Title: Maternal ECG removal using short time Fourier transform and convolutional auto-encoder

Authors: Wei Zhong; Xuemei Guo; Guoli Wang

Addresses: School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, Guangdong, China ' School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, Guangdong, China ' School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, Guangdong, China

Abstract: Foetal electrocardiography (FECG) plays an important role in prenatal monitoring. However, the abdominal electrocardiography (AECG) recorded at the maternal abdomen is significantly affected by the maternal electrocardiography (MECG), making the extraction of FECG a challenging task. This paper presents a deep learning method for MECG removal from single-channel AECG. Firstly, the short time Fourier transform (STFT) is applied to obtain the two-dimensional (2D) features of AECG in time-frequency domain. Secondly, the Convolutional Auto-encoder (CAE) is used to estimate the 2D features of MECG. Finally, after subtracting the estimated MECG, the FECG can be extracted from the AECG. Unlike the methods eliminated the MECG in the 1D time domain, the proposed method focuses on estimating the MECG in the 2D time-frequency domain, where we can take advantage of the structured information in the ECG data. Experimental results on two FECG databases show that the proposed method is effective in eliminating the features of MECG. This study facilitates the clinical applications of FECG in the foetal monitoring.

Keywords: foetal monitoring; convolutional auto-encoder; FECG extraction; MECG removal; short time Fourier transform.

DOI: 10.1504/IJDMB.2020.10029556

International Journal of Data Mining and Bioinformatics, 2020 Vol.23 No.2, pp.160 - 175

Received: 20 Mar 2020
Accepted: 23 Mar 2020

Published online: 22 May 2020 *

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