A cardiac sound characteristic waveform method for in-home heart disorder monitoring with electric stethoscope

Abstract An analytical model based on a single-DOF is proposed for extracting the characteristic waveforms (CSCW) from the cardiac sounds recorded by an electric stethoscope. Also, the diagnostic parameters [T1, T2, T11, T12], the time intervals between the crossed points of the CSCW and an adaptive threshold line (THV), were verified useful for identification of heart disorders. The easy-understanding graphical representation of the parameters was considered, in advance, even for an inexperienced user able to monitor his or her pathology progress. Since the diagnostic parameters were influenced much by a THV, the FCM clustering algorithm was introduced for determination of an adaptive THV in order to extract reliable diagnostic parameters. Further, the minimized J m and [ v 1 , v 2 , v 3 , v 4 ] could be also efficient indicators for identifying the heart disorders. Finally, a case study on the abnormal/normal cardiac sounds is demonstrated to validate the usefulness and efficiency of the cardiac sound characteristic waveform method with FCM clustering algorithm. NM1 and NM2 as the normal case have very small value in J m ( v 1 , v 2 , v 3 , v 4 ] are about [0.1, 0.1, 0.8, 0.4]. For abnormal cases, in case of AR, its J m is very small and the values of [ v 1 , v 3 , v 4 ] are very high comparing to the normal cases. However, in cases of AF and MS have very big values in J m (>0.38).