) or (1438), respectively, meets or exceeds the displacement threshold D YMAX. then, in step (1444), the BreathingFlag is set to indicate that the test-subject 22 is breathing. Otherwise, from step (1442), if the amount of the peak- to-peak thorax displacement DU calculated in steps (1434) or (1438), respectively, does not exceed the displacement threshold DUMAC, then, in step (1446), the BreathingFlag is reset to indicate that the test-subject 22 is not breathing. Following either step (1444) or (1446), in step (1410), the sample counter i is incremented, after which the breath-hold detection process 1400 repeats with step (1404).
Referring again to FIG. 7, in step (712), a corresponding block of scaled auscultatory-sound-sensor time-series data S is calculated by multiplying the acquired block of auscultatory-sound-sensor time-series data S by the scale factor SF, and in step (714), the scaled auscultatory-sound-sensor time-series data S is analyzed by an associated debond detection process 1500 to determine whether or not any of the auscultatory sound sensors 12, 121’, 12 , 123’ , 121”, 122”, 123” was debonded from skin 38 of the thorax 20 of the test-subject 22 during the associated data acquisition process 800.
More particularly, referring to FIG. 15, the debond detection process 1500 commences with step (1502) by initializing a sample counter j to a value of one, and initializing a threshold counter TC to a value of zero, wherein the threshold counter TC is a count of the number of contiguous successive samples for which the value of the scaled auscultatory-sound-sensor time-series data S is less than an associated predetermined debond-detection threshold DT. For example, in one set of embodiments, the debond- detection threshold DT is set to a value that is about 20% of the achievable maximum value of the output from the analog-to-digital converter (ADC). Then, in step (1504), if the absolute value of the sample of scaled auscultatory-sound-sensor time-series data S, i.e. |S 0)1, is less than the predetermined debond-detection threshold DT (or, alternatively, if |SF*S(j)| < DT, thereby precluding a need to separately calculate and store scaled auscultatory-sound-sensor time-series data S), then in step (1506), the threshold counter TC is incremented, after which, in step (1508), if the value of the threshold counter TC does not exceed the number of samples in ND successive data segments 46, i.e. TC < ND *5D *FS, and in step (1510), if the sample counter j does not exceed the number of samples Ns in the block of scaled auscultatory-sound-sensor time-series data S, then, in step (1512), the sample counter j is incremented, and the process continues again with step (1504). Otherwise, from step (1504), if the absolute value of the current sample of scaled auscultatory-sound-sensor time-series data S, i.e. |S (j)|, is not less than the predetermined debond-detection threshold DT - indicating that the auscultatory sound sensor 12, 121’, 12 , 123’ , 121”, 122”, 123” is not debonded from the skin 38 of the thorax 20 of the test- subject 22,— then, in step (1514), the threshold counter TC is reset to a value of zero and the process continues with step (1510). Otherwise, from step (1510), if the sample counter j exceeds the number of samples Ns in the block of scaled auscultatory-sound-sensor time- series data S or auscultatory-sound-sensor time-series data S— indicating that the entire block of scaled auscultatory-sound-sensor time-series data S or auscultatory-sound- sensor time-series data S has been screened,— then the debond detection process 1500 is terminated with step (1516) by returning an indication to step (714) of the auscultatory- sound-sensing process 700 that the associated auscultatory sound sensor 12, 121’, 122’, 123’ , 121”, 122”, 123” is not debonded from the skin 38 of the thorax 20 of the test-subject 22. Otherwise, from step (1508), if the value of the threshold counter TC exceeds the number of samples in ND successive data segments 46, then the debond detection process 1500 is terminated with step (1518) by returning an indication to step (714) of the auscultatory- sound-sensing process 700 that the associated auscultatory sound sensor 12, 121’, 12r, 123’ , 121”, 122”, 123” is debonded from the skin 38 of the thorax 20 of the test-subject 22. For example, in one set of embodiments, the value of ND is equal to 4, and the value of & is equal to 1 second.
Returning to FIG. 7, in step (716), if, from step (714), the debond detection process 1500 detected that the associated auscultatory sound sensor 12, 121’, 12 , 123’ , 121”, 122”, 123” was debonded while acquiring the block of auscultatory-sound-sensor time-series data S, then the Data Recording Application (DRA) 14 is aborted in step (718), and the operator 48 is alerted that one or more auscultatory sound sensors 12, 121’, 122’, 123’ , 121”, 122”, 123” are debonded, so that this can be remedied. Otherwise, if, from step (714), the debond detection process 1500 did not detect that the associated auscultatory sound sensor 12, 121’, 122’, 123’ , 121”, 122”, 123” was debonded while acquiring the block of auscultatory-sound-sensor time-series data S, then, in step (720), if sufficient noise- screened data has not been gathered - for example, in one set of embodiments, a total duration of at least 65 seconds of recorded data,— then the auscultatory-sound-sensing process 700 continues with step (708).
In step (724), an associated noise detection (i.e. noise-screening) process - operating on either the block of scaled auscultatory-sound-sensor time-series data S, or the block of auscultatory-sensor time-series data S, in parallel with the debond detection process 1500 - provides for detecting if the block of auscultatory-sound-sensor time-series data S has been corrupted by excessive noise, and if so, from step (726), that block of auscultatory- sound-sensor time-series data S is ignored, and the auscultatory-sound-sensing process 700 continues by repeating step (710) to acquire a new block of auscultatory-sound-sensor time-series data S. Otherwise, from step (726), if the block auscultatory-sound-sensor time-series data S has not been corrupted by excessive noise, the process continues with the above-described step (720).
From step (720), if sufficient noise-screened data has been gathered for which the associated one or more auscultatory sound sensors 12, 121’, 122’, 123’ , 121”, 122”, 123” were not debonded from the skin 38 of the thorax 20 of the test-subject 22 - for example, in one set of embodiments, a total duration of at least 65 seconds of recorded data,— then, in step (722), at least the composite set of blocks of breath-held auscultatory-sound-sensor time- series data S acquired in step (710) are subsequently analyzed by an associated Data Analysis Application (DAA) 54 operative on the docking system 27 - as illustrated in FIGS. 2 and 3— so as to provide for detecting an abnormal cardiovascular condition of the test-subject 22. In addition to recording the segments of breath-held data, alternatively, all data may be recorded and provided to the Data Analysis Application (DAA) 54, along with an associated index that provides for identifying the corresponding associated breath-held portions thereof for which the associated auscultatory sound sensors 12, 121’, 12 , 123’ , 121”, 122”, 123” were neither detached nor debonded from the skin 38 of the thorax 20 of the test-subject 22, nor corrupted by noise.
FIGS. 9 and lOa-lOf illustrate a simulation of six blocks of breath-held auscultatory-sound-sensor time-series data S recorded in accordance with the first aspect 700 of auscultatory-sound-sensing process 700, with respective durations of di. <%, <%, d4, ds, and db during which time periods the test-subject 22 was holding their breath, separated by periods Di, D2. A3, D4, and As of normal breathing, wherein FIGS. 10a- lOe illustrate breath-held auscultatory sound signals 16.1, 16.1’ from a normally -bonded auscultatory sound sensor 12, 121’, 12r, 123’ , 121”, 122”, 123” as illustrated in FIG. 5a, and FIG. lOf illustrates a breath-held auscultatory sound signal 16.1, 16.1” from a debonded auscultatory sound sensor 12, 121’, 122’, 123’ , 121”, 122”, 123”, for example, as illustrated in any of FIGS. 5d-5g, for example, as might be caused by excessive hair between the adhesive interface 40 and the auscultatory sound sensor 12, poor placement of the auscultatory sound sensor 12 on the thorax 20 of the test-subject 22, poor angular orientation of the auscultatory sound sensor 12 relative to the surface of the skin 38, or wrinkled adhesive interface 40 between the auscultatory sound sensor 12 and the skin 38, For purposes of simplicity of illustration, FIGS. 10b- lOe are identical to FIG. 10a. However, it should be understood that typically the amplitude of the auscultatory sound signals 16, 16.1 varies from heartbeat to heartbeat, and from one breath-held segment to another.
Alternatively, one or more of the auscultatory-sound-sensing process 700, the data acquisition process 800, the scale-factor-determination process 1300, or the de-bond detection process 1500 could be implemented with corresponding alternative processes disclosed in U.S. Application Serial No. 16/136,015 filed on 19 September 2018 - with particular reference to FIGS. 16 through 22 - which is incorporated by reference herein in its entirety.
Referring to FIG. 16, in one set of embodiments, all the data is recorded throughout the duration of the test, including segments both with and without breathing, and a set of index pointers are used to identify locations of associated events, for example, index pointer arrays io[] and ii[] to store the sample locations at the beginning and end of breath-held data segments of the corresponding sampled auscultatory sound data Sm[] from the mth auscultatory sound sensor 12, and later-used index pointer arrays isi[] and is2 [] to store the sample locations of the Sl sound at the beginning of each heart cycle, and the S2 sound at the beginning of diastole respectively, wherein in FIG. 16a, the symbol“B” is used to indicate a period of breathing, the symbol Ή” is used to indicate a period of breath-holding, and the symbol“D” is used to indicate a period of diastole. A status array Status [m, k] indicates the measurement status of the kth breath-held data segment of the mth auscultatory sound signal 16, i.e. the sampled auscultatory sound data Sm() from the mth auscultatory sound sensor 12. Accordingly, step (728) that provides for ignoring data may be implemented by setting the corresponding value of the status array Status(m, k) to a value of IGNORE.
