Key result
Review evaluates the effectiveness, generalizability, and shortcomings of machine learning models for cardiac arrhythmia detection.
Why the study?
Heart disease is rising and timely detection of cardiac arrhythmia is essential, prompting the need to thoroughly evaluate recent automated machine learning and deep learning ECG detection models.
This review evaluates the performance, generalizability, and interpretability of recent machine learning and deep learning models for automated ECG-based cardiac arrhythmia detection.
ML/DL arrhythmia detection models show promise yet lack generalizability and interpretability; leaves open standardized prospective validation before clinical adoption.
Heart disease is on the rise in both young and old people in today's modern culture, which is marked by elevated levels of stress, anxiety, and depression. In addition to being common, these illnesses carry serious dangers to life. Timely detection and prevention of heart problems is essential for a successful recovery. After extensive study on prognosis and prevention, cardiac arrhythmia becomes a major issue impacting a significant proportion of the population. Every day in medical institutions, a large amount of data is generated by the electrocardiogram (ECG), which is the most economical way to diagnose cardiac arrhythmia. number of automated models for identifying cardiac arrhythmia have been developed recently using machine learning and deep learning methods. This paper performs a thorough analysis of these latest developments, rating their effectiveness according to certain criteria such as applied settings, deployed datasets, input data changes, approaches, and outcomes attained. Apart from these dimensions, the review also takes generalizability and interpretability into account. It also discusses the shortcomings of the current research and suggests directions for further development. The significance of resolving these issues is highlighted by this work in order to improve the efficiency and usefulness of automated cardiac arrhythmia detection algorithms.
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Kumar et al. (2025) conducted a review in Cardiac arrhythmia. Automated machine learning and deep learning models was evaluated on Model effectiveness, generalizability, and interpretability. Automated machine learning and deep learning models for cardiac arrhythmia detection were reviewed to evaluate their effectiveness, generalizability, interpretability, and current shortcomings.
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