Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features

• Published in: IEEE journal of biomedical and health informatics Vol. 18; no. 4; pp. 1138 - 1152
• Main Authors: Papadaniil, Chrysa D., Hadjileontiadis, Leontios J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Aged; Aged, 80 and over; Aortic Valve Stenosis - physiopathology; Data mining; Databases, Factual; Electrocardiography; Ensemble empirical mode decomposition (EEMD); Feature extraction; Female; first and second heart sound; Heart; heart sound segmentation (HSS); Heart Sounds - physiology; HOS; Humans; Informatics; kurtosis; Male; Middle Aged; Mitral Valve Insufficiency - physiopathology; Noise; phonocardiogram (PCG); Phonocardiography; Phonocardiography - methods; Physical examinations; Signal Processing, Computer-Assisted; Valves; Young Adult
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2013.2294399

An efficient heart sound segmentation (HSS) method that automatically detects the location of first ( S 1 ) and second ( S 2 ) heart sound and extracts them from heart auscultatory raw data is presented here. The heart phonocardiogram is analyzed by employing ensemble empirical mode decomposition (EEMD) combined with kurtosis features to locate the presence of S 1 , S 2 , and extract them from the recorded data, forming the proposed HSS scheme, namely HSS-EEMD/K. Its performance is evaluated on an experimental dataset of 43 heart sound recordings performed in a real clinical environment, drawn from 11 normal subjects, 16 patients with aortic stenosis, and 16 ones with mitral regurgitation of different degrees of severity, producing 2608 S 1 and S 2 sequences without and with murmurs, respectively. Experimental results have shown that, overall, the HSS-EEMD/K approach determines the heart sound locations in a percentage of 94.56% and segments heart cycles correctly for the 83.05% of the cases. Moreover, results from a noise stress test with additive Gaussian noise and respiratory noises justify the noise robustness of the HSS-EEMD/K. When compared with four other efficient methods that mainly employ wavelet transform, energy, simplicity, and frequency measures, respectively, using the same experimental database, the HSS-EEMD/K scheme exhibits increased accuracy and prediction power over all others at the level of 7-19% and 4-9%, respectively, both in controls and pathological cases. The promising performance of the HSS-EEMD/K paves the way for further exploitation of the diagnostic value of heart sounds in everyday clinical practice.