A combined approach using support vector machines and a genetic algorithm distinguished four types of cardiac arrhythmias with 93% accuracy.
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method combines both support vector machine (SVM) and genetic algorithm approaches. First, twenty two features from electrocardiogram signal are extracted. These features are obtained semiautomatically from time-voltage of R, S, T, P, Q features of an Electro Cardiagram signals. Genetic algorithm is used to improve the generalization performance of the SVM classifier. In order to do this, the design of the SVM classifier is optimized by searching for the best value of the parameters that tune its discriminate function, and looking for the best subset of features that optimizes the classification fitness function. Experimental results demonstrate that the approach adopted better classifies ECG signals. Four types of arrhythmias were distinguished with 93% accuracy.
Nasiri et al. (Thu,) conducted a other in 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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