This work investigates technics that allow for the automatic classification of normal vs abnormal heartbeats with the goal of assisting general practitioners. In fact, many different ECG waveform features have been proposed over the years as inputs to normal/abnormal heartbeat classifiers. However, there is a need for the formal comparison of the classification performances obtained when using these features, and more importantly their joint combinations, on a single common dataset. This study

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... hus investigates the classification of heartbeats as normal or abnormal using combinations of 5 different types of features and 2 classifiers. Two different supervised classifiers were used: a Multilayer Perceptron (MLP) and a Support Vector Machine (SVM). The best feature set in terms of the accuracy of classification was found to be the combination of the Hermite basis function expansion, the complete higher order statistics of the ECG waveform and the RR intervals. In fact, a classification accuracy of 94.6% was obtained with the MLP for this feature set while a near perfect accuracy of 99.1% was obtained with the SVM.