ECG classification performance ECG database Frank lead ECG automatic classification automatic feature selection classifier model disease categories feature sets linear discriminants logistic discriminants multiple runs quadratic discriminants selected time-domain features seven-way accuracy standard cardiology features ten-fold cross-validation time-domain samples wavelet-based features electrocardiogram
deChazal, P., Reilly, R.B., Automatic classification of ECG beats using waveform shape and heart beat interval features: proceedings of the 2003 IEEE International Conference on Acoustics, Speech and Signal Processing, Hong Kong: IEEE, 2003, pp269-72
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.
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