US20080243017A1 - Breathing sound analysis for estimation of airlow rate - Google Patents

Breathing sound analysis for estimation of airlow rate Download PDF

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US20080243017A1
US20080243017A1 US11/692,745 US69274507A US2008243017A1 US 20080243017 A1 US20080243017 A1 US 20080243017A1 US 69274507 A US69274507 A US 69274507A US 2008243017 A1 US2008243017 A1 US 2008243017A1
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sounds
breathing
patient
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signals
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Zahra Moussavi
Azadeh Yadollahi
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TR TECHNOLOGIES Inc
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Priority to PCT/CA2008/000581 priority patent/WO2008116318A1/fr
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/003—Detecting lung or respiration noise
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/087—Measuring breath flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
    • A—HUMAN NECESSITIES
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    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6822—Neck
    • A—HUMAN NECESSITIES
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    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
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    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
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    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period

Definitions

  • This invention relates to an apparatus for use in breathing sound analysis for estimation of airflow rate.
  • Airflow estimation has drawn much attention in recent years due to difficulties in airflow measurement.
  • flow is usually measured by spirometry devices, such as pneumotachograph, nasal cannulae connected to a pressure transducer, heated thermistor or anemometry.
  • Airflow is also measured by indirect means, i.e., detection of chest and/or abdominal movements using respiratory inductance plethysmography (RIP), strain gauges, or magnetometers.
  • RIP respiratory inductance plethysmography
  • strain gauges or magnetometers.
  • the most reliable measurement of airflow is achieved by a mouth piece or facemask connected to a pneumotachograph. However, this device cannot be used during the swallowing assessment.
  • nasal cannulae connected to a pressure transducer and the measurement of respiratory inductance plethomogoraphy to monitor volume changes has been recommended as the best approach in recording flow to assess respiratory and swallowing patterns.
  • application of these techniques has some disadvantages, especially when studying young children or patients with neurological impairments, where the study of swallowing is clinically important.
  • nasal cannulae may seem a minor intrusion, it can produce agitation in children and patients with neurological impairment.
  • applying the RIP devices is difficult in children with neurological impairment as their poor postural control and physical deformities can make it challenging to ensure stable positioning.
  • the tracheal sound envelope was investigated for flow estimation.
  • the tracheal sound was band-pass filtered in the range of 200-1000 Hz and then a Hilbert transform was applied to the filtered signal.
  • the transformed signal was used to calculate the tracheal sound envelope and to estimate the flow from the calculated envelope by a linear model.
  • the estimated flow was then used to measure ventilation, but the flow estimation error was not reported.
  • the flow rate in that study was constant at tidal flow and half of the recorded flow signal was used to calibrate the model.
  • U.S. Pat. No. 6,241,683 (Macklem) issued Jun. 5 th 2001 discloses a method for estimating air flow from breathing sounds where the system determines times when sounds are too low to make an accurate determination and uses an interpolation method to fill in the information in these times. Such an arrangement is of course of no value in detecting apnea or hypopnea since it accepts that the information in these times is inaccurate.
  • an apparatus comprising:
  • a microphone arranged to be located on the patient for detecting breathing sounds
  • a detector module for receiving and analyzing the signals to extract data relating to the breathing
  • the detector module being arranged to analyze the signals to generate an estimate of air flow
  • the detector module is arranged to calculate a function representing the range of the signal or the entropy of the signal providing an estimate of air flow during breathing.
  • the detector module is arranged to cancel heart sounds from the function.
  • the function is the range of the signal which is defined as the log of the difference between minimum and maximum of the signal within each short window (i.e. 100 ms) of data.
  • the function is the entropy of the signal which is defined by the following formula:
  • p i is the probability distribution function of the i th event.
  • the extraneous sounds are removed by the detector module prior to flow estimation.
  • the display is arranged to display the estimated air flow versus time in any desired time length being chosen by the user.
