Combining SVM and PSO for PVC Detection

This paper proposes a method for premature ventricular contraction detection. The method consist of three modules. Feature extraction module extracts ten electrocardiogram (ECG) morphological features and two timing interval features. Then a number of support vector machine (SVM) classifiers with different values of C and the GRBF kernel parameter, sigma, are designed and compared their ability for classification of three different classes of ECG signals. An overall classification accuracy of detection of 98.38% were achieved over nine files from the MIT/BIH arrhythmia database. The classification accuracy increased to 99.90% when particle swarm optimization (PSO) employed to find the best values of SVM parameters

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