Fetal Arrhythmia Detection Using Fetal ECG Signal

This paper proposes a denoising method based on an adversarial deep learning approach for the post-processing of multi-channel fetal electrocardiogram (ECG) signals. As it’s well known, noise leads to misinterpretations of fetal ECG signals and thus limits the use of fetal electrocardiography for healthcare applications. Therefore, denoising algorithms are essential for the exploitation of non-invasive fetal ECG. The proposed method is based on the combination of three end-to-end trained sub-networks to convert noisy fetal ECG signals into clean signals. The first two sub-networks are linked by skip connections and form a deep convolutional network that downsamples the noisy signals into a latent representation and subsequently upsamples this latent representation to recover clean signals. The third sub-network aims to boost the decoder sub-network to generate realistic clean signals. Experiments carried out on synthetic and real data showed that the proposed method improved by the signal-to-noise (SNR) of fetal ECG signals with input SNR ranging from $$-\,30$$ - 30 to 0 dB by an average of 20 dB, and improve fetal signal quality by significantly increasing the number of true detected QRS complexes and halving QRS complex detection errors.

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Fetal Arrhythmia Detection Using Fetal ECG Signal

Cited by 16 publications

References 10 publications

A Machine Learning Framework for Fetal Arrhythmia Detection via Single ECG Electrode

A Machine Learning Framework for Fetal Arrhythmia Detection via Single ECG Electrode

Health Monitoring Methods in Heart Diseases Based on Data Mining Approach: A Directional Review

Multichannel high noise level ECG denoising based on adversarial deep learning

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