Abstract: A Support Vector Machine trained responsive to mean and median values of standard and Shannon energy for a plurality of time and frequency intervals within a heart cycles provides for detecting coronary artery disease (CAD). A quality of an auscultatory sound time-series vector is assessed responsive to a vector distance and angle thereof in relation to a median heart cycle vector.

Abstract: An auscultatory sound signal from at least one auscultatory sound-or-vibration sensor is filtered with a high-pass filter and then 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. A representation 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.

Abstract: An auscultatory sound signal from at least one auscultatory sound-or-vibration sensor is filtered with a high-pass filter and then 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. A representation 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.

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. A representation 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.

Abstract: At least one swing value is determined responsive to a difference between maximum and minimum amplitude values of an auscultatory sound signal within a temporal region of a heart-cycle segment spanning an entire heart cycle of an auscultatory sound signal, wherein a location of at least one temporal region is responsive to a duration of the heart-cycle segment. S4 sound presence is detected responsive to ratio of S4SWING to S2SWING in relation an associated median value thereof from a population of test-subjects. A Support Vector Machine trained responsive to age, sex, S4 presence and a plurality of heart sound swing measures provides for detecting CAD. Unsupervised classification of an S3swing and median and mean values of a Short Time Fourier Transform within associated frequency intervals, based upon data from a plurality of heart cycles of a plurality of test-subject provides for detecting presence of an S3 sound.

Abstract: At least one of at least one data-dependent scale factor or at least one data-dependent detection threshold is determined responsive to at least one block of time-series data of an auscultatory-sound signal generated by an auscultatory-sound sensor operatively coupled to a portion of the skin of a test-subject, wherein the at least one data-dependent scale factor or at least one data-dependent detection threshold provides a measure of a range of values of the at least one block of time-series data in relation to a predetermined metric, and is used to determine whether or not the auscultatory-sound sensor is either debonded or detached from the skin of the test-subject.

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. A representation 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.

Abstract: At least one of at least one data-dependent scale factor or at least one data-dependent detection threshold is determined responsive to at least one block of time-series data of an auscultatory-sound signal generated by an auscultatory-sound sensor operatively coupled to a portion of the skin of a test-subject, wherein the at least one data-dependent scale factor or at least one data-dependent detection threshold provides a measure of a range of values of the at least one block of time-series data in relation to a predetermined metric, and is used to determine whether or not the auscultatory-sound sensor is either debonded or detached from the skin of the test-subject.