  • the display is capable of zoom-in and zoom-out functions in the same window.
  • the display is capable of playing the breathing sounds in any data window.
  • the microphone is arranged to be located in the ear of the patient.
  • the microphone in the ear includes a transmitter arranged for wireless transmission to a receiver.
  • the apparatus is arranged such that inspiration and expiration are monitored by an initial calibration wherein the patient is instructed to initialize the system by taking a deep breath, hold it, start up of the monitoring system, then exhale and continue breathing.
  • an estimate of flow rate is calibrated using a look-up table of previously measured flow-sound relationship data that is sorted based on characteristics of the subjects.
  • the characteristics in the look-up table include BMI, gender, height, neck circumference, and smoking history of the subject.
  • the detector module is arranged to cancel heart sounds.
  • an apparatus for use in use in analysis of breathing of a patient during sleep comprising:
  • a microphone arranged to be located on the patient for detecting breathing sounds
  • a detector module for receiving and analyzing the signals to extract data relating to the breathing
  • the detector module being arranged to analyze the signals to generate an estimate of air flow during inspiration and expiration;
  • the apparatus is arranged such that inspiration and expiration are monitored by an initial calibration wherein the patient is instructed to initialize the system by taking a deep breath, hold it, start up of the monitoring system, then exhale and continue breathing.
  • an apparatus for use in use in analysis of breathing of a patient during sleep comprising:
  • a microphone arranged to be located on the patient for detecting breathing sounds
  • a detector module for receiving and analyzing the signals to extract data relating to the breathing
  • the detector module being arranged to analyze the signals to generate an estimate of air flow during inspiration and expiration;
  • an estimate of flow rate is calibrated using a look-up table of previously measured flow-sound relationship data that is sorted based on characteristics of the subjects.
  • This proposal aims to develop a prototype of an integrated system to acquire, de-noise, analyze the tracheal respiratory sounds, estimate airflow acoustically.
  • FIG. 1 is a schematic illustration of a sleep apnea detection apparatus according to the present invention.
  • FIG. 2 is an illustration of a typical screen displaying the data to the physician.
  • FIG. 3( a ) is a graphical representation of Tracheal sound entropy
  • FIG. 3( b ) is a graphical representation of entropy after applying nonlinear median filter (star marks represents the estimated apnea segments)
  • FIG. 3( c ) is a graphical representation of flow signal (solid line) along with the estimated (dotted line) and real (dashed line) apnea segments for a typical subject.
  • FIG. 4 is a graphical representation of Mean and standard deviation values of errors in estimating apnea periods for different subjects
  • FIG. 5 is a block diagram illustrating the adaptive filtering scheme for removing the snoring sounds from the signal using the signal recorded by the auxiliary microphone in the vicinity of the patient.
  • nasal cannulae are used to measure airflow; however, when the patient breathes through the mouth, the nasal cannulae register nothing and hence give a false positive detection error for apnea. Therefore, combination of nasal pressure plus thermistor and End-tidal carbon dioxide concentration in the expired air (ETCO 2 ) is used to have a qualitative measure of respiratory airflow.
  • the abdominal movement recordings are mainly used to detect respiratory effort and hence to distinguish between central and obstructive sleep apnea.
  • the ECG signals are also being used for detecting heart rate variability and another measure for apnea detection as well as monitoring patient's heart condition during the night.
  • EOG Electrooculogram
  • EEG Electroencephalogram
  • EMG Electromyogram
  • REM rapid eye movement
  • Recording these signals are necessary if insight in sleep quality is saught for diagnosis of certain sleep disorders.
  • the most important information that doctors seek from a complete sleep study is the duration and frequency of apnea and/or hypopnea and the blood's Oxygen saturation (SaO 2 ) level of the patient during the apnea. Oxygen level usually drops during the apnea and will rise quickly with awakenings. However, oximetry alone does not detect all cases of sleep apnea.
