Automated analysis of wearable ECG: machine learning methods, preprocessing pipelines, and benchmark datasets

Wearable and mobile electrocardiography (ECG) has rapidly expanded access to rhythm monitoring outside of clinical settings, but the single- or few-lead nature of these recordings and their susceptibility to motion artefacts complicate clinical-grade interpretation. This review synthesises recent machine learning approaches for wearable ECG across three problem domains-signal-quality assessment (SQA), single-lead interpretation, and multi-lead interpretation-and covers preprocessing pipelines, quality-handling strategies, and 18 benchmark datasets. Across studies, no single architectural paradigm consistently outperforms others when evaluated fairly: classical feature-based methods remain competitive for rhythm tasks under resource constraints, while deep discriminative models show stronger cross-database generalisation when training data are sufficient. The most consistent predictor of reported performance is dataset characteristics-particularly scale, demographic composition, and whether signals were acquired from wearable devices in free-living conditions-rather than architectural choice. Three recurring evaluation deficiencies are identified: subject-level data leakage inflating classification metrics, SQA performance reported without acceptance rates, and reconstructed ECG leads validated solely by waveform similarity, without downstream diagnostic concordance. A structured comparative summary of all the methods reviewed is provided. The analysis clarifies that future progress depends primarily on evaluation rigour, representative datasets, and reproducible preprocessing rather than on architectural novelty alone.