Referring to FIGS. 17-20, a noise detection process 1700 called from step (724) provides for determining whether or not a particular segment of breath-held auscultatory sound signal 16.1 is corrupted by excessive noise, and if so, provides for flagging that segment as being excessively noisy so as to be prospectively excluded from subsequent processing. Generally the noise detection process 1700 generates, in step (1714), frequency-domain noise filters FH[] responsive to cross-correlations of frequency spectra of pairs of adjacent auscultatory sound sensors 12. Accordingly, sensor-index pointers ml[p] and m2[p] provide for identifying the associated auscultatory sound sensors 12, ml[p], m21 p | of each pair, the latter of which is identified by pair pointer p. For each pair of adjacent auscultatory sound sensors 12 in a set of adjacent pairs— selected by sensor- index pointers ml[p] and m2[p] in steps (1706) and (1710), respectively, wherein p is a pair-pointer initialized to 1 in step (1704) - the noise detection process 1700 generates, in step (1714), a frequency-domain noise filter FH[] by cross-correlating in step (1802) the frequency spectra FSA[] and FSB[] (generated in steps (1708) and (1712) of each associated breath-held auscultatory sound signal 16.1: SA[] and SB[], wherein the values of the frequency-domain noise filter FH[] are generated by normalizing the frequency spectra of the cross-correlation function to a range of 0 to 1 in step (1804), then subtracting these values from unity, and then setting resulting values that are less than a noise floor to the value of the noise floor in step (1806). Accordingly, when used to multiply the frequency spectra FSA[] and FSB[] in step (1814) as called from steps (1718) and (1720) for frequency spectra FSA[] and FSB[], respectively, the frequency-domain noise filter FH[] provides for attenuating the components of the breath-held auscultatory sound signal 16.1: SA[] and SB[] that are correlated with one another. Accordingly, the operation of step (1904) provides for a matched filtering process to accentuate the underlying noise in the associated breath- held auscultatory sound signal 16.1: SA[] or SB[] The product of the frequency-domain noise filter FH[] with either of the frequency spectra FSA[] or FSB[] in step (1904) in inverse Fourier transformed back to the corresponding time domain noise signal SN[] Then, in steps (1908) through (1918), a plurality of NFFT-point short-time Fourier transform (STFT) arrays are generated, for example, for NFFT=1024, with each overlapped with respect on one another by a half width, i.e. NFFT 12, after which the associated average spectral power array FX[] is calculated in dB in step (1920). Then, , the average powers PLOW, PMID, PHIGH in three respective frequency ranges 20 Hz to 200 Hz, 200 Hz to 800 Hz, and 800 Hz to 1,500 Hz, respectively, is calculated in steps (1922), (1924) and (1926), respectively, wherein each average power PLOW, PMID, PHIGH is compared with a corresponding threshold— for example, in one set of embodiments, -20 dB, -50 dB and -30 dB, respectively - in step (1928). The corresponding breath-held auscultatory sound signal 16.1: SA[] or SB[] is then flagged in steps (1930) as being noisy if any of the associated noise power thresholds are exceeded. Referring to FIG. 20, if one auscultatory sound sensor 12 exceeds the noise threshold in a given kth breath-held segment of breath- held sampled auscultatory sound data Sm[ io [k]: ii [k]], that auscultatory sound sensor 12, m is ignored for that kth breath-held segment. If a particular auscultatory sound sensor 12, m exceeds the noise threshold for more than one breath-held segment of breath- held sampled auscultatory sound data Sm[ io [k]: ii [k]], then that auscultatory sound sensor 12, m is flagged as being bad. For each breath-held segment k of data, the process of steps (1706) through (1722) repeats for each of NPAIRS pairs of adjacent auscultatory sound sensors. For example, referring to FIG. 3, in one set of embodiments, there are three pairs of adjacent auscultatory sound sensors, i.e. NPAIRS=3, as follows: for p=l, the auscultatory sound sensors 12 at the left and sternum second intercostal spaces L2, S2; for p=2, the auscultatory sound sensors 12 at the left and sternum third intercostal spaces L3, S3; and for p=3, the auscultatory sound sensors at the left and sternum fourth intercostal spaces L4, S4. The process of steps (1704) through (1726) is repeated until all the breath-held segments k of data have been processed.
More particularly, referring to FIG. 17, the noise detection process 1700 commences with step (1702) by initializing a breath-held-segment pointer A to a value of 1, so as to provide for pointing to the first breath-held segment of data. Generally, the breath-held- segment pointer k provides for pointing to the kth breath-held segment of data, of duration 5k, extending between sample locations io[k] and ii [k], as illustrated in FIG. 16. Then, in step (1704), the pair pointer p is initialized to a value of 1, and a noise counter NNOISY is initialed to a value of 0. Then, in step (1706), the kth breath-held segment of breath-held sampled auscultatory sound data Sml[p][ io [k]: ii [k]] is selected as the sampled auscultatory sound data SA[] of the first auscultatory sound sensors 12, ml[p] of the pair p, and in step (1708) the Fourier Transform of the sampled auscultatory sound data SA[] is calculated as FSA[] = FFT(SA[]). Similar, then, in step (1710), the kth breath- held segment of breath-held sampled auscultatory sound data Sm2[p][ io [k] : ii [k]] is selected as the sampled auscultatory sound data SB[] of the second auscultatory sound sensor 12, m2[p] of the pair p, and in step (1712) the Fourier Transform of the sampled auscultatory sound data SA[] is calculated as FSB[] = FFT(SA[]).
Then, in step (1714), the frequency-domain noise filter FH[] generated by a matched-noise-filter generation process 1800 that — referring also to FIG. 18 — commences in step (1802) with the cross correlation of the frequency spectra FSA[], FSB[] of the sampled auscultatory sound data SA[], SB[] of the first ml[p] and second ui2[p] auscultatory sound sensors 12 of the pair p, wherein the resulting cross-correlation is stored in array FH[] and then normalized to a range of 0 to 1 in step (1804), and then inverted in step (1806), wherein each element of the normalized array FH[] is subtracted from 1, and if the result is less than a noise floor, is set to the value of the noise floor NoiseFloor. Then, in step (1808), the resulting frequency-domain noise filter FH[] is returned and subsequently used in steps (1716) and (1718) of the noise detection process 1700 to evaluate the noise in the frequency spectra FSA[], FSB[] of the sampled auscultatory sound data SA[], SB[] of the first ml[p] and second m2[p] auscultatory sound sensors 12 of the pair p, respectively, in accordance with an associated noise-content-evaluation process 1900, the latter of which is called from step (1716) to evaluate the noise content of the frequency spectrum FSA[] of the sampled auscultatory sound data SA[] of the first auscultatory sound sensor 12, ml[p] of the pair p, and which is called from step (1718) to evaluate the noise content of the frequency spectrum FSA[] of the sampled auscultatory sound data SA[] of the second auscultatory sound sensor 12, m2[p] of the pair p.
