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Arrhythmia classification using morphological features and probabilistic neural networks

Arrhythmia can be detected by carefully studying the electrocardiogram (ECG) and the distortions in the QRS complex. Since the appearance of the distorted beats, the indicators of arrhythmia, may occur randomly with respect to time and span over a large time interval, an automated classification mechanism may reduce the tedium in identifying and isolating these beats. This paper proposes an arrhythmia classifier based on probabilistic neural networks. The data is derived from MIT-BIH arrhythmia database. The classifier is designed to classify ten different types of beats, where the difference is based on morphology of the beat. Ten statistical morphological parameters are computed from the training dataset and they form the feature vector for the PNN training. The proposed classifier performs quite well with an average classification accuracy of 98.1%, average sensitivity of 0.9810, average specificity of 0.9978, average positive prediction rate as 0.981, average false prediction rate of 0.002 and average classification rate of 0.9962. The main advantage of using PNN is that it requires no training and a new class category can be added without major modifications to the network.