An adaptive approach to abnormal heart sound segmentation

Heart sound is one of the significant bio-signals to diagnose certain cardiac anomalies. Aiming to provide an automatic heart sounds analysis to medical professionals, we present a method for heart sound segmentation based on the signal´s simplicity and strength. The method has two phases: the first phase identifies the timings of SI and S2 sound limits using simplicity and strength in the wavelet domain; the second phase discriminates among the SI and S2 using high frequency information. The method incorporates an adaptive approach for thresholding the simplicity and strength profiles to detect the timings of the sound in the presence of murmur. The algorithm was tested using a database that contains 7 types of murmurs of various grades. An average accuracy of 4.22 ms was achieved in SI and S2 boundaries identification. Regarding SI and S2 classification overall sensitivity and specificity of 91.78% and 90.41% were obtained, respectively.