An efficient heart sound segmentation approach using kurtosis and zero frequency filter features. (March 2020)

• Record Type: Journal Article
• Authors: Shukla, Sumitra; Singh, Sachin Kumar; Mitra, Debjani
• Is Part Of: Biomedical signal processing and control. Volume 57(2020)
• Publication Date: 2020-03
• Subjects: Heart sound segmentation -- Kurtosis -- Zero frequency resonator -- Hilbert envelope -- Squared envelope; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101762
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: Abrupt change at the heart sound locations is used for heart sound localization. Kurtosis of signal envelope is passed through zero frequency filter to preserve the impulses at heart sound locations. Algorithm is working well for normal heart sounds and pathological heart sounds. The robustness of the proposed algorithm is verified using additive white Gaussian noise and respiratory noise. Abstract: This paper proposes an efficient heart sound segmentation method for automatic detection of heart sounds. In this method, the abrupt change at the heart sound locations is considered as a cue factor for segmentation. The phonocardiogram signal is analysed by passing kurtosis of the signal envelope through zero frequency filter (ZFF). The impulses at the locations of S1 and S2 in the filtered signal are used for the localization of heart sound. The performance of proposed method is evaluated on a real clinical dataset PhysioNet/CinC Challenge Heart Sound (PhysioNet/CinC). A set of 120 heart sound recordings, consisting of normal heart sound as well as pathological heart sound, is considered for evaluation. The experimental result shows that the proposed algorithm achieves an average sensitivity of 98.61%, average positive prediction of 99.11% and average overall accuracy of 98.07%. The robustness of the proposed algorithm is verified using additive white Gaussian noise and respiratory noise.