WO2016000061A1 - Système et procédé d'évaluation de volume de fluide de col acoustique - Google Patents

Système et procédé d'évaluation de volume de fluide de col acoustique Download PDF

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Publication number
WO2016000061A1
WO2016000061A1 PCT/CA2014/050627 CA2014050627W WO2016000061A1 WO 2016000061 A1 WO2016000061 A1 WO 2016000061A1 CA 2014050627 W CA2014050627 W CA 2014050627W WO 2016000061 A1 WO2016000061 A1 WO 2016000061A1
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Prior art keywords
fluid volume
subject
neck
features
acoustic
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PCT/CA2014/050627
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English (en)
Inventor
Azadeh Yadollahi
Frank RUDZICZ
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University Health Network
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • 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
    • 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
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/02028—Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0537—Measuring body composition by impedance, e.g. tissue hydration or fat content
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4818—Sleep apnoea

Definitions

  • the present disclosure relates to pharyngeal and/or airway assessment methods and systems, and, in particular, to an acoustic neck fluid volume assessment system and method.
  • OSA Obstructive sleep apnea
  • apnea is a common disorder that increases cardiovascular morbidity and mortality.
  • OSA occurs due to a partial or complete collapse of the upper airway during sleep, the underlying mechanisms of this collapse are not fully understood.
  • Fluid accumulation in the neck could cause distension of the neck veins and/or edema of the peripharyngeal soft tissue, narrow the upper airway, and facilitate its obstruction.
  • severity of OSA is strongly correlated with the amount of edema in the pharyngeal tissue.
  • diuretics and ultrafiltration could reduce OSA severity; fluid overloading could induce or worsen OSA severity. Therefore, developing convenient and non-invasive techniques to measure fluid accumulation in the neck could contribute to monitoring the effects of neck edema on the severity of sleep apnea and to modify and evaluate various treatments for reducing neck edema to prevent their adverse effects.
  • a sedentary lifestyle causes fluid retention in the legs during the day, which would be redistributed to the thorax and neck when lying down at night. Fluid redistribution into the neck could contribute to the OSA severity, as assessed by apnea-hypopnea index (AHI), by narrowing the upper airway, increasing its resistance, and collapsibility.
  • AHI apnea-hypopnea index
  • the only predictors of AHI were the mucosal water content in the pharynx and internal jugular vein volume as assessed by magnetic resonance imaging. Ultrafiltration reduces total body water, neck fluid volume, and OSA severity in patients with end stage renal disease.
  • wearing compression stockings for 1 day or 1 week respectively reduces overnight decreases in leg fluid volume and increases in neck circumference, in association with a 30% decrease in
  • NFV neck fluid volume
  • a neck fluid volume assessment device for use with a subject while breathing, the device comprising: a microphone to be
  • 1004P-ANF-WO01 positioned in an area of the subject so to acquire acoustic breath sounds emanating from the subject and generate a signal representative thereof; a digital storage device having stored thereon a neck fluid volume assessment engine having associated therewith one or more designated acoustic features previously identified to provide a measure of neck fluid volume; and a data processor operatively coupled to said digital storage device to implement said neck fluid volume assessment engine to act on said signal to automatically extract said one or more designated acoustic features therefrom and output an indication of the subject's neck fluid volume as a function of said extracted one or more features.
  • a non-invasive neck fluid volume assessment method to be performed on a subject, the method comprising: receiving as input a signal representative of breath sounds generated by the subject; extracting one or more designated acoustic features from said input signal, wherein said one or more designated acoustic features define one or more preset neck fluid volume assessment metrics; comparing said one or more extracted features with said one or more preset neck fluid volume assessment metrics; and outputting, based on said comparing, characterization of the subject's neck fluid volume as a function of said one or more metrics.
  • a computer-readable medium having statements and instructions stored thereon for implementation by a processor to act on a signal representative of acoustic breath sounds emanated by a subject in outputting an assessment of the subject's neck fluid volume by performing the steps of the above method.
  • a non-invasive method for assessing a neck fluid volume in a subject comprising: acquiring acoustic breath sounds emanating from the subject over a time period; generating a data signal representative of said acquired acoustic breath sounds; extracting one or more designated acoustic features from said data signal, wherein said one or more designated acoustic features define, alone or in combination, a neck fluid volume metric; comparing said one or more extracted features with said neck fluid volume metric; and outputting, based on said comparing, an estimated neck fluid volume in the subject during said time period.