Referring to FIG. 19, the noise-content-evaluation process 1900 commences with step (1902) with receipt of the index m of the associated auscultatory sound sensor 12, m, the associated frequency spectrum FS[] of the associated auscultatory sound sensor 12, m, and the associated frequency-domain noise filter FH[] Then, in step (1904), the time domain noise signal SN[] - containing a total of NPTS data points, i.e. the number of data points in the breath-held sampled auscultatory sound data Sm[ io [k]: ii [k]]— is given by the inverse Fourier Transform of the product of the associated frequency spectrum FS[] with the associated frequency-domain noise filter FH[], corresponding to a time-domain cross-correlation of the corresponding associated time-domain signals. Then, in step (1906), each of the NFFT summation data points of a frequency-domain summation array FXSUM[] is initialized to zero, as is an associate summation counter NSUM, wherein the number of summation data points NFFT is a power of 2, for example, 1024, and substantially less than the total number of data points NPTS in the time domain noise signal SN[] Then, in step (1908), an index jMiN— to the first sample of an NFFT-point window of samples to be analyzed from the time domain noise signal SN[] - is initialized to a value of 1. Then, in step (1910), an index jMAx — to the last sample of the NFFT-point window of samples to be analyzed from the time domain noise signal SN[] - is set to the value of jniiN + NFFT - 1. Then, in step (1912), if the end of the time domain noise signal SN[] has not been reached, then, in step (1914), the square of values - i.e. corresponding to noise power— of an NFFT Fourier Transform of the data form the NFFT-point window of samples from the time domain noise signal SN[] over the range of samples SIN [j MIN ] to SNJJMAX], is added to the frequency-domain summation array FXSUM[] Then, in step (1916), the summation counter NSUM is incremented, and in step (1918), the index jMiN is incremented by half the width of the NFFT-point window, i.e. by a value of NFFT 12, so as the provide for the next NFFT-point window to be analyzed to overlap the current NFFT-point window by half the window width. The above noise-content-evaluation process 1900 repeats beginning with step (1910), until, in step (1912), the index jMAx exceeds the end of the time domain noise signal SN[], after which, in step (1920), each of the values of the frequency-domain summation array FXSUM[] is divided by the value of the summation counter NSUM SO as to calculate an associated average noise power FX[] Then, in steps (1922), (1924) and (1926), respectively, the noise power is summed in three different corresponding respective frequency ranges, for example, 20 Hz to 200 Hz, 200 Hz to 800 Hz, and 800 Hz to 1,500 Hz, respectively, to give three corresponding respective power levels, PLOW, PMID and PHIGH, respectively. Then, in step (1928), if any of the power levels, PLOW, PMID or PHIGH exceeds a corresponding respective power threshold value ThresholdLow, ThresholdMiD or ThresholdHiGH, then, in step (1930), the noise threshold is considered to have been exceed for the particular auscultatory sound sensor 12, m and the particular associated segment k of breath-held sampled auscultatory sound data Sm[ io [k]: ii [k]] with indication of an associated status in a NoiseStatus flag. Otherwise, from step (1928), if none of the power levels, PLOW, PMID or PHIGH exceeds the corresponding respective power threshold value ThresholdLow, ThresholdMiD or ThresholdmGH, then, in step (1932), the noise threshold is considered to have not been exceed for the particular auscultatory sound sensor 12, m and the particular associated segment of breath-held sampled auscultatory sound data Sm[ io [k]: ii [k]], with indication of an associated status in the NoiseStatus flag. The results from either steps (1930) or (1932) are then logged by an associated results-logging process 2000 which is called from step (1934). Referring to FIG. 20, the results-logging process 2000 commences with receipt in step (2002) of the index m of the associated auscultatory sound sensor 12, m and the associated NoiseStatus flag. If, in step (2004), the NoiseStatus flag indicates a noisy auscultatory sound sensor 12, m, then, in step (2006), the noise counter NNOISY is incremented, and, in step (2008), the corresponding element of the status array Status [m, k] for the associated auscultatory sound sensor 12, m and breath-held segment k is updated to activate an associate Noisy flag, so as to indicate the associated auscultatory sound sensor 12, m being noisy for the associated breath-held segment k. Then, in step (2010), if the value of the noise counter NNOISY is greater than 1, then, step (2012), the corresponding elements of the status array Status [m, k] for each of the auscultatory sound sensors 12, m is updated to activate the Ignore flag, so as to provide for ignoring each of the auscultatory sound sensors 12, m for the associated breath-held segment k. Then, or otherwise from step (2010), in step (2014), if the Noisy flag of the status array Status[m, k] is activated for at least NFAIL breath-held segments k for the associated auscultatory sound sensor 12, m, then, in step (2016), status array Status [m] for the associated auscultatory sound sensor 12, m is updated to activate the Bad flag for the associated auscultatory sound sensor 12, m, so as to indicate that the associated auscultatory sound sensor 12, m is bad. Then, or otherwise from either step (2004) or step (2014), results-logging process 2000 terminates by returning to the noise detection process 1700.
More particularly, referring again to FIG. 17, in step (1720), if all of the pairs of adjacent auscultatory sound sensors 12 have not been processed, then, in step (1722), the pair pointer p is incremented, and the noise detection process 1700 is repeated for the next pair of auscultatory sound sensors 12, ml[p], m2[p], beginning with step (1706) for the same breath-held segment k. Otherwise, from step (1720), if in step (1724), additional segments of breath-held sampled auscultatory sound data Sm[ io [k]: ii [k]] remain to be processed, then, in step (1726), the breath-held-segment pointer k is incremented, and the noise detection process 1700 is repeated beginning with step (1704) for the next segment of breath-held sampled auscultatory sound data Sm[ io [k]: ii [k]]. Otherwise, from step (1724), the noise detection process 1700 terminates with step (1728).
Referring again to FIG. 1, in accordance with one set of embodiments, the results from the docking system 27 may be transferred to a server computer system 56, for example, for subsequent transfer to an external data storage or processing system 58, for example, to provide for either viewing or analyzing the results at a remote location. Referring again to FIGS. 2 and 4, in one set of embodiments, the composite set of blocks of breath-held auscultatory-sound-sensor time-series data S are screened prior to analysis by the associated Data Analysis Application (DAA) 54, for example, by first segmenting the set of blocks of breath-held auscultatory-sound-sensor time-series data S by heart cycle with an associated segmentation process 60, and then validating the associated heart cycle data with an associated heart cycle validation process 62, for example, to provide for additional screening for heart-phase associated noise.
Referring to FIG. 21, an auscultatory sound signal preprocessing and screening process 2100 provides for preprocessing breath-held auscultatory sound signals 16.1 and an associated electrographic signal 37 from an ECG sensor 34, 34’, for example, as acquired, for example, in accordance with the above-described auscultatory-sound-sensing process 700, with concurrent recording of an associated electrographic signal 37 from an ECG sensor 34, 34’. In accordance with one embodiment as illustrated, a good beat counter GB is initialized to zero in step (2102).
Then, from step (2104), referring to FIG. 22a, a segment of breath-held auscultatory sound signals 16.1 is acquired and preprocessed by an associated auscultatory sound signal acquisition and filtering process 2200, wherein, in step (2202), for each auscultatory sound sensor 12, in step (2104), a block of data of associated breath-held auscultatory sound signals 16.1 is acquired, for example, in accordance with the auscultatory-sound-sensing process 700, or as an associated segment k of a previously acquired and recorded breath-held sampled auscultatory sound data Sm[ io [k]: ii [k] for the mth auscultatory sound sensor 12. With data recorded at a sampling frequency Fs of 24 kHz, and for portions of the spectrum of the breath-held auscultatory sound signals 16.1 of interest related to coronary artery disease limited to a few hundred Hertz - i.e. without significant acoustic energy above 500 Hz— the sampling rate may be reduced to 2 kHz. Accordingly, in step (2206), the breath-held auscultatory sound signal 16.1 is filtered with a fourth order Type II Chebyshev filter low-pass filter with cut-off 500 Hz to avoid aliasing, and then, in step (2208), decimated to a 2 kHz sampling rate using a phase invariant method that also involves low-pass filtering, so as to generate corresponding filtered-decimated breath-held sampled auscultatory sound signal 64.
The breath-held auscultatory sound signal 16.1 in some cases can include very low frequency - for example, around 10 Hz— vibrations that are believed to be associated with movements of the entire sensor stimulated by chest motion. Considering the sensor housing as an inertial mass under a tangential component of gravitational force attached to elastic surface, it is possible to initiate sensor vibration by small surface displacements. Such vibrations can be amplified by resonance characteristics of the tissue-sensor interface. Depending on the Q-factor of tissue-sensor system, vibrations may decay very slowly extending well into diastolic interval of heart beat, contaminating the signal of interest with large amplitude unwanted interference. The net effect of such interference is an unstable signal baseline and distortion of the actual underlying heart sounds. Potential sources of noise relevant to digitized acquisition of acoustic signals include: electric circuit thermal noise, quantisation noise of AD converter, electro-magnetic interference, power line 60 Hz interference and acoustic noises relevant to human physiology and the environment where recording is done (ambient noise). Generally thermal noise power and ADC quantization noise are very low for the bandwidth of interest and may be important only for the signals with amplitude in microvolt region. Furthermore, recording artifacts may significantly reduce visibility of the signal of interest since they may have high amplitude and overlap in spectral content. These artifacts are due to uncontrolled patient movements, signals related to respiration and sensor vibrations produced by oscillating mass attached to elastic tissue surface (cardio seismographic waves). The latter type of artifact may be caused by the inertial mass of sensor casing and can be high in amplitude due to resonance properties of the sensor- tissue interface. Although, frequency of such vibrations is relatively low (around 10 Hz), their high amplitude results in unstable signal baseline which complicates detection of target signals.
However cardiac activity may produce low frequency signals as well - for example, as a result of contraction of heart muscle, as a result of valvular sounds— that may have valuable diagnostic information. In some situations, the spectrum of the artifacts may overlap with acoustic spectrum of cardiac signals such as myocardium vibrations. Therefore, it can be beneficial to reduce baseline instability so as to provide for recording only acoustic signals originating from cardiac cycle.
