Journal
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
Volume 38, Issue 3, Pages 448-455Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.bbe.2018.03.003
Keywords
Fetal ECG extraction; Independent component analysis (ICA); Automatic identification; ECG features
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Funding
- National Natural Science Foundation of China [61271079]
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Independent component analysis (ICA) is usually used as a preliminary step for maternal electrocardiogram (ECG) QRS detection in fetal ECG extraction. When applying ICA to do this, a troublesome problem arises from how to automatically identify the separated maternal ECG component. In this paper we proposed a method called PRCH (short for Peak to peak entropy, R-R interval entropy, Correlation coefficient and Heart rate) for the automatic identifying. In the method, we defined four kinds of features, including amplitude, instantaneous heart rate, morphology and average heart rate, to characterize a signal, and determined some decision parameters through machine learning. Experiments and comparison with other three existed methods were given. Through taking metric F1 for evaluation, it showed that the proposed PRCH method has the highest identifying accuracy and generalization capability. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
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