A Novel Framework For Maternal Ecg Removal From Single-Channel Abdominal Recording

Objective: Abdominal ECG (AECG) recorded at the maternal abdomen is significantly affected by the maternal ECG (MECG), making the extraction of FECG a challenging task. This paper presents a new MECG elimination method based on Short Time Fourier Transform (STFT) and Convolutional Autoencoder (CAE). Methods: First, the STFT is used to transform the AECG from one-dimensional (1D) time domain into two-dimensional(2D) time-frequency domain. Next, the CAE model is applied to estimate the 2D-STFT coefficients of MECG. Finally, after the inverse STFT of MECG, we can extract the FECG by subtracting the MECG from the AECG in the time domain. Different from the methods estimated the MECG in the 1D time domain, the novelty of the proposed method relies on estimating the MECG in the 2D time-frequency domain. Specifically, the CAE model learns the end-to-end mappings from the 2D-STFT coefficients of AECG to the MECG. Results: Experimental results show that the proposed method is effective in removing the MECG. Significance: This work enhances the clinical applications of FECG in the early detection of fetal heart diseases.