A novel fetal ecg signal extraction from maternal ecg signal using conditional generative adversarial networks (CGAN)

Abstract

Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system.

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

“…Dividing the signal into many sources entails separating this TFI and TSI into multiple distinct TFIs and TSIs meant to convey those sources. To provide context for the concept of independence in these surroundings, we must elaborate on underlying assumptions [23][24][25][26] . The TSI is obtained by computing the modulus of a CWT applied to the observed signal X.t /: Then, the TSI coefficients are calculated to sort out the amplitude of a specific frequency at a particular time.…”