Deep learning with fetal ECG recognition.

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Abstract

Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG recognition by using a deep learning strategy.A cross-domain consistent convolutional neural network (CDC-Net) is developed for this task of FECG recognition. The output of the ICA algorithm is used as input to the CDC-Net. And the CDC-Net identifies which channel’s signal is the target FECG.signals from two databases are used to test the efficiency of the proposed method. The proposed deep learning method exhibits good performance on FECG recognition. Specifically, the Precision, Recall and F1-score of the proposed method on ADFECGDB database are 91.69%, 91.37% and 91.52%, respectively. And the Precision, Recall and F1-score of the proposed method on DDB database are 97.85%, 97.42% and 97.63%, respectively.This work is a proof of concept that the proposed method can automatically recognize the FECG signals in multichannel ECG data. Development of fetal ECG recognition technology contributes to the automated fetal ECG monitoring.© 2023 Institute of Physics and Engineering in Medicine.