Automatic identifying of maternal ECG source when applying ICA in fetal ECG extraction

Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Qiong Yu, Huawen Yan, Lin Song, Wenya Guo, Hongxing Liu, Junfeng Si, Ying ZhaoAbstractIndependent 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.
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research