  • a method of manufacturing a neck fluid volume assessment device comprising: acquiring acoustic breath sounds from multiple subjects to generate respective data signals representative thereof; concurrently measuring respective neck fluid volumes in said subjects during said acquiring; extracting one or more designated acoustic features from each of said signals; classifying said one or more extracted features, alone or in combination, for each of said signals, to correspond with an estimated neck fluid volume based on said concurrently measured neck fluid volumes; defining a neck fluid volume metric based on said classifying to be applied to said one or more designated features once extracted from input breath sound signals to output a corresponding neck fluid volume assessment; and programming a computing device with said defined metric so to act on new input breath sound signals to: extract said one or more designated acoustic features therefrom; compare said one or more extracted features with said neck fluid volume metric; and output, based on said comparing, an estimated neck fluid volume indication.
  • FIG. 1 is a schematic diagram of a neck fluid volume (NFV) assessment system, in accordance with one embodiment of the invention
  • Figure 2 is a schematic diagram of a NFV assessment device, and components thereof, in accordance with one embodiment of the invention.
  • Figure 3 is a diagram of an experimental setup for recording neck bioelectrical impedance and measuring NFV, in accordance with one embodiment;
  • Figure 4 is a chart illustrating an average and standard error of FV among all subjects in different time periods, in accordance with one example (**: P ⁇ 0.001);
  • Figures 5A and 5B are charts of average and standard error of changes in select acoustic features, namely Mel-Frequency Cepstral Coefficients (MFCC) and Mel-Power frequencies acoustic features, respectively, after 90 minutes from baseline ⁇ %.p ⁇ 0.1, *: > ⁇ 0.05, and % / 0.01), in accordance with one embodiment;
  • MFCC Mel-Frequency Cepstral Coefficients
  • Figure 6 is a chart of changes in 1 st and 2 nd formants after 90 minutes in different individuals, in accordance with one embodiment
  • Figure 7 is a chart of recorded and estimated NFV measures based on a regression method implemented in accordance with one embodiment
  • Figure 8 is a flow chart of an exemplary acoustic neck fluid assessment method, in accordance with one embodiment.
  • Figure 9 is a flow chart of a more detailed acoustic neck fluid assessment method, in accordance with one embodiment.
  • an acoustic neck fluid volume assessment system and method will now be described.
  • the systems and methods considered herein rely on acoustic variations observed in relation to the amount of fluid in the neck, for example.
  • the methods and systems described herein can be used to accurately and non-invasively assess an increase in a candidate's neck fluid, which increase constricts the airway and can be correlated with OSA in some instances, by identifying acoustic changes in breathing and/or snoring sounds resulting therefrom.
  • tracheal sound analysis in the context of the below-described embodiments, can provide an effective and non-invasive way to investigate variations in the pathophysiology of the airways and monitor upper airway obstruction during both wakefulness and sleep.
  • Different mechanisms including turbulence of respiratory airflow and pressure fluctuations in the pharynx can contribute to the generation of tracheal sounds.
  • the vibrations so generated are transmitted to the skin through the tracheal wall and tissue beneath the skin, and can be picked up by a microphone placed over the trachea, for example, but also for example via a microphone mounted to or in the ear, the cheek, a face mask disposed above a nose and mouth area of the subject's face, or again, but subject to greater ambient noise, freestanding, mounted or positioned in a room near the subject.
  • a microphone placed over the trachea, for example, but also for example via a microphone mounted to or in the ear, the cheek, a face mask disposed above a nose and mouth area of the subject's face, or again, but subject to greater ambient noise, freestanding, mounted or positioned in a room near the subject.
  • ambient and other noise may be reduced upon positioning the microphone in skin- contact with the subject, for example in a throat, cheek or ear area.
  • the system 100 generally comprises a microphone 102 or the like to be attached on the surface of a throat area of a candidate for acquiring acoustic sounds and/or signals over time.
  • the microphone 102 is operatively coupled to a data processing device 104 having stored and implemented thereon one or more neck fluid volume assessment tools/engines to automatically process the acquired data according to one or more designated assessment protocols for output.
