CNN models outperform LSTM, RBM, and wavelet on same-record ECG denoising, but cross-record generalization is poor, with negative SNR on a held-out record.
Deep Convolutional Neural Networks for Noise Detection in ECGs
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abstract
Mobile electrocardiogram (ECG) recording technologies represent a promising tool to fight the ongoing epidemic of cardiovascular diseases, which are responsible for more deaths globally than any other cause. While the ability to monitor one's heart activity at any time in any place is a crucial advantage of such technologies, it is also the cause of a drawback: signal noise due to environmental factors can render the ECGs illegible. In this work, we develop convolutional neural networks (CNNs) to automatically label ECGs for noise, training them on a novel noise-annotated dataset. By reducing distraction from noisy intervals of signals, such networks have the potential to increase the accuracy of models for the detection of atrial fibrillation, long QT syndrome, and other cardiovascular conditions. Comparing several architectures, we find that a 16-layer CNN adapted from the VGG16 network which generates one prediction per second on a 10-second input performs exceptionally well on this task, with an AUC of 0.977.
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Complex Deep Learning Models for Denoising of Human Heart ECG signals
CNN models outperform LSTM, RBM, and wavelet on same-record ECG denoising, but cross-record generalization is poor, with negative SNR on a held-out record.