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Blind Source Subspace Separation and Classification of ECG Signals

Kahina Bensafia 1, 2, 3 Ali Mansour 1, 2 Salah Haddab 3
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Extracting foetal electrocardiogram (fECG) plays an important role in diagnosing foetus’s health. However, in real clinical tests, a clean extraction of fECG is difficult to be obtained, because it is affected by various signals such as mother electrocardiogram (mECG), electromyogram (EMG) derived from the uterus and muscle contractions, the respiration signal, electronic noise, etc. Inspired by recent work estimating fECG subspace from a mixed ECG signals using multidimensional independent component analysis (MICA) along with the cyclic coherence (CC), we propose here an approach to separate and classificaty ECG signals recorded from abdominal and thoracic electrodes of pregnant women. The first step, blind source separation (BSS) is done by applying the joint approximate of Eigen matrices (JADE) algorithm to obtain independent components (ICs). Then continuous wavelet transform (CWT) is adopted for classifying the independent components previously obtained into three subspace components: fetal ECG signals, the mother ECG signals, and the noise. Our experimental results have corroborated the proposed approach using the database DaISy.
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Conference papers
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Contributor : Annick Billon-Coat <>
Submitted on : Friday, June 30, 2017 - 11:51:06 AM
Last modification on : Monday, April 19, 2021 - 3:36:05 PM


  • HAL Id : hal-01551453, version 1


Kahina Bensafia, Ali Mansour, Salah Haddab. Blind Source Subspace Separation and Classification of ECG Signals. ATS 2017, Mar 2017, Sousse, Tunisia. ⟨hal-01551453⟩



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