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.

翻译：心电图（ECG）记录长期以来在诊断不同心脏疾病中具有关键作用。近年来，利用机器学习方法进行自动ECG处理的研究日益重要，主要通过将深度学习方法应用于原始ECG信号。卷积神经网络（CNN）等模型的主要优势在于其能有效处理生物医学成像或信号数据。然而，这些优势因其可解释性不足、需要大量训练数据以及适应无监督聚类任务的复杂性等挑战而受到制约。针对这些任务，我们旨在通过利用ECG信号的半结构化周期特性，将浅层学习技术（包括支持向量机和主成分分析）重新引入ECG信号处理领域。为此，我们开发并评估了一种能够将ECG信号有效重构为完全结构化格式的转换方法，从而便于后续使用浅层学习算法进行分析。本研究提出了一种自适应转换方法，该方法通过对齐数据集中所有信号的R峰并对R峰间片段进行重采样（包括依赖心率与不依赖心率两种模式），显著提升了传统分析技术在分类、聚类和可解释性方面的性能，其表现优于商用软件的中位心搏转换方法和CNN方法。我们的方法证明了浅层机器学习方法相对于CNN的显著优势，尤其在训练数据有限的情况下。此外，我们发布了一个经过全面测试且可公开访问的代码框架，提供了一个稳健的对齐流程以支持未来研究，该框架可通过https://github.com/imi-ms/rlign获取。