In accordance with approach, these very low frequency artifacts may be rejected by using additional signal filtering to suppress the associated characteristic vibration frequencies, for example, using a software-implement high-pass filter 66 having a 3 dB cut off frequency above 10 Hz, for example, as provided for by a Savitzky-Golay-based high- pass filter 66’, wherein, in step (2212), the flltered-decimated breath-held sampled auscultatory sound signal 66 is smoothed by a Savitzky-Golay (SG) smoothing filter 68 to generated a smoothed breath-held sampled auscultatory sound signal 70, the latter of which, in step (2212), is subtracted from the flltered-decimated breath-held sampled auscultatory sound signal 64 to then generate the corresponding resulting high-pass- filtered breath-held sampled auscultatory sound signal 72 with good signal-to-noise ration (SNR) without significant distortions of original flltered-decimated breath-held sampled auscultatory sound signal 64. The digital Savitzky-Golay smoothing filter 68 is useful in stabilizing baseline wandering (for example, as may be exhibited in ECG signals) and provides for removing the low-frequency signal components without causing significant ringing artifacts. The Savitzky- Golay smoothing filter 68 employs a least squares approximation of a windowed signal using a polynomial function. The associated parameters of the Savitzky-Golay smoothing filter 68 include the window size M in samples that defines the associated cut-off frequency, and the polynomial degree N used for the approximation. The associated roll-off range is fairly wide and the cut-off frequency is somewhat arbitrary. For example, for the Savitzky- Golay smoothing filter 68 used in step (2210), the associated window size M - expressed in terms of window time duration tw - is in the range of 8 milliseconds to 100 milliseconds, and N=3. For example, a window time duration tw = 8 millliseconds provides a cut-off frequency approximately of 100 Hz, and window time duration tw = 25 millliseconds, provides for passing signal frequencies above 40 Hz through the Savitzky-Golay-based high-pass filter 66’.
The Savitzky-Golay smoothing filter 68 is defined by a least-squares fit of windowed original samples with an Nth degree polynomial,
Figure imgf000031_0001
so as to minimizes the associated error function, E:
Figure imgf000031_0002
wherein the total window width is 2M+1 samples. The associated short-time window sliding through entire time series fits the data with a smooth curve. The frequency response of the Savitzky-Golay smoothing filter 68 depends strongly on the window size M and polynomial order N. The normalized effective cut-off frequency of the Savitzky-Golay smoothing filter 68 is empirically given as follows, wherein fc = 00s / :
N + l
fc (8)
3.2M - 4.6
Following the high-pass filter 66, 66’ of steps (2210) and (2212), from step (2214), the auscultatory sound signal acquisition and filtering process 2200 is repeated beginning with step (2202) until each of the auscultatory sound sensors 12, after which, from step (2216), the resulting blocks of high-pass-filtered breath-held sampled auscultatory sound signals 72— for each of the auscultatory sound sensors 12— are returned to step (2104). Then, also from step (2104), referring to FIG. 22b, a segment of breath-held auscultatory sound signals 16.1 is acquired and preprocessed by an associated electrographic signal acquisition and filtering process 2200’, wherein, in step (2204’), a block of electrographic data 74 of the corresponding electrograhic signal 37 from the ECG sensor 34, 34’ - in correspondence with the blocks of high-pass-filtered breath-held sampled auscultatory sound signals 72 returned by the above-described auscultatory sound signal acquisition and filtering process 2200 - is acquired, for example, in accordance with the above-described data acquisition process 800, or as an associated segment k of previously acquired and recorded electrographic data 74. Then, in step (2206'), and as explained more fully herein below, the electrographic data 74 is filtered with a fourth order Type II Chebyshev filter low-pass filter with cut-off 40 Hz, and then, in step (2208’) , and decimated by a factor of ten, so as to generate corresponding filtered- decimated electrographic signal 76, which, in step (2216'), is returned to step (2104).
Referring again to FIG. 21, in step (2106), the high-pass-filtered breath-held sampled auscultatory sound signals 72, or alternatively, the breath-held auscultatory sound signals 16.1, are segmented in step (2106) to identify the starting and ending points of each heart cycle, and to identify the starting and ending points of each associated diastole region or phase, thereof. In accordance with a first aspect, this may be accomplished using the breath-held auscultatory sound signals 16.1— or the corresponding associated high- pass-filtered breath-held sampled auscultatory sound signals 72 - alone, without relying upon the associated electrograhic signal 37 from the ECG sensor 34, 34’, or upon the corresponding associated filtered-decimated electrographic signal 76, for example, in accordance with the following portions of the disclosure and drawings of U.S. Patent No. 9,364,184: Abstract, FIGS. 1-50, Col. 1, Line 1 through Col. 3, line 59 (indicated), Col. 5, line 1 through Col. 34, line 55 (indicated), and the Claims, which are incorporated herein by reference.
However, in accordance with a second aspect, the electrograhic signal 37 from the ECG sensor 34, 34’, and particularly, the corresponding associated filtered-decimated electrographic signal 76 responsive thereto, provide an effective basis for segmenting the breath-held auscultatory sound signals 16.1, 72 by heart cycle, after which the high-pass- filtered breath-held sampled auscultatory sound signal 72 may be used to locate the associated dominant SI and S2 heart sounds that provide for located the associated heart phases of systole and diastole, data from the latter of which provides for detecting coronary artery disease responsive to information in the breath-held auscultatory sound signals 16.1, 72. Referring also to FIG. 23, in accordance with the second aspect, a heart-phase identification process 2300 is called from step (2106) of the auscultatory sound signal preprocessing and screening process 2100 to locate the heart cycles responsive to the flltered-decimated electrographic signal 76, to then segment the high-pass-filtered breath-held sampled auscultatory sound signal 72 by heart cycle responsive thereto, and to then identify the region of diastole within each heart cycle (an implicitly, to therefor also identify the remaining region systole) from an analysis of the high-pass-filtered breath-held sampled auscultatory sound signal 72 alone.
Normal human cardiac cycle consist of four major intervals associated with different phases of heart dynamics that generate associated audible sounds: 1) the first sound (Sl) is produced by closing of mitral and tricuspid valves at the beginning of heart contraction, 2) the systolic interval when heart contracts and pushes blood from ventricle to the rest of the body, 3) the second sound (S2) originated from closing of aortic and pulmonary valves and 4) the diastolic interval is when the heart is relaxed and ventricles are filled with oxygenated blood.
Referring to FIGS. 24 and 25, respectively illustrating a breath-held auscultatory sound signal 16.1, 72 and a corresponding electrograhic signal 37, 76 associated with six heatbeats, each QRS complex 78 illustrated in FIG. 25 is generated by an associated ventricular depolarization that precedes cardiac contraction. The SI heart sound resulting from the closing of mitral and tricuspid valves is observed immediately after R-peak , and S2 heart sound resulting from the closing of aortic and pulmonary valves is observed as the end of T-wave of ECG cycle. This relative timing of the heart sounds SI, S2 and the QRS complex 78 of the associated electrographic signal 37, 76 provides for using the electrograhic signal 37 to locate the beginning and end of each heart cycle without relying upon the breath-held auscultatory sound signal 16.1, 72 to do so. For each heartbeat, the SI heart sound— marking the start of systole - follows shortly after the R-peak, and the S2 heart sound - marking the end of systole and the beginning of diastole - follows thereafter, with diastole continuing until the next R-peak.
Although QRS complex 78 is the most prominent feature, the electrographic signal 37, 76 may be distorted by low frequency baseline wandering, motion artefacts and power line interference. To stabilize the baseline, a Savitzky-Golay-based high-pass filter 66’ - similar to that used in steps (2210/2212) when filtering the breath-held auscultatory sound signal 16.1— may used to cancel low-frequency drift prior to the subsequent low-pass filtering with the 40 Hz cut-off frequency in step (2206’) and decimation of the sampling by factor of 10 in step (2208’) to provide for reducing high frequency noise and provide for emphasizing QRS complexes 78.
More particularly, the heart-phase identification process 2300 commences in step (2302) by calling an electrographic segmentation process 2600 that provides for identifying the locations of the R-peaks. More particularly, in step (2602), a block of electrographic data x[] is received, and in step (2604) normalized to have a maximum absolute value of unity. Then, in step (2606), beginning with step (2702) of an associated first envelope generation process 2700 with reference to FIG. 27, the block of flltered-normalized electrographic data x[] is used to generate an associated electrographic envelope waveform 80, Fs[] that emphasizes the R-peaks in the flltered-normalized electrographic data x[], wherein, for each value of index k— set in step (2702)— the corresponding value of the electrographic envelope waveform 80, Fs[k] is set in step (2704) responsive to a sum of values with in a sliding window containing a subset of Nw points, as follows:
Figure imgf000034_0001
Equation (9) is similar to a Shannon energy function but discrete signal values are raised to the fourth power to emphasize difference between R-peaks 80’ and baseline noise.
The value of the electrographic envelope waveform 80, Fs[] is calculated for each of the NPTS values of index k until, in step (2706), all points have been calculated, after which, in step (2708), the electrographic envelope waveform 80, Fs[] is returned to step (2606) of the electrographic segmentation process 2600.
Returning to FIG. 26, in step (2608), the peaks of the electrographic envelope waveform 80, Fs[] are located, for example, by finding indices k for which the associated value of the electrographic envelope waveform 80, Fs[k] is a local maximum exceeding a fixed, predetermined threshold. For example, FIG. 28 illustrates the electrographic envelope waveform 80, Fs[] and associated R-peaks 80’ thereof, for a portion of a 10- second recording of a filtered-decimated electrographic signal 76.
