Key result
A combined approach using support vector machines and a genetic algorithm distinguished four types of cardiac arrhythmias with 93% accuracy.
Why the study?
Does a combined SVM and genetic algorithm approach accurately classify cardiac arrhythmias from ECG signals?
Population
ECG signals for cardiac arrhythmia classification
Authors
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May aid ML-based ECG tools in research; leaves open prospective clinical validation before any practice change.
Does a combined SVM and genetic algorithm approach accurately classify cardiac arrhythmias from ECG signals?
A machine learning approach combining SVM and genetic algorithms can accurately classify four types of cardiac arrhythmias from ECG signals.
Nasiri et al. (2009) studied Cardiac arrhythmia. Support vector machine (SVM) combined with genetic algorithm was evaluated on Classification accuracy of four types of arrhythmias. A combined approach using support vector machines and a genetic algorithm distinguished four types of cardiac arrhythmias with 93% accuracy.
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