  • data processing device 104 is illustrated in Figure 1 as distinct from the microphone/recording device 102, in some embodiments, the microphone 102 and data processing device 104 may be integral to or combined in a common data recording device to be worn on the subject's neck area, for example. While the term “data processing device” is used genetically herein to refer not only to a device for performing automated or semi-automated acoustic neck fluid volume assessments, it may also refer to similar devices also configured for the detection or assessment of other more or less related conditions, symptoms, and/or biological processes.
  • the processing device 104 is depicted herein as a distinctly implemented device operatively coupled to microphone 102 for communication of data thereto, for example, via one or more data communication media such as wires, cables, optical fibres, and the like, and/or one or more wireless data transfer protocols, as would be readily appreciated by one of
  • the processing device may, however, in accordance with other embodiments, be implemented integrally with a recording device embodying the microphone (e.g. within the context of a self-contained assessment tool or device that can be secured to or on the subject's body during data acquisition and processing), for example, depending on the intended practicality of the system 100, and/or context within which it is to be implemented.
  • processing device 104 may further or alternatively be coupled to, or operated in conjunction with, an external processing and/or interfacing device, such as a local or remote computing device or platform provided for the further processing and/or display of raw and/or processed data, or again for the interactive display of system implementation data, protocols and/or screening/assessment tools.
  • an external processing and/or interfacing device such as a local or remote computing device or platform provided for the further processing and/or display of raw and/or processed data, or again for the interactive display of system implementation data, protocols and/or screening/assessment tools.
  • the processing device depicted herein generically as a self-contained device 200, generally comprises a power supply 202, such as a battery or other known power source, and various input/output port(s) 204 for the transfer of data, commands, instructions and the like with interactive and/or peripheral devices and/or components (not shown), such as for example, a distinctly operated microphone and/or acoustic data recorder, external data processing device, display or the like.
  • the device 200 further comprises one or more computer-readable media 208 having stored thereon statements and instructions for implementation by one or more processors 206 in automatically implementing various computational tasks with respect to, for example, acoustic data acquisition and processing 210, operation of the device 212 (e.g.
  • the device 200 may further comprise a user interface 216, either integral thereto, or distinctly and/or remotely operated therefrom for the input of data and/or commands (e.g. keyboard, mouse, scroll pad, touch screen, push-buttons, switches, etc.) by an operator thereof, and/or for the presentation of raw, processed and/or assessment data outputs (e.g. graphical user interface such as CRT, LCD, LED screen, touchscreen, or the like, visual and/or audible signals/alerts/warnings/cues, numerical displays, etc.)
  • data and/or commands e.g. keyboard, mouse, scroll pad, touch screen, push-buttons, switches, etc.
  • raw, processed and/or assessment data outputs e.g. graphical user interface such as CRT, LCD, LED screen, touchscreen, or the like, visual and/or audible signals/alerts/warnings/cues, numerical displays, etc.
  • device 200 may be considered herein without departing from the general scope and nature of the present disclosure. It will further be appreciated that device 200 may equally be implemented as a distinct and dedicated device, such as a dedicated home, clinical or bedside assessment device, or again implemented by a multi-purpose device, such as a multi-purpose clinical or bedside device, or again as an application operating on a conventional computing device, such as a laptop or PC, or other personal computing devices such as a PDA, smartphone, tablet or the like.
  • a conventional computing device such as a laptop or PC, or other personal computing devices such as a PDA, smartphone, tablet or the like.
  • the stored statements and instructions of computer-readable medium 208 encompass one or more acoustic FV assessment tools/engines 214 that, when launched via processor 206, act on acquired acoustic data to output one or more assessments useful in characterizing an amount of fluid in the subject's neck, for example.
  • the assessment tool/engine 214 may be configured to receive as input (e.g. via input port 204) acoustic data of interest acquired, for example, via a recording device and/or microphone, such as microphone 102 of Figure 1.
  • the engine will include an optional preprocessing utility, a feature extraction utility, an estimation utility, and one or more optional post-processing utilities, the later generating a global or respective outputs to be rendered or otherwise provided via the system's input/output port 204 and/or user interface 216.
  • a neck fluid volume assessment method 800 may initiate from recorded breath sounds, indicated herein as breath sound signal 802, be they prerecorded, stored and/or recorded in real or quasi real-time fashion.
  • the breath sound signal 802 is processed via a predefined feature extraction process 804 which takes as input both the (optionally preprocessed) sound signal 802 and a set of designated acoustic features 806 previously identified to accurately characterize (i.e. distinctly identify or quantify) neck fluid volumes, for example.
