Medical decision support system

A computer-implemented method of detecting a cardiac condition from an auscultatory sound signal, comprising:

• a. receiving an electrographic signal from an electrocardiogram (ECG) sensor;
• b. receiving at least one auscultatory sound signal responsive to a corresponding at least one auscultatory sound sensor operatively associated with a test-subject;
• c. generating a feature set of data responsive to said at least one auscultatory sound signal, wherein said feature set of data provides for input to a support vector machine (SVM) that is pretrained to detect the cardiac condition, wherein the operation of generating said feature set of data comprises:
• i. for each frequency interval of a plurality of frequency intervals:
• a) bandpass filtering said at least one auscultatory sound signal so as to generate a corresponding time series of bandpass-filtered data;
• b) segmenting said corresponding time series of bandpass-filtered data responsive to said electrographic signal so as to associate a plurality of heart-cycle time series with said corresponding time series of bandpass-filtered data, wherein each heart-cycle time series of said plurality of heart-cycle time series spans a single corresponding heart cycle;
• c) identifying a start of an S1 heart sound in each of said plurality of heart-cycle time series;
• d) temporally aligning each of said plurality of heart-cycle time series with respect to said start of said S1 heart sound therein;
• e) identifying a longest said heart-cycle time series of said plurality of heart-cycle time series corresponding to a slowest heart cycle of a plurality of heart cycles that correspond to said plurality of heart-cycle time series;
• f) for each relatively-shorter said heart-cycle time series of said plurality of heart-cycle time series that is shorter in duration than said longest said heart-cycle time series, temporally scaling and temporally interpolating said each relatively-shorter said heart-cycle time series so as to generate a corresponding temporally-scaled heart-cycle time series that is temporally synchronized with, and of the same length, as said longest said heart-cycle time series;
• g) determining an average heart-cycle time series for said frequency interval, wherein each sample of said average heart-cycle time series is responsive to an average of corresponding samples of a) each said heart-cycle time series of said plurality of heart-cycle time series having said same length as said longest said heart-cycle time series and b) each said corresponding temporally-scaled heart-cycle time series associated with remaining said heart-cycle time series of said plurality of heart-cycle time series;
• h) determining at least one energy time series selected from the group of energy time series consisting of a standard-energy time series determined from said average heart-cycle time series, and a Shannon-energy time series determined from said average heart-cycle time series, wherein each sample of said standard-energy time series is responsive to a sum squares of values of samples of said average heart-cycle time series for a subset of samples of said average heart-cycle time series within an associated time interval within said average heart-cycle time series, and each sample of said Shannon-energy time series is responsive to a sum of products for a subset of said samples of said average heart-cycle time series within an associated time interval within said average heart-cycle time series;
• i) locating at least one first time interval within said average heart-cycle time series, wherein a temporal duration of each first time interval of said at least one first time interval is shorter than a temporal duration of said average heart-cycle time series, and each said first time interval is selected from the group of first time intervals consisting of an S1-heart-sound time interval, a systolic time interval, an S2-heart-sound time interval, a pre-S3-heart-sound time interval, an S3-heart-sound time interval, a diastasis time interval, and an S4-heart-sound time interval;
• and
• j) determining a plurality of features of said feature set of data, wherein each feature of said plurality of features is selected from the group of energy measures consisting of a plurality of integrals of said standard-energy time series over a corresponding plurality of second time intervals, a plurality of mean values of said standard-energy time series over said corresponding plurality of second time intervals, a plurality of median values of said standard-energy time series over said corresponding plurality of second time intervals, a plurality of integrals of said Shannon-energy time series over said corresponding plurality of second time intervals, a plurality of mean values of said Shannon-energy time series over said corresponding plurality of second time intervals, and a plurality of median values of said Shannon-energy time series over said corresponding plurality of second time intervals, wherein each second time interval of said corresponding plurality of second time intervals is selected from the group of second time intervals consisting of the entirety of said at least one energy time series and said at least one first time interval;
• and
• d. using said support vector machine (SVM) to generate an estimate of whether or not said test-subject exhibits said cardiac condition responsive to said feature set of data.