The electrographic segmentation process 2600 for finding R-peaks is quite simple and stable when signal-to-noise ratio (SNR) of recording is sufficiently high. Otherwise, when the electrographic data 74 is excessively noisy, additional signal processing such as discrete wavelet decomposition may be used to further enhance R-peaks and facilitate QRS detection. Occasional high amplitude transients due to patient movements or ambient interference may produce false peaks unrelated to QRS complex and complicate segmentation. Referring to FIG. 29, in step (2610), the R-peaks 80’ of the electrographic envelope waveform 80, Fs[] are then validated by a peak validation process 2900, both with respect to temporal location - i.e. relative spacing— and magnitude - i.e. deviation from a median value,— so as to verify that the detected R-peaks 80’ correspond to R-peaks 80’ rather than noise. More particular, in step (2902), for each of the detected R-peaks 80’, in step (2904), if the difference in temporal locations of each pair of R-peaks 80’ is less than a time threshold TMIN, or if, in step (2906), the difference between the magnitude P(tpEAK(i)) of each R-peak 80’and the median value of the magnitudes of all R-peaks 80’ is greater than a magnitude threshold VMAX, then, in step (2908), the status of the R-peak 80’ being tested is set to IGNORE, in order to prevent the associated R-peak 80’ from being used for subsequent segmentation of the breath-held auscultatory sound signal 16.1, 72. Otherwise, from step (2910), the peak validation process 2900 terminates in step (2912) and returns to step (2612) of the electrographic segmentation process 2600, which in turn returns the locations tpEAK[] to step (2302) of the heart-phase identification process 2300, wherein valid R-peaks 80’ satisfy both of the following conditions:
Figure imgf000035_0001
and
Figure imgf000035_0002
wherein tpEAK(i) is the time of the ith R-peak 80’ and R(ίrEAk(ΐ)) is the corresponding magnitude of the R-peak 80’. For example, FIG. 30 illustrates a correspondence between the breath-held auscultatory sound signal 16.1, 72 and the corresponding electrographic envelope waveform 80, Fs[], wherein the locations of the R-peaks 80’ in the latter are used to segment the former by heart cycle. More particularly referring again to FIG. 23, in step (2304), for each auscultatory sound sensor 12, in step (2306), the associated high-pass- filtered breath-held sampled auscultatory sound signal 72 thereof is segmented based upon the locations of the associated R-peaks 80’ in the corresponding flltered-decimated electrographic signal 76. For example, FIG. 31 illustrates a stack of segments of the high- pass-filtered breath-held sampled auscultatory sound signal 72, each segment corresponding to a different corresponding heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9— each resulting from a single corresponding heart beat— underlying the corresponding continuous high-pass-filtered breath-held sampled auscultatory sound signal 72 illustrated in FIG. 30.
The exact location of the first SI and second S2 heart sounds produced by the respective closing of atrioventricular and semilunar valves is somewhat ambiguous, and can be particularly difficult to locate in situations when the associated breath-held auscultatory sound signals 16.1 are recorded in an environment with relatively high level ambient noise, or if there are associated heart murmurs resulting from turbulent blood flow. However, even under relatively-poor recording conditions, the first SI and second S2 heart sounds of the cardiac cycle remain the most prominent acoustic features.
Referring again to FIG. 23, following segmentation of the high-pass-filtered breath- held sampled auscultatory sound signal 72 into corresponding associated segments in step (2306), each comprising one heart cycle 82— resulting from a single corresponding heart beat - beginning with step (2308), for each of the heart cycles 82 located in step (2306), in step (2310) a Shannon energy envelope is generated from the corresponding associated portion of the high-pass-filtered breath-held sampled auscultatory sound signal 72 in accordance with a corresponding second envelope generation process 3200, which is similar to the above-described first envelope generation process 2700 except for the associated envelope function. More particularly, referring to FIG. 32, beginning with step (3202) of an associated second envelope generation process 3200, for the kth of NSAMPLES samples of a block of high-pass-filtered breath-held sampled auscultatory sound data 72, s[] of the corresponding associated heart cycles 82, a corresponding value of an associated acoustic envelope waveform 84, Es[k] is generated in step (3204), responsive to a sum of values within a short-time sliding window - with different windows overlapping one another - containing a subset of Nw points, as follows in accordance with a canonical Shannon energy function Es[k] that provides a measure of the energy of the underlying high-pass- filtered breath-held sampled auscultatory sound signal 72, s[] :
Figure imgf000036_0001
The value of the acoustic envelope waveform 84, Es[k] is calculated for each of the NSAMPLES values of index k until, in step (3206), all points have been calculated, after which, in step (3208), the acoustic envelope waveform 84, Es[k] is returned to step (2310) of the heart-phase identification process 2300.
For envelope generation by each of the first 2700 and second 3200 envelope generation processes illustrated in FIGS. 27 and 32, the associated signal x[], s[], from which the corresponding envelope Fs[], Es[] is generated, is zero-padded at the beginning and at the end with (Nw - l)/2 zeros, assuming Nw is an odd integer, wherein the first data point in the padded signal x[], s[] would then begin at index l+(Nw - l)/2. After that, sliding window with size Nw and stride = 1 is used to compute windowed values of the associate envelope function, so as to provide for generating a time-series of the same length as the associated signal x[], s [], without a time shift relative thereto.
Referring again to FIG. 23, in step (2312), the acoustic envelope waveform 84, Es[k] generated in step (2310) is smoothed to reduce local fluctuations therein, for example using either a moving average filter, or a Savitzky-Golay smoothing filter 68 with window size of tw = 8.3 milliseconds.
FIG. 33 illustrates an example of an acoustic envelope waveform 84, Es[] in correspondence with a rectified version 86 of the associated high-pass-filtered breath-held sampled auscultatory sound data 72, s[] from which the acoustic envelope waveform 84, Es[k] was generated (i.e. containing an absolute value thereof), wherein the respective envelope peaks 88S1, 88s2 associated with the corresponding SI and S2 heart sounds can be readily identified in the acoustic envelope waveform 84, Es[].
Referring again to FIG. 23, in step (2314), the locations of the envelope peaks 88S1, 88s2 associated with the corresponding SI and S2 heart sounds are identified using an adaptive threshold method that iteratively adjusts an associated threshold value down from a maximum value until the two most prominent envelope peaks 88S1, 88s2 emerge within the associated heart cycle 82, above the a particular threshold limit. The final position of envelope peaks 88S1, 88s2 associated with the corresponding SI and S2 heart sounds is found as the apex of a local quadratic model 90.1, 90.2 of the acoustic envelope waveform 84, Es[] . Adaptive thresholding provides for accommodating substantial variation in the relative magnitude of the envelope peaks 88S1, 88s2 that occur either from one heart beat to another, from one patient to another, or from one recording site to another.
Then, in step (2316), the locations of the envelope peaks 88S1, 88s2 associated with the corresponding SI and S2 heart sounds are validated using a normalized acoustic envelope waveform 84, Es[], i.e. normalized to a range of 0 and 1, and the associated local quadratic models 90.1, 90.2 thereof, in accordance with a minimum-time-spacing criteria used to remove or ignore spurious transient peaks unrelated to heart sounds, similar to equation 10 above that is associated with step (2904) of the above-described peak validation process 2900 used to validate the electrographic envelope waveform 80, Fs[] .
Then, in step (2318), the acoustic envelope waveform 84, Es[] is searched about the associated indices ksi PEAK, ks2j>EAK respectively associated with the corresponding respective envelope peaks 88S1, 88s2 to find adjacent data points therein, i.e. associated indices ksi-, ksi+, ks2-, ks2+ for which the corresponding values of the acoustic envelope waveform 84, Es(ksi-), Es(ksi+), Es(ks2-), Es(ks2+) are each about 5 percent down, i.e. 95 percent of, the corresponding values Es(ksi_PEAK), Es(ks2_PEAK) of the associated envelope peaks 88S1, 88s2
Then, in step (2320), a respective local quadratic models ESl(k), 90.1’ and ES2(k), 90.2’ is fitted - for example, by least-squares approximation— to the three points associated with each of the corresponding respective envelope peaks 88S1, 88s2 as follows:
ES l(k) =Quadratic Fit ( {ksi-. Es(ksi-)}, {ksi_PEAK, Es(ksi)}, {ksi+, Es(ksi+)} ) (l2a)
ES2(k) =Quadratic Fit ( {ks2-, Es(ks2-)}, {ks2_PEAK, Es(ks2)}, {ks2+, Es(ks2+)} ) (l2b)
Then, referring to FIG. 34, in step (2322), the respective pairs of roots (ksi START, ksi ENo) and (ks2_START, ks2_END) of the local quadratic models ESl(k), 90.1’ and ES2(k), 90.2’ are solved, such that ESl(ksi sTART) = ESl(ksi END) = 0 and ESl(ks2_START) = ESl(ks2_END) = 0, wherein ksi_START < ksi_PEAK< ksi_END and ks2_START < ks2_PEAK < ks2_END.
If, in step (2324), all heart cycles 82 in the high-pass-filtered breath-held sampled auscultatory sound signal 72 have not been processed, then the heart-phase identification process 2300 is repeated beginning with step (2308). Otherwise, from step (2326), if all auscultatory sound sensors 12 have not been processed, then the heart-phase identification process 2300 is repeated beginning with step (2304). Otherwise, from step (2326), in step (2328), the heart-phase identification process 2300 returns either the mean values ksi, ks2 of the corresponding root locations {ksi START, ksi END}, {ks2_START, ks2_END } associated with the corresponding SI and S2 heart sounds, or the corresponding mean values tsi, ts2 of the associated times, i.e. {t(ksi_sT\RT), t(ksi_END)}, { t(ks2_START), t(ks2-END)},for each of the heart cycles 82 in each of the high-pass-filtered breath-held sampled auscultatory sound signals 72 of each of the associated auscultatory sound sensors 12.
