Key result
ML arrhythmia detection requires standardized evaluation including inter-patient testing and addressing class imbalance.
Why the study?
Arrhythmias can lead to sudden cardiac arrest, driving the need for early detection via automated classification techniques, and a comprehensive review of existing methods is needed to guide prospective research.
What are the existing machine-learning and deep-learning techniques for automated cardiac arrhythmia detection using ECG signals?
What are the existing machine-learning and deep-learning techniques for automated cardiac arrhythmia detection using ECG signals?
This review highlights the current state of machine-learning and deep-learning techniques for ECG-based arrhythmia detection, emphasizing methodological challenges like class imbalance and dataset partitioning.
Automated ECG arrhythmia detectors should not yet guide care; leaves open need for standardized inter-patient validation and imbalance handling in future models.
Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.
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Chandan Kumar Jha (2024) conducted a review in Cardiac arrhythmias. Automated arrhythmia detection techniques was evaluated. Automated arrhythmia detection techniques using machine-learning and deep-learning require standardized evaluation paradigms, including inter-patient testing and addressing class-imbalance.
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