  Electrocardiogram (ECG) recordings have long been vital in diagnosing
different cardiac conditions. Recently, research in the field of automatic ECG
processing using machine learning methods has gained importance, mainly by
utilizing deep learning methods on raw ECG signals. A major advantage of models
like convolutional neural networks (CNNs) is their ability to effectively
process biomedical imaging or signal data. However, this strength is tempered
by challenges related to their lack of explainability, the need for a large
amount of training data, and the complexities involved in adapting them for
unsupervised clustering tasks. In addressing these tasks, we aim to reintroduce
shallow learning techniques, including support vector machines and principal
components analysis, into ECG signal processing by leveraging their
semi-structured, cyclic form. To this end, we developed and evaluated a
transformation that effectively restructures ECG signals into a fully
structured format, facilitating their subsequent analysis using shallow
learning algorithms. In this study, we present this adaptive transformative
approach that aligns R-peaks across all signals in a dataset and resamples the
segments between R-peaks, both with and without heart rate dependencies. We
illustrate the substantial benefit of this transformation for traditional
analysis techniques in the areas of classification, clustering, and
explainability, outperforming commercial software for median beat
transformation and CNN approaches. Our approach demonstrates a significant
advantage for shallow machine learning methods over CNNs, especially when
dealing with limited training data. Additionally, we release a fully tested and
publicly accessible code framework, providing a robust alignment pipeline to
support future research, available at https://github.com/ imi-ms/rlign.
