Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning

TL;DR

This paper introduces Derived Peak (DP) encoding, a shift- and scale-invariant signal encoding method that enhances the robustness of deep learning models for ECG classification against common artefacts like baseline drift and noise.

Contribution

The study presents DP encoding, a novel non-parametric approach that improves ECG analysis robustness without requiring user-defined parameters, outperforming existing methods under artefact conditions.

Findings

DP encoding maintains high accuracy under artefacts.

DP outperforms prior methods in robustness tests.

Significant accuracy drop in other methods with artefacts.

Abstract

Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of machine learningbased automated ECG analysis and interpretation. This study proposes Derived Peak (DP) encoding, a non-parametric method that generates signed spikes corresponding to zero crossings of the signals first and second-order time derivatives. Notably, DP encoding is invariant to shift and scaling artefacts, and its implementation is further simplified by the absence of userdefined parameters. DP encoding was used to encode the 12-lead ECG data from the PTB-XL dataset (n=18,869 participants) and was fed to 1D-ResNet-18 models trained to identify myocardial infarction, conductive deficits and ST-segment abnormalities. Robustness to artefacts was assessed by corrupting ECG data with sinusoidal baseline drift, shift, rescaling and noise, before encoding. The addition of these artefacts resulted in a significant drop in accuracy for seven other methods from prior art, while DP encoding maintained a baseline AUC of 0.88 under drift, shift and rescaling. DP achieved superior performance to unencoded inputs in the presence of shift (AUC under 1mV shift: 0.91 vs 0.62), and rescaling artefacts (AUC 0.91 vs 0.79). Thus, DP encoding is a simple method by which robustness to common ECG artefacts may be improved for automated ECG analysis and interpretation.