Open AccessSource separation from single-channel abdominal phonocardiographic signals based on independent component analysis
Author(s) -
Sepideh Jabbari
Publication year - 2021
Publication title -
biomedical engineering letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.709
H-Index - 26
eISSN - 2093-985X
pISSN - 2093-9868
DOI - 10.1007/s13534-021-00182-z
Subject(s) - independent component analysis , phonocardiogram , blind signal separation , computer science , signal (programming language) , noise (video) , wavelet , pattern recognition (psychology) , source separation , channel (broadcasting) , artificial intelligence , speech recognition , telecommunications , image (mathematics) , programming language
Purpose : Continuous monitoring of fetal heart rate (FHR) is essential to diagnose heart abnormalities. Therefore, FHR measurement is considered as the most important parameter to evaluate heart function. One method of FHR extraction is done by using fetal phonocardiogram (fPCG) signal, which is obtained directly from the mother abdominal surface with a medical stethoscope. A variety of high-amplitude interference such as maternal heart sound and environmental noise cause a low SNR fPCG signal. In addition, the signal is nonstationary because of changes in features that are highly dependent on pregnancy age, fetal position, maternal obesity, bandwidth of the recording system and nonlinear transmission environment. Methods : In this paper, a sources separation process from the recorded fPCG signal is proposed. Independent component analysis (ICA) has always been one of the most efficient methods for extracting background noise from multichannel data. In order to extract the source signals from the single-channel fPCG data using ICA algorithm, it is necessary to first decompose the signal into multivariate data using a proper decomposition technique. In this paper, we implemented three combined methods of SSA-ICA, Wavelet-ICA and EEMD-ICA. Results : In order to validate the performance of the methods, we used simulated and real fPCG signals. The results indicated that SSA-ICA recovers sources of single-channel signals with different SNRs. Conclusion : The performance criteria such as power spectral density (PSD) peak and cross correlation value show that the SSA-ICA method has been more successful in extracting independent sources.