Key result
Machine learning and swarm optimization algorithms show potential for ECG signal classification.
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Review summarizes swarm-optimized ML for ECG classification; leaves open prospective validation before clinical adoption.
Electrocardiogram (ECG), non-stationary signals, is extensively used to evaluate the rate and tuning of heartbeats. The main purpose of this paper is to provide an overview of utilizing machine learning and swarm optimization algorithms in ECG classification. Furthermore, feature extraction is the main stage in ECG classification to find a set of relevant features that can attain the best accuracy. Swarm optimization algorithm is combined with classifiers for the purpose of searching the best value of classification parameters that best fits its discriminant purpose. Finally, this paper introduces an ECG heartbeat classification approach based on the water wave optimization (WWO) and support vector machine (SVM). Published literature presented in this paper indicates the potential of ANN and SVM as a useful tool for ECG classification. Author strongly believes that this review will be quite useful to the researchers, scientific engineers working in this area to find out the relevant references.
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Hassanien et al. (2017) conducted a review in ECG classification. Machine learning and swarm optimization algorithms was evaluated. Machine learning and swarm optimization algorithms, particularly artificial neural networks and support vector machines, show potential as useful tools for ECG signal classification.
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