  • the extracted features output from step 806 are then processed through a predefined feature characterization process 808 that takes as input a designated neck fluid volume metric 810 predefined as a function of the designated features 806 so to output a neck fluid volume characterization 812.
  • a more detailed neck fluid volume assessment method 900 may again initiate from a breath sound signal 902, in this embodiment, that is optionally preprocessed via a noise reduction algorithm at step 904.
  • the (preprocessed) signal is then spliced into distinct time segments at step 906 so to allow for an assessment of neck fluid volume over time.
  • Each time-segmented signal is then optionally transformed via time and/or frequency transformation utilities at step 908, to have time, frequency and/or time-frequency domain features respectively extracted therefrom at step 910 based on input designated acoustic features 912.
  • the input device is operable to obtain an acoustic signal that is to be used for estimation, and may comprise a microphone as noted above, or another sound source, or again may include an input communicatively linked to a microphone or other sound source, for example.
  • a sound source could be a sound file stored on a memory or an output of a sound producing device, and used as an alternative to direct acoustic sound recording of pharyngeal/airway sounds.
  • the sound may be a pre-recorded sound that is synthesized to resemble a natural sound.
  • a simulation computer may be programmed to output a particular sound that resembles bodily sounds.
  • the fluid estimation engine may be applied to the outputted sound for the purposes of model simulation.
  • the optional preprocessing utility may apply noise reduction on the acoustic signal by applying a noise reduction algorithm, such as spectral subtraction, for example.
  • the one or more feature extraction utilities may then measure specific attributes of the acoustic signal, designated in accordance with the methods described herein, to produce quantifiable results reliably indicative of fluid volume. From these extracted features, the
  • 1004P-ANF-WO01 estimation utility may then estimate the amount of fluid in the airway that can be post- processed or normalized for output, such as in the form of a stored output on a computer- readable memory or device, a readout such as on screen or display, and the like.
  • the engine(s) may be implemented by a computerized device, such as a desktop computer, laptop computer, tablet, mobile device, or other device having one or more computer processors and a memory having stored thereon statements and instructions which, when executed by the one or more computer processors, provide the functionality described herein.
  • a computerized device such as a desktop computer, laptop computer, tablet, mobile device, or other device having one or more computer processors and a memory having stored thereon statements and instructions which, when executed by the one or more computer processors, provide the functionality described herein.
  • the engine(s) may be embodied in a single-use device or in respective single use devices.
  • the device could, for example, be a handheld computerized device comprising a microphone as the input device, a screen or speaker as the output device, and one or more processors, controllers and/or electric circuitry implementing, for example, one or more of a signal splicing utility, a time transformation utility and a frequency transformation utility, for example, or may otherwise be implemented within a more general device, such as depicted in Figure 2, or again within the context of a general purpose computer.
  • One particular example of such a device is a mobile device whose input device is pressed against the neck or airway under consideration.
  • Another example of such a device is an implantable or wearable device (for example, worn around the neck).
  • Another example of such a device is a microphone connected to a stationary computational device in which the estimation occurs.
  • the estimation engine(s) may be applied to different sounds represented by an acoustic signal.
  • the sound may be the breathing or snoring of an individual.
  • the engine(s) may be applied jointly or independently to the breathing of an individual with OSA in order to estimate the amount of fluid in their neck, for example.
  • the processing of acoustic sound data in accordance with the herein-described embodiments allows for a characterization of a subject's neck fluid volume, and this, despite significant variations in neck tissue
  • 1004P-ANF-WO01 compositions (fat, muscle, etc.), variations between subjects, and the general dynamics of the neck as compared to other more static or uniform portions of the body.
  • Example 1 provides different examples, in accordance with some aspects of the above-described embodiments, of an acoustic neck fluid volume assessment method and system. It will be appreciated by the skilled artisan that this example is not intended to limit the general scope and nature of the present disclosure, but rather provide further evidence as to the utility, applicability and/or accuracy of the methods and system described herein in accordance with different embodiments of the invention.
  • Example 1 is not intended to limit the general scope and nature of the present disclosure, but rather provide further evidence as to the utility, applicability and/or accuracy of the methods and system described herein in accordance with different embodiments of the invention.
