Automatic classification of ECG beats using waveform shape and heart beat interval features

The paper presents the classification performance of an automatic classifier of the electrocardiogram (ECG) for the detection of normal, premature ventricular contraction and fusion beat types. Both linear discriminants and feedforward neural networks were considered for the classifier model. Features based on the ECG waveform shape and heart beat intervals were used as inputs to the classifiers. Data was obtained from the MIT-BIH arrhythmia database. Cross-validation was used to measure the classifier performance. A classification accuracy of 89% was achieved which is a significant improvement on previously published results.