DocumentCode :
18721
Title :
Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features
Author :
Papadaniil, Chrysa D. ; Hadjileontiadis, L.J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece
Volume :
18
Issue :
4
fYear :
2014
fDate :
Jul-14
Firstpage :
1138
Lastpage :
1152
Abstract :
An efficient heart sound segmentation (HSS) method that automatically detects the location of first ( S1) and second ( S2) 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 S1, S2, 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 S1 and S2 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.
Keywords :
cardiology; diseases; frequency measurement; medical signal processing; patient diagnosis; signal denoising; wavelet transforms; Gaussian noise; HSS-EEMD-K approach; HSS-EEMD-K scheme; Kurtosis features; S1 sequences; S2 sequences; aortic stenosis; ensemble empirical mode decomposition; frequency measurement; heart auscultatory raw data; heart cycles; heart phonocardiogram; heart sound extraction; heart sound locations; heart sound recordings; heart sound segmentation method; heart sounds; mitral regurgitation; noise robustness; noise stress test; pathology; respiratory noises; wavelet transform; Data mining; Feature extraction; Heart; Informatics; Noise; Phonocardiography; Valves; Ensemble empirical mode decomposition (EEMD); HOS; first and second heart sound; heart sound segmentation (HSS); kurtosis; phonocardiogram (PCG);
fLanguage :
English
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
Publisher :
ieee
ISSN :
2168-2194
Type :
jour
DOI :
10.1109/JBHI.2013.2294399
Filename :
6680648
Link To Document :
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