USF researchers have developed a machine learning framework to identify the patterns of cardiac sounds. The acoustic sounds are obtained from the body site and are preprocessed to identify the denoised heart sound using a segmentation and wavelet decomposition framework. To characterize the healthy and unhealthy heart sounds, multiscale energy feature was developed and obtained for the segmented denoised heart signals. These features are then used as input to the pattern recognition framework for further classification. The framework is quantitatively and qualitatively validated. The classification accuracy of the framework to identify the abnormalities is 94.37%.
MSE of Acoustic Heart Pulses Corresponding to Abnormal Sinus Rhythms