ECG arrhythmia classification using modified visual geometry group network (mVGGNet)

In this paper, the authors propose an improved convolutional neural network for automatic arrhythmia classification using Electro-Cardio-Gram (ECG) signal. It is essential to periodically monitor the heart beat arrhythmia to reduce the risk of death due to cardiovascular disease (CVD). The Visual Geometry Group network (VGGNet) is being widely used in computer vision problems. However, the same network cannot be used for classification of ECG beats as ECG signal is different from image signal in terms of dimensionality and inherent features. Thus, the authors investigated the effect of decreasing depth and width of a convolutional neural network in context of cardiac arrhythmia classification. In this paper, six configurations differing in depth and width are evaluated using benchmark MIT-BIH database. A deep network having thirteen convolution layers but with a smaller number of filters (reduced width) showed outstanding performance for the given problem. Based on the findings, the work is further extended to propose an improved convolutional neural network named as modified VGGNet (mVGGNet) for the task of ECG arrhythmia classification into four classes which are normal (N), ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB) and fusion beat (F). The hyper-parameters of the proposed architecture were optimized using sequential model based global optimization (SMBO) algorithm. The proposed architecture is evaluated using subjected-oriented patient-independent evaluation protocol. The performance is evaluated using five-fold cross-validation. The proposed mVGGNet achieved 98.79% and 99.16% accuracy for ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) classification respectively. The proposed method resulted in higher specificity and precision as compared to other state-of-the-art algorithms. Thus, it can be effectively used for ECG arrhythmia classification.