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Architecture-Specific Impact of Preprocessing on Machine Learning Models for ECG Classification.

Lucas Bickmann¹, Lucas Plagwitz², Antonius Büscher^2,3

¹Institute of Medical Data Science, Otto-von-Guericke University Magdeburg.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This summary is machine-generated.

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Deep learning for electrocardiogram (ECG) analysis shows preprocessing impacts vary by model. Convolutional neural networks excel with raw data, challenging standard practices for ECG classification.

Area of Science:

Cardiology
Artificial Intelligence
Biomedical Signal Processing

Background:

Deep learning models are increasingly used for automated electrocardiogram (ECG) analysis.
Preprocessing steps are often assumed to universally improve ECG classification performance.

Purpose of the Study:

To investigate the impact of signal cleaning, trend removal, and normalization on six deep learning architectures for ECG classification.
To challenge the conventional assumption that specific preprocessing techniques universally benefit ECG analysis.

Main Methods:

Evaluation of 24 preprocessing combinations across six leading deep learning architectures (CNNs, Wavelet-based, Transformers).
Utilized the PTB-XL dataset, a standard reference for ECG data analysis.
Each trial was repeated ten times to ensure robustness.

Keywords:

Electrocardiogram Health Informatics Machine Learning Preprocessing

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Main Results:

Significant architecture-dependent sensitivity to preprocessing techniques was observed.
Convolutional neural networks performed best with raw, unnormalized ECG data.
Wavelet-based models improved with trend removal; Transformers showed broad robustness.

Conclusions:

Preprocessing pipelines for ECG analysis should be tailored to specific deep learning model architectures.
The assumption of universally beneficial normalization for bounded signal data in ECG preprocessing is challenged.
Findings advocate for a nuanced approach to ECG data preprocessing in deep learning.