Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network

Electrocardiogram (ECG) signal represents the electrical activity of the heart and playing an increasingly important role for practitioners to diagnose heart diseases. Widely available ECG data and machine learning algorithms present an opportunity to improve the accuracy of automated arrhythmia diagnosis. However, a comprehensive evaluation of morphological arrhythmias for the ECG analysis across a wide variety of diagnostic classes is still a complex task. This paper presents a generic morphological arrhythmias classification method designed for robust and accurate detection of the ECG heartbeat. In this method, the gray-level co-occurrence matrix (GLCM) is employed for features vector description because of its extraordinary statistical feature extraction ability. In addition, the convolutional neural network (CNN) approach is utilized to automatically classification from the generated 3D multi-scale GLCM. Obtained results from the experiments demonstrate that the proposed method in this paper is quite suitable for morphological arrhythmias detection. These findings demonstrate that the GLCM description can efficiently extract the shape features vector for a broad range of distinct arrhythmias from the lead ECGs with high diagnostic performance. The experimental results of the recognition for morphological arrhythmias show the feasibility and effectiveness of the proposed method and could be used to reduce the rate of the misdiagnosed computerized ECG interpretations.