Analysis and Classification of Cardiac Arrhythmia Using ECG Signals

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Population

3003 ECG beats (2101 used for training, 902 for testing) from an ECG dataset

Authors

Discussion

May support automated ECG classification research; leaves open prospective clinical validation before practice change.

Population

3003 ECG beats (2101 used for training, 902 for testing) from an ECG dataset

Intervention

Support Vector Machine (SVM) technique using LibSVM3.1

Outcome

Performance accuracy of arrhythmia classification into five categories (one normal, four arrhythmic)

A Support Vector Machine (SVM) technique can automatically classify ECG beats into normal and arrhythmic categories with high accuracy.

Bhardwaj et al. (2012) studied this question.

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