  • the estimation engine makes use of a model based on relevant acoustic measurements and estimates of fluid.
  • Bioelectrical impedance is a non-invasive technique to estimate fluid volume of tissues. Accordingly, and in one example, the estimation engine is modeled using bioelectrical impedance measures indicative of quantifiable fluid volumes, taken in parallel with acoustic measures to be modeled; bioelectrical impedance measures may include, but are not limited to, single frequency methods to measure extracellular fluid, multi -frequency methods that sweep across a range of frequencies, and bio-impedance tomography in which several electrodes are placed around a relevant body part and activated in succession.
  • NFV neck fluid volume
  • an administrator or user of the fluid estimation engine could populate a sample database with a set of sound recordings and bioelectrical impedance measurements.
  • an administrator or user of the fluid estimation engine could populate a sample database with a set of sound recordings and fluid measurements based on different imaging modalities such as MRI of the neck, for example.
  • Tracheal respiratory sounds can be recorded by a microphone (for example, the Sony ECM-44B omni-directional microphone embedded in a chamber) and can be attached to the suprasternal notch of the subject. Tracheal sounds can be low-pass filtered with a cut-off frequency of 5 kHz using an, for example, the Biopac DA100C amplifier). Both FV and tracheal sounds can be digitized and recorded simultaneously with a given sampling rate (for example, 12.5 kHz).
  • a microphone for example, the Sony ECM-44B omni-directional microphone embedded in a chamber
  • Tracheal sounds can be low-pass filtered with a cut-off frequency of 5 kHz using an, for example, the Biopac DA100C amplifier). Both FV and tracheal sounds can be digitized and recorded simultaneously with a given sampling rate (for example, 12.5 kHz).
  • Tracheal sounds can be band-pass filtered, for example in the frequency range of [30-3000] Hz to remove low- and high-frequency noise, including motion artefacts and measurement noi se respectively .
  • each designated time period for example each inspiratory breath cycle
  • features in the temporal and spectral domains can be extracted from the sound signal. These may include, but are not limited to features such as total duration, average energy, skewness, kurtosis, the ratio between vocalized and unvocalized segments of breath sound lengths, recurrence features such as recurrence period density entropy, and zero crossing rate.
  • pitch frequency can be extracted, for example using the robust-adaptive pitch tracking algorithm, and one or more of the first four formants can be estimated in overlapping or non-overlapping windows, for example Hamming windows of 10 ms.
  • Pitch and formant frequencies can be calculated using analysis of linear prediction coefficients, for example, with frequencies above 90 Hz and bandwidths below 400 Hz. Average power of breath sound may also be calculated, including in specific frequency bands, including Mel-frequency bands. Further processing of the spectrum may be performed using cepstral analysis, for example.
  • the features extracted from the acoustic signal can be reduced or transformed in a number of ways. For example, principal component analysis or independent components analysis may be performed to transform the available data into a smaller dimensionality. Specific features may also be isolated from others, for example, by forward selection, minimum-redundancy-maximum-relevance or another statistics-based systems.
  • the estimation engine Given a designated set of features (whether original, transformed, or selected), the estimation engine outputs an estimate of fluid, for example, fluid in the neck, in an established measurement scheme, such as millilitres.
  • the output may be derived by a neural network, Bayes network inference, or regression, for example.
  • This output may be further post-processed, for example, by z-score normalization oorr ffiilltteerriinngg.. FFoorr eexxaammppllee,, tthhee ffiilltteering utility may apply a 10 -order low-pass Butterworth filter whose magnitude response is
  • the estimated fluid value may be output using the output device, saved onto a storage device, or transmitted over a transmission line.
  • inclusion criteria admitted healthy men between 18 and 70 years of age and healthy women more than 18 years of age who were premenopausal and did not have their menstrual cycle at the time of experiments, with a body mass index (BMI) ⁇ 30 kg/m 2 , and a blood pressure of ⁇ 140/90 mmHg.
  • BMI body mass index
  • the exclusion criteria were a history of hysterectomy, having metal implants, cardiovascular, renal, neurological or respiratory diseases, taking any medication for them, or taking any over the counter medication that might influence fluid retention.
  • 1004P-ANF-WO01 two sensing electrodes measure bioelectrical impedance, which is inversely related to the amount of fluid in the tissue.
