Identifying Noisy ECG Signals in Large Datasets Using a Temporal Convolutional Neural Network Trained to Estimate Pseudo-SNR

Abstract

Background: Electrocardiogram (ECG) signals are often contaminated by noise. Manual review of large ECG databases to identify noisy signals is time-consuming. Traditional signal quality assessment algorithms often do not generalize well or are computationally expensive. This study developed a Temporal Convolutional Neural Network (TCNN) to estimate the signal-to-noise ratio (SNR) of ECG signals. Method: We trained a TCNN on a proprietary database of 134,019 12-lead ECGs without any machine or human-added noise labels. Assuming that this data had high SNR, we randomly selected a single lead from each ECG and added random Gaussian noise. We then scaled the signals and added noise to give a negatively skewed normal distribution of true SNR values. We trained a TCNN to regress low-and high-frequency pseudo-SNR values from the raw noisy input signals. Results: On the testing dataset, the TCNN achieved a mean error of 0.31pm 1.80 dB and a Pearson correlation coefficient of 0.96 for low-frequency pseudo-SNR. Similarly, for high-frequency pseudo-SNR, the mean error was 0.29pm 1.63 dB and the Pearson correlation coefficient was 0.97. Conclusion: A Temporal Convolutional Neural Network can accurately estimate the SNR of unseen ECGs.