  • the present arrangement provides a fully automated system to detect apnea with only one single sensor that can also easily be applied by the patient at home and detect apnea acoustically; hence reducing the need for a complete laboratory sleep study.
  • the apparatus provides an integrated system for remote and local monitoring and assessment of sleep apnea as a diagnostic aid for physicians and allows the following:
  • FIG. 1 shows the apparatus for sleep apnea detection that can also be used as a home-care device while being connected to a clinical diagnostic center for online monitoring.
  • the apparatus consists of six modules that permit sleep apnea detection diagnosis.
  • the clinical diagnosis can be performed either locally (e.g. at a clinical diagnostic center) and/or remotely (e.g. at clinician's office/home).
  • the apparatus will support several clinicians simultaneously carrying out clinical work on different patients.
  • patients can be monitored either locally (e.g. at a clinical diagnostic center) and/or remotely (e.g. at patient's home).
  • the apparatus will also support many patients being concurrently monitored.
  • the apparatus has the following modules
  • Collector module 10 captures physiological signals from different body parts.
  • the body parts include a microphone and transmitter 20 at the ear or over the neck by a wireless microphone mounted in chamber with a neckband for recording sounds, a sensor 22 at the fingers of the patient for recording oximetry data and an external microphone 21 for recording sound from the environment around the patient.
  • Other signals can be detected in some cases from other body parts if the physicians request other biological signals, but this is not generally intended herein.
  • the collector module locally transfers wirelessly the signals to the Transmitter module 11 .
  • Transmitter module 11 receives biological signals from the Collector module 10 , securely transmits those signals and receives the signals at the diagnostic center for its delivery to the Organizer module 12 .
  • the Transmitter module 11 consists of two components; The Transmitter Sender (S) and the Transmitter Receiver (R).
  • the Transmitter Sender together with the Collector module resides at the patient location.
  • the Transmitter Sender receives and store temporally signals from the Collector, and securely and reliably transfers the signals to the Transmitter Receiver.
  • the Transmitter Receiver resides at the diagnostic center location.
  • the Transmitter Receiver securely and reliably accepts the signals from the Transmitter Sender, and forwards the signals to the Organizer for the signal management and processing. There is one pair of collector—transmitter modules per patient being monitored.
  • Inter-Transmitter components signal transmission can occurred locally for those cases when the Collector-Transmitter Sender resides in the same center (e.g. at a diagnostic facility) or remotely for those cases when the Collector-Transmitter Sender resides externally (e.g. at a patient home).
  • the transmission can be wireless or wired (e.g. through the internet/intranet).
  • Organizer module 12 receives all captured signals from the Transmitter module, organizes and classifies received signals per patient/physician and prepares the signals for its processing by the Detector module.
  • the Organizer module simultaneously supports receiving many signals from different patients that is signals from collector—transmitter module pairs.
  • Detector module 13 pre-processes and analyzes the patient biological signals, and performs the sleep apnea detection.
  • the Detector performs snoring sound detection and separation prior to the apnea/hypopnea detection.
  • the Detector has self-calibrated acoustical respiratory airflow estimation and phase detection utilized in respiratory and sleep apnea assessments
  • Interface module 14 provides the graphical user interface to the clinicians.
  • the Interface module gives a secure, reliable, user-friendly, interactive access to the analysis performed by the Detector and it is organized per patient physician.
  • the Interface module consists of two main components: the Interface Master (M) and the Interface Client (C).
  • the Interface Master serves the information to the Interface Client(s), while the Interface Client provides the access to the clinicians, Several Interface Clients can run concurrently giving out results to several clinicians.
  • the Interface Client can be executed locally (e.g. intranet) or externally (e.g. internet).
  • Manager module 15 provides the application management functions. It provides the graphical user interface to the application administrator at the diagnostic center location.
  • All system/application parameters are setup at the Manager module.
  • the system/application parameters configure the apparatus for its proper operation.