  • sensing electrodes V+, V-
  • injecting electrodes I+, I-
  • Bioelectrical impedance is inversely related to the fluid content of each segment and can be estimated as defined above by equation (1).
  • neck length and circumference were measured with a measuring tape.
  • Tracheal respiratory sounds were recorded by a Sony ECM-44B omni-directional microphone embedded in a chamber (diameter of 6 mm) and attached to the suprasternal notch of the subject with double-sided tape. Tracheal sounds were low-pass filtered with a cut-off frequency of 5 kHz. Both NFV and tracheal sounds were digitized and recorded simultaneously with a sampling rate of 12.5 kHz (MP150, Biopac Systems).
  • Tracheal sounds were band-pass filtered in the frequency range of [30-3000] Hz to remove low- and high-frequency noise, including motion artifacts.
  • 4 periods of data between 0-10 minutes (Period 1), 20-30 minutes (Period 2), 50-60 minutes (Period 3), and 80-90 minutes (Period 4) were selected by an expert annotator and the inspiratory breath cycles without noise artifacts were marked manually.
  • Period 4 4 periods of data between 0-10 minutes (Period 1), 20-30 minutes (Period 2), 50-60 minutes (Period 3), and 80-90 minutes (Period 4) were selected by an expert annotator and the inspiratory breath cycles without noise artifacts were marked manually.
  • For each inspiratory breath cycle several features in the temporal and spectral domains were extracted.
  • Temporal features included duration, average energy, skewness and kurtosis of amplitudes over time, the ratio between voiced and unvoiced segments of breath sound lengths, recurrence period density entropy (RPDE), and zero crossing rate (normalized by the duration) of the inspiratory breath cycle.
  • RPDE recurrence period density entropy
  • 1004P-ANF-WO01 sound was calculated in the following bands: [30 - 100], [100 - 450], [450 - 600], [600 - 800], [800 - 1200], [1200 - 2000], and [2000 - 3000].
  • the power of breath sounds was also calculated over 19 Mel -frequency bands, and the first 12 Mel -frequency cepstral coefficients (MFCCs) were also extracted.
  • MFCCs Mel -frequency cepstral coefficients
  • stepwise regression selects features and derives the model parameters for fluid estimation.
  • features are iteratively added and removed from the input set based on a combination of the t-test and root mean squared error fitting. This method assumes no features are part of an initial set, features are added only if their associated p-va ⁇ ue is below 0.05, and removed only if their associated p-va ⁇ ue is above 0.10.
  • fluid estimation parameters are set along with the feature selection step to achieve the minimum root mean square error between the estimated fluid and measured fluid volume.
  • mRMR minimum-redundancy-maximum-relevance
  • mRMR multi-feature interaction
  • each feature is compared with the class individually and to reduce redundancy only pairwise comparisons between features are made.
  • an additional empirical parameter, ⁇ G R [a i] is added that balances class-feature relevance against feature-feature redundancy. This approach is used to minimize the difference between vectors of correlation coefficients for the set of features and the class while maximizing the average difference of those vectors among the features. Given the correlation table # ( F+I ) X ( F+ I ) where
  • the matrix D provides a similarity measure between two variables in terms of their overall similarity with all other variables in the system. Optimization then becomes a matter of finding the N values of i that minimize
  • this method can be computed in 0(F 2 ) time, without the need for iterative 'hill-climbing'. Both mRMR and this method can replace Pearson's correlation coefficient with other measures of statistical similarity, including mutual information.
  • fluid volume was estimated based on a mixture density neural network consisting of a single output Gaussian, an input vector of the N selected features for the given frame and ⁇ optional frames of context before and after the current frame.
  • the network uses one hidden layer with
  • the output Gaussian represents a distribution over the estimated NFV, with the centroid of that Gaussian taken as the estimate.
  • E[NFV meas ⁇ 2 where E[.] is expected values, NFVmeas is the measured NFV, and NFVest is the estimated NFV.
  • E[.] is expected values
  • NFVmeas is the measured NFV
  • NFVest is the estimated NFV.
  • the performance of two NFV estimation methods among all subjects was also compared by Student's paired t-test.
  • Figure 4 shows the average and standard deviation of absolute values of NFV among all subjects. NFV increased progressively and significantly in all subjects from baseline to 90 minutes (p ⁇ 0.001). It has been shown that relative to baseline (just after lying supine), the changes in NFV follow an exponential model over time. Since the change in NFV over time is smaller than the baseline amplitude of NFV, such exponential changes in NFV are not visible in Figure 4. However, since the main objective was to estimate absolute values of NFV and not the changes in NFV, absolute values of NFV were demonstrated at various time segments.
