Key result
Rlign algorithm enables shallow learning to match CNNs in data-limited ECG analysis with superior interpretability.
Why the study?
Deep learning models for ECG analysis require large annotated datasets and offer limited explainability, prompting the need to reintroduce shallow learning methods.
Does the Rlign algorithm improve the performance and explainability of shallow learning methods for ECG analysis compared to CNNs?
Does the Rlign algorithm improve the performance and explainability of shallow learning methods for ECG analysis compared to CNNs?
The Rlign algorithm enables shallow machine learning methods to achieve CNN-level performance in ECG analysis with improved explainability, particularly in data-limited scenarios.
Aims Electrocardiogram (ECG) recordings are fundamental for diagnosing cardiac conditions. Recent advances in automatic ECG analysis have been dominated by deep learning, particularly convolutional neural networks (CNNs). CNNs excel in processing high-dimensional signal data, where their ability to automatically extract complex features has enabled significant progress. However, while CNNs are powerful for biomedical signal analysis, their application to ECG data also carries disadvantages, such as the requirement for large annotated datasets and limited explainability. To address these challenges, we aim to reintroduce shallow learning methods, such as linear classifiers, by leveraging the cyclic nature of ECG signals. Methods and Results We developed an adaptive transformation that restructures ECG signals into a fully structured format suitable for shallow learning algorithms. This method aligns R-peaks across all signals in a dataset and resamples the inter-QRS segments to match a predefined reference heart rate. The approach was systematically evaluated across tasks including classification, clustering, and explainability. Our transformation substantially improved the performance of shallow learning techniques. Compared to CNN approaches, shallow models trained on transformed ECGs achieved superior accuracy and interpretability in data-limited scenarios. Conclusion We demonstrate that shallow machine learning methods, when combined with our alignment-based transformation, can reach CNN level performance in ECG analysis, especially under conditions of limited training data. This approach offers clear advantages in classification, clustering, and explainability, and provides an accessible alternative to deep learning. To facilitate adoption and further research, we release a publicly available framework for ECG signal alignment at https://github.com/imi-ms/rlign.
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Plagwitz et al. (2026) studied Cardiac conditions. Rlign Algorithm (adaptive transformation for ECG signals) vs. Convolutional neural networks (CNNs) was evaluated on Classification, clustering, and explainability performance. The Rlign algorithm enabled shallow machine learning methods to achieve CNN-level performance in ECG analysis, offering superior accuracy and interpretability in data-limited scenarios.
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