  • the collector module microphone may comprise a neck band with a microphone mounted in a chamber placed over the supra-sternal notch.
  • the preferred arrangement as shown in FIG. 1 schematically comprises a wireless microphone inside the ear or by a microphone mounted in a chamber with a neck band to record respiratory sounds followed by a suitable signal conditioning unit depending on the type of the used sensor.
  • the second sensor 21 collects sound from the environment around.
  • the third sensor 22 collects the conventional SaO 2 data or other oximetry data. The three sensors allow from the patient simultaneous data acquisition of the sound signals and the SaO 2 data.
  • the very small miniature ear microphone is inserted into a piece of foam which has open ends and inserted to inside the microphone.
  • the small preamplifier of the microphone is placed behind the ear similar to a hearing aid device,
  • the ear microphone includes a wireless transmitter which is placed behind the ear, the miniature microphone and the foam for securing the microphone inside the ear.
  • neck microphone it is inserted in a chamber (with the size of a loony) which allows about 2 mm distance between the microphone and the skin when the chamber is placed over supra-sternal notch of the trachea of the patient with double sided adhesive ring tapes.
  • the neck microphone will come with a neck band mainly for the comfort of the patient and also to keep the wire of the microphone free of touching the skin.
  • the preamplifier and transmitter of the wireless microphone can be placed in the pocket of the subject.
  • the whole element mounted in the ear canal includes the pre-amplifier and transmitter for complete wireless operation.
  • the detector module pre-processes and analyzes the recorded signal in order to provide a user friendly, smart and interactive interface for the physician as a monitoring and diagnostic aid tool.
  • the software in this part will de-noise the recorded sound, separate snoring sounds, estimate the flow acoustically, detect apnea and/or hypopnea episodes, count the duration and the frequency of their occurrence, display the estimated flow with marked apnea episodes as shown in FIG. 3 along with the related information.
  • a Transmitter—Sender Module DSP board is designed to receive the analog signal, amplify and filter the signal, digitize it with a minimum of 5120 Hz sampling rate and store it as a binary file.
  • the SaO 2 data simultaneously with the respiratory sounds is digitized with 5120 Hz sampling rate and stored in a binary file for the entire duration of the sleep at the collector module.
  • the detector module signal processing of the sound signals has three stages. First an automated algorithm finds the artifacts (that normally appear as impulses in the signal) and removes them from further analyses. Secondly, the snoring sounds, if they exist, are identified and separated from the respiratory sounds. Finally, from the cleaned respiratory sounds the entropy of the signal is calculated, the effect of heart sounds is removed, and apnea episodes are detected by the technique as described hereinafter. The average duration of the apnea episodes, their frequency of occurrence and whether they are associated with snoring, is presented as part of the information in the GUI interface for the physician.
  • snoring sounds are musical sounds which appear with harmonic components in the spectrogram of the recorded signal. Detection of snoring sounds is similar to detection of crackle sounds in the lung sounds. Multi-scale product of the wavelet coefficients is used to detect and separate the snoring sounds. Techniques for the application of digital signal processing techniques on biological signals including noise and adventitious sounds separation are known.
  • the entropy of the signal is calculated.
  • heart sounds have overlap with respiratory sounds at low frequencies and this is more pronounced at very low flow rate (the case of hypopnea)
  • the effect of heart sounds has to be cancelled from the entropy or the range parameter of the signals prior to apnea detection. This is described in more detail hereinafter.
  • the apnea episodes are identified using Otsu's thresholding method as described hereinafter.
  • the flow estimation method as described hereinafter is enhanced to make the method self-calibrated. That enables the apparatus to estimate the actual amount of flow. Finally, the episodes of hypopnea and apnea are marked; their duration and frequency of occurrence during the entire sleep is presented on the interface module GUI display as a diagnostic aid to the physician.
  • both sounds result in the same apnea detection episodes and flow estimation while the tuning of the algorithm for each sound signal requires slight modification, i.e. the threshold or the parameters of the flow estimation model are different.