  • Figure 6 shows the variations in the first and second formant frequencies of every individual after 90 minutes. In some subjects there was a decrease in the first or the second formants, which complies with the shift of the power spectrum to the lower frequencies as observed in Figure 5. However as presented in Figure 6, this pattern was not consistent in all subjects. This may be due to the differences among subjects in the upper airway (UA) dilator muscle activities or reflexes. [0075] Among those features selected for each subject, the 10 most frequent features across all subjects were identified. These features (Table 1) can be considered as a globally optimum set of features based on each method for estimating NFV. For both methods, MFCC was selected.
  • Table 1 Ten most frequently selected features among all subjects. (MFCC: Mel- frequency cepstral coefficient)
  • Figure 7 shows an example of recorded and estimated NFV based on a regression model for a typical subject.
  • the results show that while the absolute values of recorded NFV changed from 199 millilitres (ml) to 208ml, the error of estimating NFV based on regression model was less than ⁇ lml.
  • the average and standard deviation of absolute and relative errors over all subjects for each method is shown in Table 2. Both methods achieved high accuracy in estimating NFV from the selected acoustic features for each subject. However, compared to the neural network method, the absolute and relative errors were significantly smaller when
  • Table 2 Average and standard deviation of errors for each method for estimating NFV.
  • the herein-described methods and system allowed for the investigation of physiological factors that may contribute to the changes in tracheal sound features, and the analysis of the relative utility of different acoustic features in the estimation of NFV.
  • Acoustic features were compared using two methods: 1) an approach using feature selection with stepwise regression; and 2) an approach that weighs the relative similarity of a
  • 1004P-ANF-WO01 set of features and the predicted variable against the relative similarity among those features.
  • This latter approach allows for a weighting between the relevance of a set of features to a predicted variable, and their internal redundancy.
  • One use of this approach is to avoid issues of over-fitting or over-specification. Additionally, this latter approach to feature selection avoids the need to perform iterative 'hill-climbing' optimization and instead finds a global optimum quickly.
  • Fluid accumulation in the neck may increase pharyngeal tissue pressure around the UA and consequently narrow the UA.
  • narrowing in the oro-pharyngeal part of the UA could change the spectral shape of the generated tracheal sound.
  • narrowing in the top or back of the oral cavity due to tongue movement can decrease the first and second formants, respectively.
  • Posterior movements of the tongue could reflect narrowing in the UA. Therefore, it may be expected that UA narrowing could cause a shift in the power components of the tracheal sounds to the lower frequencies and decrease the second formant of tracheal sound.
  • tracheal sound analysis can be used, as described above, to develop non-invasive, convenient and reliable methods to estimate fluid accumulation in the neck, which can provide a useful, non-invasive tool in the study of the pathophysiology of
  • 1004P-ANF-WO01 sleep apnea for example, and in providing useful diagnostic and/or monitoring information for the treatment of OSA, of example.

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Abstract

La présente invention concerne différents modes de réalisation d'un système et d'un procédé d'évaluation de volume de fluide de col acoustique. Dans un tel mode de réalisation, un dispositif d'évaluation de volume de fluide de col comprend un microphone destiné à être positionné dans une région du sujet de manière à acquérir des sons de respiration acoustique émanant du sujet tandis qu'il respire et génère un signal représentatif de celui-ci, un dispositif de stockage numérique dans lequel sont stockés un moteur d'évaluation de volume de fluide de col ayant, associé à celui-ci, une ou plusieurs caractéristiques acoustiques précédemment identifiées pour produire une mesure de volume de fluide de col, et un processeur de données fonctionnellement couplé au dispositif de stockage numérique pour mettre en œuvre le moteur d'évaluation de volume de fluide de col pour agir sur le signal de façon à extraire automatiquement les un ou plusieurs éléments acoustiques désignés de celui-ci et délivrés en sortie une indication du volume de fluide de col du sujet en fonction des une ou plusieurs caractéristiques extraites.
PCT/CA2014/050627 2014-07-02 2014-07-02 Système et procédé d'évaluation de volume de fluide de col acoustique WO2016000061A1 (fr)

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