Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG

TL;DR

This paper introduces a multi-channel masked autoencoder (MCMA) to reconstruct 12-lead ECG signals from single-lead ECGs, enabling more practical cardiac diagnostics with comprehensive evaluation benchmarks.

Contribution

The study proposes a novel MCMA model and a comprehensive evaluation benchmark, ECGGenEval, for reconstructing 12-lead ECGs from single-lead inputs, achieving state-of-the-art results.

Findings

Achieved low mean square error and high Pearson correlation in signal reconstruction.

Demonstrated accurate feature-level metrics with low heart rate variability.

Attained high F1-scores in diagnostic-level evaluations.

Abstract

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0317 and 0.1034, Pearson correlation coefficients of 0.7885 and 0.7420. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410.