# Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning

arXiv: https://arxiv.org/abs/2405.00724 · HTML: https://mlsanity.com/paper/2405.00724/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Numerical differentiation of noisy data: A unifying multi-objective optimization framework](https://mlsanity.com/paper/2009.01911/) (2020)
- [Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor](https://mlsanity.com/paper/1911.05521/) (2019)
- [Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout](https://mlsanity.com/paper/1708.05356/) (2017)
- [FRI-TEM: Time Encoding Sampling of Finite-Rate-of-Innovation Signals](https://mlsanity.com/paper/2106.05564/) (2021)
- [Encoding Integers and Rationals on Neuromorphic Computers using Virtual Neuron](https://mlsanity.com/paper/2208.07468/) (2022)

## Built on by (most cited first, 1 of 1)

- [Closed-Form Feedback-Free Learning with Forward Projection](https://mlsanity.com/paper/2501.16476/) (2025)

## Abstract (arXiv)

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.