Article

Evaluation of Cascade Forests towards Interpretable ECG Arrhythmia Classification: Performance, Interpretability, and Deployment Trade-offs

2026-03-08

Abstract excerpt

<title>Abstract</title> <p>Background Deep learning models achieve high accuracy in electrocardiogram (ECG) arrhythmia classification but pose critical barriers to clinical deployment: opaque decision-making processes that resist clinical validation, substantial computational requirements that necessitate specialised hardware, and training instability that requires extensive expertise. This study provides a rigo...

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Literature Corpus work: e92a3350-37fc-5d67-a175-3cac2ba47871
DOI: 10.21203/rs.3.rs-8950285/v1

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