Referring to FIG. 35, segmented high-pass-filtered breath-held sampled auscultatory sound data 72, s[] from each of the heart cycles 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9 may be synchronized with respect to the mean time ts2 associated with the S2 heart sound of each heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9, or alternatively, may be synchronized with respect to the mean time tsi associated with the SI heart sound of each heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9, wherein the region of diastole extends from the time associated with the second root ks2_END , i.e. t(ks2_END), associated with the S2 heart sound and the end of the associated heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9.
Referring again to FIG. 21, beginning with step (2108), for each heart cycle 82, and for each auscultatory sound sensor 12, in step (2110), each region of diastole - i.e. either the breath-held auscultatory sound signal 16.1, or the corresponding high-pass-filtered breath-held sampled auscultatory sound signal 72, extending between samples is2[ksEG, kBEAT] and isifksEG, kBEAi 1], wherein ksEG is a breath-held segment counter, and TUFA r is a heart-cycle counter within the breath-held segment, and is2[ksEG, k/iEA // corresponds to the ks2_END for each heart cycle 82— is checked for outliers, e.g. random spikes, using a beat outlier detection process 3600, which, referring to FIG. 36, in steps (3606) through (3618) provides for repetitively calculating, in step (3614), a standard deviation D[] of the sample values in a plurality of windows of NWINDOW samples, each window shifted by one sample with respect to the next, resulting in a standard deviation array D[] containing KSTDDEV standard deviation values. For example, in one set of embodiments, NWINDOW is equal to 128. Then, in step (3620), the standard deviation array D[] is rank ordered, and the median value thereof, medianStd, is used in step (3622) to calculate a standard deviation compactness metric STDDEVCM, as follows:
Figure imgf000039_0001
Then, in step (3624), if the standard deviation compactness metric STDDEVCM exceeds a threshold, for example, in one set of embodiments, equal to 6, but generally between 1 and 10, the particular region of diastole for the particular breath-held segment from the particular auscultatory sound sensor 12 designated as m, is flagged as an outlier. The process of steps (3606) through (3630) is repeated for each heart cycle in the breath-held segment.
Referring again to FIG. 21, then, returning to step (2112) of the auscultatory sound signal screening process 2100, if an outlier was detected in step (3624) and flagged in step (3626) of the beat outlier detection process 3600, then in step (2120), if the end of the breath-held segment has not been reached, the auscultatory sound signal preprocessing and screening process 2100 repeats beginning with step (2108) for the next heart cycle 82.
Otherwise, from step (2112), referring to FIGS. 17-20, the above-described noise detection process 1700 may be called from step (2114) to provide for determining whether or not a particular region of diastole for a particular heart cycle 82 of either the breath-held auscultatory sound signal 16.1, or the corresponding high-pass-filtered breath-held sampled auscultatory sound signal 72, is corrupted by excessive noise, and if so, provides for flagging that region of diastole as being excessively noisy so as to be prospectively excluded from subsequent processing, wherein above references to breath-held auscultatory sound signal 16.1 in the description of the associated noise detection process 1700 should be interpreted as referring to the corresponding region of diastole for a particular heart cycle 82 of either the breath-held auscultatory sound signal 16.1, or the corresponding high-pass-filtered breath-held sampled auscultatory sound signal 72, and references to the breath-held segment k in the description of the associated noise detection process 1700 should be interpreted as referring to a corresponding particular heart cycle 82 associated with the particular region of diastole.
Then, returning to the step (2116) of the auscultatory sound signal screening process 2100, if a noise threshold was exceeded, in step (2120), if the end of the breath-held segment has not been reached, the process repeats beginning with step (2108) for the next heart cycle. Otherwise, from step (2116), the good beat counter GB is incremented in step (2118) before continuing with step (2120) and proceeding therefrom as described hereinabove. Otherwise, from step (2120) if the end of the breath-held segment has been reached, in step (2122), if a threshold number of valid heart cycles has not been recorded, the process repeats with step (2104). Otherwise, the recording process ends with step (2124).
Breath holding interval is segmented into individual heart beats and the diastolic interval is analysed with respect to noise level. Quality control of the recorded signals is required in order detect weak signals that may indicate health problems but can be blocked by strong noise or unwanted interference. The present method is developed for quantitative control of signal quality and can be deployed on a recording unit for real-time quality monitoring or can be used at the post recording stage to extract only low-noise heart beats that satisfy specific quality condition. Practical implementation of the beat selection algorithm is aimed at collecting heart beats that have diastolic noise level below the specific threshold is outlined below. Referring to FIG, 37, the beat selection algorithm proceeds through a number of steps. It is assumed the recording proceeds through sequential acquisition of heart sound intervals with duration between 5 and 15 sec (for example, breath holding intervals). The recorded data chunk is passed through beat segmentation procedure that identifies the beginning and the end of each heart beats using synchronized ECG recording or another signal processing code that identifies timing of the first (Sl) and second (S2) heart sounds. The algorithm input parameters include the noise threshold mean power level and the required number of high quality heart beats. Following the segmentation procedure, a two-dimensional array of heart beats is created. The segmentation code also identifies timing of diastolic interval of each heart beat. In the next step, heart beats are normalized with respect to absolute value of the S2 peak and the mean noise power is computed within the time window Tw. The time-window is sliding along the diastole with the 50% overlap to compute array of localized signal power. Then the maximum power for the diastole is determined and compared against the selected threshold, for instance - 20 dB. Details of signal noise analysis are shown in FIG. 38. If noise level in one of the windows is greater then Po then it may indicate excessive noise or presence of a large amplitude transient outlier. If diastolic signal power exceeds the threshold that it is labeled as nosy beat and is not counted in the overall tally of heart beats. The algorithm exits when number of low-noise heart beats reaches Nmax. If this number beats can not be reached within reasonable time, then the system informs user that recording is noisy and additional actions should be performed to improve signal quality.
When sufficient number of low-noise heart beats acquired, data passed through additional preprocessing steps to emphasize acoustic signals in diastole and extract signal specific features that can be used for disease classification. Since we are looking for acoustic signature that is present in majority heart beats and coherent between beats, it is important to synchronize beats in the segmented stack. This is accomplished by finding peak of S2 and alignment of the beats with respect to the mean position of S2 peak. FIGS. 39a and 39b respectively show the signal stack before and after S2 alignment.
The next preprocessing step deals with the variable heart beat rate and as a result variable length of diastolic intervals. The method of signal processing that presents data in uniform way on the same time scale is developed to synchronize diastolic features of the time-variable heart beats. The heart beat rate is always changing and never remains the same during recording time of several minutes. It creates serious problems when specific signal features should be identified in diastole, for example using cross-correlation method. Heart beat segmentation allows one to align heart beat starting moments but due to difference in heart beat rate those features appear shifted and out of sync with respect to each other. However, such offset can be removed if the signals transformed to common normalized time scale t/T*, where T* is the fixed time interval that can be related to duration of the slowest beat. Then the time normalization can be performed using beat resampling and interpolation original signal to the new sampling rate. FIGS. 40a and 40b respectively show stack of heart beats before and after the time normalization.
The final stage of signal pre-processing is needed to extract acoustic features from the recorded heart beats. There are numerous options that can be applied to feature extraction which typically involves certain transformation of raw data to low-dimensional or sparse representation that uniquely characterizes the given recording. Alternatively, the raw data can be transformed in a set of images and some image recognition algorithm like convolutional neural net can be employed for automatic feature selection. The current data processing algorithm relies on cross-correlation (CC) of multiple heart beats to identify high-pitch signal in the diastolic phase of heart beat that occurs in multiple beats and can be linked to blood turbulent flow. The schematic diagram of cross-correlation is shown in FIG. 41. For this purpose, the 2D stack of heart beats is high-pass filtered and then each pair of signals is processed to compute cross-correlation function Rxixj. This computation is accomplished by segmenting each signal using sliding short-time window with Nw samples (typically 128) which is advancing one sample per each iteration of CC computation. Cross-correlation is computed without the time lag, resulting in an array of the same size as the original signals that can be expressed by the following equation:
Figure imgf000042_0001
and cross-correlation assigned to each beat is an average of remaining beats Nb - 1 beats:
Figure imgf000042_0002
where xi and xj are the diastolic sounds of two heart beats and Nb is the total number of heart beats. Following computation of all possible pairs of heart beats, the cross-correlation matrix Nb x Nt is obtained and displayed as a 2D image. The similar signal pattern that is present in majority of heart beats will produce a localized cross-correlation peak in the diastole. Since micro-bruit signal is expected to occur approximately at the same fraction of diastole interval, the all cross-correlation peaks produce distinct bands across the image illustrated in FIG. 42. Ultimately, the cross-correlation operation will emphasize signal features that are coherent within the current time window and suppress uncorrelated noise increasing the signal-to- noise ratio.
