Machine learning approach to recognize ventricular arrhythmias using VMD based features

• Published in: Multidimensional systems and signal processing 2020, Vol.31 (1), p.49-71
• Main Authors: Mohanty, Monalisa, Biswal, Pradyut, Sabut, Sukanta
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Cardiac arrhythmia; Circuits and Systems; Classifiers; Data analysis; Decomposition; Electrical Engineering; Electrocardiography; Engineering; Feature extraction; Fibrillation; Machine learning; Signal,Image and Speech Processing; Support vector machines; Tachycardia; Ventricular fibrillation
• ISSN: 0923-6082; 1573-0824
• DOI: 10.1007/s11045-019-00651-w

The occurrence of life-threatening ventricular arrhythmias (VAs) such as Ventricular tachycardia (VT) and Ventricular fibrillation (VF) leads to sudden cardiac death which requires detection at an early stage. The main aim of this work is to develop an automated system using machine learning tool for accurate prediction of VAs that may reduce the mortality rate. In this paper, a novel method using variational mode decomposition (VMD) based features and C4.5 classifier for detection of ventricular arrhythmias is presented. The VMD model was used to decompose the electrocardiography (ECG) signals to extract useful informative features. The method was tested for ECG signals obtained from PhysioNet database. Two standard databases i.e. CUDB (Creighton University Ventricular Tachyarrhythmia Database) and VFDB (MIT-BIH Malignant Ventricular Ectopy Database) were considered for this work. A set of time–frequency features were extracted and ranked by the gain ratio attribute evaluation method. The ranked features are subjected to support vector machine (SVM) and C4.5 classifier for classification of normal, VT and VF classes. The best detection was obtained with sensitivity of 97.97%, specificity of 99.15%, and accuracy of 99.18% for C4.5 classifier with a 5 s data analysis window. These results were better than SVM classifier result having an average accuracy of 86.87%. Hence, the proposed method demonstrates the efficiency in detecting the life-threatening VAs and can serve as an assistive tool to clinicians in the diagnosis process.