Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods

作者：Alajlan Naif; Bazi Yakoub^*; Melgani Farid; Malek Salim; Bencherif Mohamed A

来源：Signal, Image and Video Processing, 2014, 8(5): 931-942.

DOI：10.1007/s11760-012-0339-8

• Premature ventricular contraction (PVC)
• Support vector machines (SVMs)
• Gaussian process classifiers (GPCs)
• Morphology
• Wavelet transform
• High-order statistics
• S transform

In this paper, we propose to investigate the capabilities of two kernel methods for the detection and classification of premature ventricular contractions (PVC) arrhythmias in Electrocardiogram (ECG signals). These kernel methods are the support vector machine and Gaussian process (GP). We propose to study these two classifiers with various feature representations of ECG signals, such as morphology, discrete wavelet transform, higher-order statistics, and S transform. The experimental results obtained on 48 records (i.e., 109,887 beats) of the MIT-BIH Arrhythmia database showed that for all feature representation adopted in this work, the GP detector trained only with 600 beats from PVC and Non-PVC classes can provide an overall accuracy and a sensitivity above 90 % on 20 records (i.e., 49,774 beats) and 28 records (i.e., 60,113 beats) seen and unseen, respectively, during the training phase.

• 出版日期2014-7