Alternatively, acoustic features in diastolic interval of heart beat can be visualized using continuous wavelet transform (CWT). The wavelet transform processing is similar to the short-time cross-correlation but instead of correlating two similar signals the signal of interest is correlated using a wavelet function with limited support to facilitate temporal selectivity.
Figure imgf000042_0003
where mother wavelet y is a continuous function in time and frequency domains. The wavelet family is typically chosen empirically using specifics of the signal under consideration. The family of Morlet wavelets appears as an appropriate choice for analysis of heart sounds. Output of the wavelet transform is a two-dimensional time-frequency representation of signal power |X(a,b)|2 defined in terms of scaling and shift parameters. Example of a wavelet transform is illustrated in FIG. 43, where color map represents distribution of the signal power in time-frequency plane. For the purpose of signal classification, the wavelet representation can be reduced further by dividing the time axis into discrete intervals and computing overall signal power within such interval and specific frequency bandwidth. For example, in one embodiment, the wavelet image may be subdivided in time intervals of 200 ms and two frequency bands 10 Hz - 40 Hz and 40 Hz - 100 Hz. The output vector of the signal power can be use as an input for a neural network classifier.
To classify the patient acoustic recordings, artificial neural network can be used. Referring to FIG. 44, to automate acoustic feature extraction, in one set of embodiments, a convolutional neural network is employed to analyze cross-correlation images of acoustic signals. The network input consists of the three-dimensional array of cross-correlation data recorded at the patient left or right positions and combined into a single array representing three individual channels. The CNN is built of three convolutional layers that use a 5x5 kernel array to scan input data to build a structure of acoustic features with increasing complexity. Neuron activation uses ReLU nonlinearity to produce output data. Final stage of the network classifier is a fully connected neural net with an additional clinical information (age, gender, blood pressure, etc.) merged with the acoustic features identified by CNN. The network output is a two-node layer implementing binary classification via softmax function applied to the incoming data. The CNN classifier is trained using 80% of all available data with binary CAD labels (Yes = 1 and No = 0). The remaining 20% of data is randomly selected to test the classifier performance. Reported performance metrics include prediction accuracy, sensitivity, specificity, negative prediction value and positive prediction value. New patient data are classified by passing through the same pre-processing stages and feeding the computed cross-correlation image to CNN classifier.
The system can present various views of the acoustic data (both unprocessed and processed) that was captured during the test for review by a clinician. By reviewing different visualizations of the test results, the clinician may strengthen his case for a particular diagnosis, either in agreement with or disagreement with the result produced by the system. Additionally, particular visualizations may help reassure the patient that the diagnosis is the correct one.
Referring to FIG. 45, the system presents heart/arteries view comprising a graphical representation of the heart and coronary artery tree, highlighting the data in accordance with a Textual View. By selecting any of the occlusions, the clinician is presented with the option to highlight the blockage in either: a Stacked Heartbeat View; a Bruit Identification View (either mode); a Bruit Analysis View; or a Receiver Operating Characteristic Curve. For example, FIG. 45 illustrates test results showing severity of obstructions and zonal location of each obstruction within coronary artery tree. The tree diagram also indicates which arteries are critical, and which are not, with associated color coding in accordance with the amount of occlusion, for example, with the illustrated region of 50 occlusion highlighted in a reddish hue, and the region of 30% occlusion highlighted in a yellowish hue.
In accordance with the Textual View, the system presents, in a textual manner, the system’s interpretation of the acoustic data, including: whether or not the patient has a clinical level of CAD; the count of obstructions detected, for each obstruction: the location (zone) of the obstruction; the percentage occlusion of the obstruction; or the type of blockage (soft plaque, hard plaque). For each of the items in the view listed above, the system provides for presenting an associated confidence level, which indicates how confident the system is of each specific element of information presented, or, based on the patient’s demographic data (age, sex, BMI, medical and family history), the percentage of other patients who have a similar count, severity, and position of obstructions. From this view, the clinician may switch to any other view listed in this document for more information.
Referring to FIG. 46a, the system presents a Receiver Operating Characteristic
(ROC) curve to highlight the system’s CAD detection algorithm sensitivity as a function of its false positive rate (l-specificity). The graph’s attributes will be as is standard for ROC curves - vertical axis representing sensitivity, horizontal axis representing the false positive rate, with a 45-degree dotted line representing a“coin flip” test (AUC = 0.5).
On this graph, the system will plot the ROC curve and calculate the Area Under the
Curve (AUC) based on the patient’s demographics, and the system’s clinical trial results. The coordinate that corresponds to the current positivity criterion will be highlighted. The clinician will be able to display a modified graph if he commands the system to exclude specific parts of the patient’s demographic data. The graph may or may not contain gridlines and/or data points defining the ROC curve, and it may or may not fill in the AUC.
Alternatively, referring to FIG. 46b, the data may be visualized as a line graph (with or without underfill), where true positives, true negatives, false negatives, and false positives are displayed, with a line indicating the currently -used cutoff value.
Referring to FIGS. 47 through 53, the system provides for visualizing the acoustic data resulting from an analysis of the breath-held auscultatory sound signals 16.1 from the auscultatory sound sensors 12.
Referring to FIG. 47, the system presents a heartbeat view comprising a graphical plot of the systolic and diastolic intervals of each heartbeat captured. The horizontal axis of the graph represents captured heartbeats. This representation is a subset of the acoustic capture time, as some acoustic data acquired is discarded (poor quality, etc.) during the system’s analysis process. The duration of the Sl and S2 sounds are also highlighted on this axis.
Generally the vertical axis may comprise acoustic data captured from each of the system’s sensors - both acoustic and ECG. A correlation procedure is performed to ensure that the data captured from each of the system’s sensors is aligned to one another. Because the initial display of this view may contain many dozens of heartbeats, the clinician has the option to highlight a subset of the data on the horizontal axis, and command the system to zoom into the selected section. In this way, the clinician can perform a deeper analysis on one or more sections of the acoustic capture data as he so chooses, and explore any discrepancies between the data captured by the ECG and acoustic sensors for any particular heartbeat. For example, FIG. 47 illustrates an example of the Heartbeat View wherein the acoustic view has been limited by the clinician to Sensor#3 only, and zoomed in to see only 2 heartbeats, with the ECG data also displayed.
Referring to FIG. 48, in accordance with a Stacked Heartbeat View, the system presents a graphical plot of the systolic and diastolic intervals of each heartbeat captured. The horizontal axis of the graph represents time (in seconds or milliseconds) from the beginning of the systolic interval to the end of the diastolic interval. The duration of the Sl and S2 sounds are highlighted on this axis. The vertical axis is comprised of the acoustic data captured for each of the heartbeats, in either ascending or descending order of capture, where each heartbeat is itself a graph, with a vertical axis representing intensity. A correlation procedure is performed to ensure that the start of the systolic interval for each heartbeat is aligned on x-axis=0. For each heartbeat, the system highlights any unexpected acoustic signals captured, as such signals may be an indication of an obstruction or other cardiac condition. For example, in FIG. 48, unexpected acoustic signals are highlighted in red. The clinician has the option to highlight a subset of the data on the horizontal axis, and command the system to zoom into the selected section. In this way, the clinician can perform a deeper analysis on one or more sections of the acoustic capture data as he so chooses, especially so that he may explore more deeply any unexpected acoustic signals.
Referring to FIG. 49, in accordance with a Bruit Identification View in a Line Graphs with Underfill mode, the system presents a graphical plot of unexpected acoustic signals captured during the diastolic interval by means of a line graph with underfill. The horizontal axis of the graph represents time (in seconds or milliseconds) from the beginning of the diastolic interval to the end of the diastolic interval. The duration of the S2 sound is also highlighted on this axis. The vertical axis is comprised of the acoustic data captured for each of the heartbeats, in either ascending or descending order of capture, where each heartbeat is itself a graph, with a vertical axis representing intensity. A correlation procedure is performed to ensure that the start of the diastolic interval for each heartbeat is aligned on x- axis=0. An underfill is used to visually highlight deviation from the baseline.
For each heartbeat diastole displayed, the system highlights any unexpected acoustic signals captured, as such signals may be an indication of an obstruction or other cardiac condition. Such signals are highlighted by the height of the line graph, which represents intensity. These highlighted areas on the graph allow a clinician to distinguish low-energy noise (which may or may not be a sign of non-obstructive CAD) from high-energy noise (which is a likely indicator of obstructive CAD). In this view, it is also easy for a clinician to understand the correlation (across heartbeats) of noise, as well as the number and timing of correlated instances of noise (which may indicate the number and location of blockages, respectively). For example, the data illustrated in FIG. 49 exhibits relatively high intensity noise in many heartbeats at approximately t=0.05 seconds into diastole.
The clinician has the option to highlight a subset of the data on the horizontal axis, and command the system to zoom into the selected section. In this way, the clinician can perform a deeper analysis on one or more sections of the acoustic capture data as he so chooses, especially so that he may explore more deeply any unexpected acoustic signals.