  • the apnea detection algorithm requires a snoring separation algorithm. This can use one or more of the following principles:
  • An automated algorithm can be provided to clean the recorded breath sound signal from all extra plausible noises such as cough sounds, swallowing sounds, vocal noise (in case the patient talks while dreaming), and artifacts due to movements following the apnea detection algorithm on the cleaned signal and validate the results.
  • extraneous sounds will be removed using wavelet analysis for localization and several different filter banks to remove each type of noises either automatically or at the users command.
  • the interface module 14 provides a display of the detected apnea/hypopnea episodes and related information for a clinician.
  • the display includes a display 30 of airflow versus time is plotted with apnea and hypopnea episodes marked on the screen.
  • the display includes oximetry data 31 plotted in association with the estimated airflow.
  • the display has touch screen controls 32 , 33 providing zoom-in and zoom-out functions in the same window for both airflow and oximetry data simultaneously.
  • the display is capable of playing the breathing and snoring sounds in any zoomed-in or zoomed-out data window, that is the sounds are stored to allow an actual rendition of those sounds to the clinician to study the sounds at or around an apnea event,
  • the display is capable of displaying the extracted information about the frequency and duration of apnea/hypopnea episodes, and their association with the level of oximetry data in a separate window for the clinician.
  • FIGS. 4( a ), 4 ( b ) and 4 ( c ) further detail of the Sleep Apnea detection components is now described.
  • the calculated entropy or range parameter In order to smooth the calculated entropy or range parameter, it is segmented into windows of 200 ms with 50% overlap between adjacent windows. Each window was then presented by its median value which is not sensitive to jerky fluctuation of the signal.
  • the smoothed entropy or the smoothed range signal is classified into two groups of breathing and apnea using a nonparametric and unsupervised method for automatic threshold selection using the principles of OTSU.
  • the threshold is chosen such that the variance between classes is maximized.
  • the between-class variance is defined as the sum of variances of all classes respect to the total mean value of all classes:
  • ⁇ B 2 w 0 ⁇ ( ⁇ 0 - ⁇ T ) 2 + w 1 ⁇ ( ⁇ 1 - ⁇ T ) 2 , ( 1 )
  • ⁇ B 2 ⁇ ( k * ) max 1 ⁇ k ⁇ L ⁇ ⁇ B 2 ⁇ ( k ) . ( 2 )
  • the average of entropy or range values is another statistical measure that can be used to detect apnea segments.
  • Otsu the average value of entropy or range value were used to define the classification threshold as:
  • k′′ is the Otsu threshold and m is the average of the entropy or range values.
  • FIG. 4 presents (a) Tracheal sound entropy, (b) entropy after applying nonlinear median filter (star marks represents the estimated apnea segments) and c) flow signal (solid line) along with the estimated (doffed line) and real (dashed line) apnea segments for a typical subject. Comparing the results depicted in FIG. 4( a ) and FIG. 4( b ), the effect of applying median filter is evident. The star marks in FIG. 4( b ) show the estimated apnea segments. Investigating the results depicted in FIG. 4( c ) it is clear that the proposed method detects all the apnea segments and classifies them correctly from the breath segments.
  • snoring sounds may have some problems due to the fact that snoring sounds also have strong low frequency components, in which the acoustical apnea detection is based on.
  • the snoring sounds can be recorded by another auxiliary microphone in the vicinity of the subject. This signal will not have breathing sounds and can be used as a noise reference.
  • the apparatus then uses adaptive filtering for noise (snore) cancellation.
  • Snoring sounds are musical sounds which appear with harmonic components in the spectrogram of the recorded signal.
  • adaptive filtering will cancel the snoring sounds from the breath and snoring sounds recorded over the neck or inside the ear of the patient.
  • FIG. 5 illustrates the block diagram of the adaptive filtering scheme.
