Supervised model for Cochleagram feature based fundamental heart sound identification. (July 2019)

• Record Type: Journal Article
• Authors: Das, Sangita; Pal, Saurabh; Mitra, Madhuchhanda
• Is Part Of: Biomedical signal processing and control. Volume 52(2019)
• Page Start: 32
• Page End: 40
• Publication Date: 2019-07
• Subjects: Heart sound -- PCG -- Cochleagram -- Gammatone filter -- ANN classifier; 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.2019.01.028
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Acoustic feature based heart sound segmentation algorithm is presented. Different features have been evaluated using ANN classifier for fundamental heart sound detection. Cochleogram feature produces the best result compared to the other features investigated. A median smoothing filter improvises the detection accuracy. Abstract: The efficiency of automated heart sound analysis mostly depends on accurate detection of acoustic events. In this study, an acoustic feature based heart sound segmentation algorithm has been proposed for automatic identification of the fundamental heart sounds (FHS). Gammatone filter bank energy has been introduced to represent the heart sound distinctive features. A supervised artificial neural network (ANN) model is used to detect S1-S2 and non S1-S2 segments of the cardiac cycle. Finally time based information is utilized to identify S1 and S2 positions. Performance of the system is evaluated using 764 real and noisy heart sound cycles (both normal and abnormal domains) from the 2016 PhysioNet/CinC challenge database with annotations provided for heart sound states. The accuracy achieved using Cochleagram feature is more than 95% for both first and second heart sound identification. Proposed technique shows that multilayer perceptron (MLP) neural network using Cochleagram feature improvises the overall S1-S2 identification accuracy compared to the other acoustic features reported earlier.