Referring to FIG. 50, in accordance with a Bruit Identification View in a Spectrogram mode, the system presents a graphical plot of unexpected acoustic signals captured during the diastolic interval by means of a spectrogram. The horizontal axis of the graph represents time (in seconds or milliseconds) from the beginning of the diastolic interval to the end of the diastolic interval. The duration of the S2 sound is also highlighted on this axis. The vertical axis is comprised of the acoustic data captured for each of the heartbeats, in either ascending or descending order of capture. A correlation procedure is performed to ensure that the start of the diastolic interval for each heartbeat is aligned on x-axis=0.
For each diastole displayed, the system highlights any unexpected acoustic signals captured, as such signals may be an indication of an obstruction or other cardiac condition. Such highlighted areas indicate the intensity of the unexpected signal. Highlights are in the form of color, which indicate varying intensity of the signal. This view could alternatively be represented in monochrome as a contour plot. For example, the data illustrated in FIG. 50 exhibits relatively high intensity noise in many heartbeats at approximately t=0.05 seconds into diastole.
These highlighted areas on the graph allow a clinician to distinguish low-energy noise
(which may or may not be a sign of non-obstructive coronary artery disease (CAD() from high-energy noise (which is a likely indicator of obstructive CAD). In this view, it is also easy for a clinician to understand the correlation (across heartbeats) of noise, as well as the number and timing of correlated instances of noise (which may indicate the number and location of blockages, respectively).
The clinician has the option to highlight a subset of the data on the horizontal axis, and command the system to zoom into the selected section. In this way, the clinician can perform a deeper analysis on one or more sections of the acoustic capture data as he so chooses, especially so that he may explore more deeply any unexpected acoustic signals.
Referring to FIG. 51, in accordance with a Bruit Analysis View, the system presents a graphical time/frequency plot of unexpected acoustic signals captured. The horizontal axis of the graph represents time (in seconds or milliseconds) from the beginning of the systolic interval to the end of the diastolic interval. The duration of the Sl and S2 sounds are highlighted on this axis. The vertical axis represents the frequency (in Hz or kHz) of any unexpected acoustic signals, averaged from all captured data. For each unexpected acoustic signal captured, the color on the graph represents the intensity of that signal. Alternatively, the graph could be represented in monochrome as a contour plot.
These highlighted areas on the graph allow a clinician to distinguish low-energy noise (which may or may not be a sign of non-obstructive CAD) from high-energy noise (which is a likely indicator of obstructive CAD). It is also easy for a clinician to understand the frequency of high-energy noise, as well as the timing of this noise - this is important as different cardiac conditions may be defined by specific frequencies and timings of noise. For example, the data illustrated in FIG. 51 exhibits relatively high intensity noises at approximately t=0.25, 0.75, and 0.8 seconds into the heartbeat. The system provides a User Interface, and associated navigation, is designed for use on tablets and smartphones, and thus uses common touch-screen user interface paradigms. For example, two fingers moving apart from one another can be used to zoom in to any area on any graph, and two fingers moving together can be used to zoom out. A single finger can be used to highlight any areas on the horizontal axis, and the graph can be zoomed in to that highlighted area by touching a button.
Touching any area of the graph provides information to the user (either in a pop-up window or beside/below the graph) on the values of the horizontal and vertical axes at that point, as well as the“height” information of that point if available (e.g. in the Matrix View). For example, in Stacked Heartbeat View, touching on an individual heartbeat would cause the system to provide the heartbeat number, the maximum intensity of that heartbeat (and the time on the horizontal axis at which the maximum intensity occurs), and the time on the horizontal axis corresponding to the touch. In the case of the Matrix View, touching on any area of the graph would cause the system to provide the frequency, time, and intensity corresponding to the coordinate on the graph that was touched.
For use with a mouse, the user interface paradigms are similar, except with zoom. In this case, zoom can be accomplished through a common desktop/laptop user interface paradigm, such as dedicated zoom in/out buttons in the UI, or mouse wheel scrolling.
For some of these graphs, it is possible to restrict the display of data to some subset of the acoustic sensors (for example, as illustrated in FIG. 47), to combine acoustic data from all sensors (the default mode), or to display data from each sensor individually. This may be helpful to clinicians who want to spatially localize any unexpected acoustic signals. This capability is relevant to the Stacked Heartbeat View, the Bruit Identification View (both modes), and the Bruit Analysis View
For some of these graphs, it is possible for the user to directly compare the results of the current test with one or more previous tests. This can be done by simply placing the graphs side-by-side (for example, as illustrated in FIGS. 52 and 53), or by overlaying them on-top of one other, with some variable transparency or color variation so that the clinician can distinguish data from one test versus another. In the case where the graphs are placed on top of one another, the system could fade in to one graph and out from the other slowly, repeatedly in a loop (almost like a video file), so that the clinician can easily see the differences in the processed data between one test and the other. Alternatively, if the graphs are placed on top of one another, a subtraction operation could be performed to highlight only the differences between the two graphs (e.g. red for increase in noise from previous test result, green for decrease). For example, FIG. 52 illustrates a comparison of a current test with a previous test, using the Bruit Identification View, Spectrogram mode, to confirm success of PCI. Similarly, as another example, FIG. 532 illustrates a comparison of a current test with a previous test, using the Bruit Identification View, Line Graph with Underfill mode, to confirm success of PCI.
The ability to compare the current test with a previous test is critical to the clinicians understanding of the progression of a particular cardiovascular condition (either worsening, or improved as in the case of a PCI procedure). This capability is relevant to the Stacked Heartbeat View, the Bruit Identification View (both modes), and the Bruit Analysis View.
The graphs may be rendered locally or remotely (server-based) or both, depending on the capabilities desired by the clinician and the organization to which he belongs. In most use cases (tablet, phone, desktop, or laptop), the graph rendering will be done locally, either through a web-browser (full or embedded) on the client, or through a graphics library optimized for each specific supported client platform.
In other cases, the rendering may be done on the server side - graphs may be generated and exported to JPEG (or other similar) format so that they can be emailed or sent via instant message to interested parties.
While specific embodiments have been described in detail in the foregoing detailed description and illustrated in the accompanying drawings, those with ordinary skill in the art will appreciate that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. It should be understood, that any reference herein to the term“or” is intended to mean an“inclusive or” or what is also known as a “logical OR”, wherein when used as a logic statement, the expression“A or B” is true if either A or B is true, or if both A and B are true, and when used as a list of elements, the expression“A, B or C” is intended to include all combinations of the elements recited in the expression, for example, any of the elements selected from the group consisting of A, B, C, (A, B), (A, C), (B, C), and (A, B, C); and so on if additional elements are listed. Furthermore, it should also be understood that the indefinite articles "a" or "an", and the corresponding associated definite articles“the’ or“said”, are each intended to mean one or more unless otherwise stated, implied, or physically impossible. Yet further, it should be understood that the expressions“at least one of A and B, etc.”,“at least one of A or B, etc.”, “selected from A and B, etc.” and“selected from A or B, etc.” are each intended to mean either any recited element individually or any combination of two or more elements, for example, any of the elements from the group consisting of “A”,“B”, and“A AND B together”, etc.. Yet further, it should be understood that the expressions“one of A and B, etc.” and“one of A or B, etc.” are each intended to mean any of the recited elements individually alone, for example, either A alone or B alone, etc., but not A AND B together. Furthermore, it should also be understood that unless indicated otherwise or unless physically impossible, that the above-described embodiments and aspects can be used in combination with one another and are not mutually exclusive. Accordingly, the particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of the invention, which is to be given the full breadth of any claims that are supportable by the specification and drawings, and any and all equivalents thereof.
What is claimed is:

Claims

1. A method of segmenting an auscultatory sound signal, comprising:
a. receiving an electrographic signal from an ECG sensor;
b. generating an electrographic envelope signal representing an envelope responsive to an even power of said electrographic signal;
c. locating a plurality of peaks of said electrographic envelope signal corresponding to a corresponding plurality of R-peaks of said electrographic signal;
d. receiving at least one auscultatory sound signal from a corresponding at least one auscultatory sound-or-vibration sensor;
e. segmenting said at least one auscultatory sound signal into a plurality of heart cycle segments responsive to said plurality of peaks of said electrographic envelope signal;
f. for at least one heart cycle of said plurality of heart cycles:
i. generating an auscultatory envelope signal representing an envelope responsive to an even power of said auscultatory sound signal within said at least one heart cycle;
ii. locating first and second peaks of said auscultatory envelope signal
corresponding to respective first and second heart sounds;
iii. generating a local mathematical model of at least said second peak of said
auscultatory envelope signal; and
iv. locating first and second roots of said mathematical model so as to provide for determining an associate start of diastole of said heart cycle.
PCT/US2018/056956 2017-10-21 2018-10-22 Method of preprocessing and screening auscultatory sound signals WO2019079829A1 (en)

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US16/854,894 US11284827B2 (en) 2017-10-21 2020-04-21 Medical decision support system
US17/509,018 US20220061797A1 (en) 2017-10-21 2021-10-24 Medical decision support system
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US5109863A (en) * 1989-10-26 1992-05-05 Rutgers, The State University Of New Jersey Noninvasive diagnostic system for coronary artery disease
US6050950A (en) 1996-12-18 2000-04-18 Aurora Holdings, Llc Passive/non-invasive systemic and pulmonary blood pressure measurement
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