  • the filter has two inputs, the primary input and the reference signal.
  • the primary input, x(t), (the microphone over the neck or inside the ear) contains an interference, m(n), (snoring sounds) along with the information bearing signal, b(n), (tracheal sound).
  • the reference input, r(n), (the auxiliary microphone) represents a version of interference with undetectable information bearing (tracheal sounds) signal.
  • the output of the RLS FIR filter, y(n) is close to the interference component of the primary signal. Therefore, the output of the adaptive filter, e(n), is the minimum mean square error estimate of the information bearing signal, ⁇ circumflex over (b) ⁇ (n).
  • the present arrangement provides a method of respiratory phase detection with only one channel breath sound (Tracheal sound signal).
  • a method of automatic self calibration using a data bank includes a very large data bank of breathing sounds (tracheal sound) of people. This data bank is sorted based on body-mass-index (BMI), age, gender, and smoking history of the subjects. This data is used to match the patient's BMI and other information to suggest the known flow-sound relationship required for calibration.
  • BMI body-mass-index
  • an algorithm is required to be run by the choice of the user (the clinician) to remove all adventitious sounds prior to flow estimation.
  • This algorithm has two parts: adventitious sound localization and removal.
  • adventitious localization the arrangement herein uses multi-scale (level 3) product of wavelet coefficients and applies a running threshold of mean plus three times of standard deviation to detect and localize the adventitious sounds. Then, the segments including artefacts will be removed in time-frequency domain, the signal will be interpolated by spline interpolation and the breath sound signal will be reconstructed in time domain by taking the inverse of the spectrogram.
  • var(x) is the variance of the signal in each segment.
  • h is the kernel bandwidth.
  • F est C 1 ⁇ ( mean ⁇ ( L ph ) mean ⁇ ( L base ) ) ⁇ L ph + C 2 , ( 6 )
  • L ph [L 1 , . . . , L w ] is a vector representing the entropy or range value of the signal in the upper 40% values of each respiratory phase (inspiration or expiration)
  • w is the number of segments in the upper 40% values of each respiratory phase
  • L i is the entropy or range values of tracheal sound in each segment (Eq. 1, 2 or 5).
  • L base is the same vector that is calculated using the base respiratory phase signal.
  • Base respiratory phase is the one breath that is assumed to be available with known flow to calibrate the model.
  • var(x) is the variance of the signal in each segment.
  • h is the kernel bandwidth.
  • ⁇ circumflex over ( ⁇ ) ⁇ (x) is the estimated standard deviation of the signal x(t) in each window.
  • tracheal sounds entropy was used to detect apnea (breath hold in the experiments of this study) without the use of the measured flow signal.
  • the recorded flow signal was used for validation of the acoustically detected apnea.
  • Tracheal sound signal was band-pass filtered in the range of [75-600] Hz, and then segmented into 50 ms (512 samples) windows with 50% overlap between the adjacent windows. In each window the tracheal sound probability density function (pdf) was estimated based on kernel methods. Then, using the method described earlier in this document Shannon entropy was calculated in each window that represents the changes in the signal's pdf. The effect of heart sounds which is most evident in the frequency range below 200 Hz was removed by the method introduced earlier in this document.
  • FIG. 3 shows the calculated entropy and its corresponding flow signal for a typical subject.
  • FIG. 3 a displays the mean and standard deviation values of length and lag errors in estimating apnea periods for different subjects.
  • tracheal sound was band-pass filtered in this range followed by segmenting the band-pass filtered signal into segments of 50 ms (512 samples) with 75% overlap between the successive segments.
  • heart sounds are the main source of interference that changes the time and frequency characteristics of the tracheal sound. Therefore, the presence of heart sounds will cause an error which can become significant in flow estimation in very shallow breathing, when most of the signal's energy is concentrated at low frequencies. Hence, in this study the effect of heart sounds on the extracted parameters was cancelled by using the same method as described above.

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