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Premature Ventricular Contraction Arrhythmia Detection and Classification with Gaussian Process and S Transform

This paper presents an efficient Bayesian classification system based on Gaussian process classifiers (GPC) for detecting premature ventricular contraction (PVC) beats in electrocardiographic (ECG) signals. GPC have the advantage over SVM classifiers in that the parameters of its kernel are automatically selected according to the Bayesian estimation procedure based on Laplace approximation. We also propose to feed the classifier with different representations of the ECG signals based on morphology, discrete wavelet transform, and S-transform. The latter representation has never been used for ECG signals before. The experimental results obtained on 48 records (i.e., 109887 heart beats) of the MIT-BIH arrhythmia database showed that for all feature representations adopted in this work, the proposed GP classifier combined with the S-transform and trained with only 600 beats from PVC and Non-PVC classes can provide an overall accuracy and a sensitivity above 96% on the whole 48 recordings.