Key result
Machine learning models achieve ~99% accuracy in automated cardiac arrhythmia detection.
Why the study?
Manual analysis of ECG reports to detect cardiac arrhythmia is time-consuming, necessitating automated diagnosis systems that deliver quick and accurate results.
How do various machine learning algorithms perform in the automated detection of cardiac arrhythmias from electrocardiograms?
Systematic Review
How do various machine learning algorithms perform in the automated detection of cardiac arrhythmias from electrocardiograms?
This systematic survey highlights the performance and challenges of using machine learning algorithms for the automated and rapid detection of cardiac arrhythmias from ECGs.
Promising ML performance in arrhythmia detection warrants prospective clinical trials; leaves open routine adoption pending real-world validation.
A significant global worry is a substantial rise in cardiac arrhythmia cases brought on by improper food and lifestyle choices. Manual analysis of the report of the electrocardiogram to detect the presence of an anomaly is a time-consuming task. Hence, it is necessary to create an automated diagnosis system that can deliver results quickly and accurately. Numerous machine learning-based models were created by researchers working in this field to determine the severity of cardiac arrhythmias. This article provides an organized and thorough assessment of previous research in the field, with a particular emphasis on machine learning methods developed by different authors to detect cardiac arrhythmia. Additionally, covered is the performance analysis of the different algorithms. The difficulties associated with developing a model for cardiac arrhythmia and its potential future impact are finally examined in the conclusion section.
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Singh et al. (2023) conducted a systematic review in Cardiac Arrhythmia. Machine learning algorithms was evaluated on Classification accuracy. Machine learning models, particularly Support Vector Machines and Fuzzy Clustering Neural Networks, achieved up to 99.3% accuracy in the automated detection of cardiac arrhythmias.
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