Automatic segmentation of Phonocardiogram using the occurrence of the cardiac events. (2017) - Shervegar, M. Vishwanath; Bhat, Ganesh V.
Abstract: Objective: This paper presents automatic method of segmentation of heart sound using the occurrence of the cardiac rhythmic events. Methods: Noisy heart sound is filtered using the 6th order Chebyshev type I low pass filter to remove the redundant noise. Bark Spectrogram is calculated from the cardiac signal by converting spectrogram to the bark scale. The bark spectrogram is smoothened and the loudness index is calculated by averaging the amplitude across all frequency bands. The loudness index is smoothened and differentiated to obtain the event detection function. The smoothened event detection function gives the occurrence of the cardiac events namely the first and the second heart sounds. Conclusion: This method is highly effective in identifying peaks S1 and S2 with the segmentation accuracy of 96.98% giving an F1 measure of 97.09%. Significance: This method does not require the setting up of any type of threshold. So it is a highly effective type of segmentation under noisy conditions.