Key result
This review examines several recently proposed ECG classification models, assessing them based on statistical parameters like computational delay, accuracy, and complexity to guide clinical deployment.
Why the study?
Because a wide range of ECG classification algorithms exist with varying complexity, accuracy, and deployment costs, it is unclear which models best fit specific applications.
This review provides a comparative analysis of various ECG classification models to guide researchers in selecting the most appropriate algorithms for clinical deployment.
Authors
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ECG model selection remains uncertain for clinicians; leaves open standardized prospective trials to identify optimal algorithms.
Jaisinghani et al. (2022) conducted a review in Heart conditions (atrial fibrillation, arrhythmia, myopathy, heart failure). ECG classification models was evaluated on Computational delay, complexity of deployment, classification accuracy, precision, and number of heart diseases covered. This review examines several recently proposed ECG classification models, assessing them based on statistical parameters like computational delay, accuracy, and complexity to guide clinical deployment.
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