A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram.

Almost all cardiac abnormalities manifest themselves as murmurs in a phonocardiogram (PCG). The location of a murmur in the PCG over a cardiac cycle depends on the underlying cardiac abnormality. Locating murmurs with respect to the cardiac cycle is useful for diagnosing cardiac dysfunction. Locating murmurs is difficult due to spectral similarity and time overlap with other heart sounds. Moreover, the wide variation in murmur amplitudes and murmur spectral characteristics across different patients and abnormalities has hindered the design of a generic time segmentation algorithm to isolate murmurs within the PCG. In this paper, we present a method to locate cardiac murmurs within the PCG that is based on their visual simplicity, which does not depend upon their absolute amplitude and frequency characteristics, and hence, results in their better localization. Then, we identify fuzzy sets to cluster simplicity values, due to murmurs, to determine the time duration over which the murmur occurs. The overall accuracy of the proposed algorithm in detecting systolic murmurs is 80%. The sensitivity of the algorithm in locating systolic murmurs is 73% and its specificity is 100%. The isolated murmur can then be further processed to extract clinically relevant features to diagnose the nature of the cardiac abnormality. Experimental results with a variety of murmurs are provided.