A classification scheme for ventricular arrhythmias using wavelets analysis

• Published in: Medical & biological engineering & computing Vol. 51; no. 1-2; pp. 153 - 164
• Main Authors: Balasundaram, K., Masse, S., Nair, K., Umapathy, K.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.02.2013; Springer Nature B.V
• Subjects: Ablation; Algorithms; Analysis; Bioengineering; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cardiac arrhythmia; Computer Applications; Decomposition; Electrocardiography; Human Physiology; Humans; Imaging; Methods; Original Article; Radiology; Sensors; Studies; Tachycardia, Ventricular - classification; Ventricular Fibrillation - classification; Wavelet Analysis; Wavelet transforms; Ventricular arrhythmia; Wavelet analysis; Feature extraction; Pattern classification; Singular value decomposition
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-012-0980-y

Identification and classification of ventricular arrhythmias such as rhythmic ventricular tachycardia (VT) and disorganized ventricular fibrillation (VF) are vital tasks in guiding implantable devices to deliver appropriate therapy in preventing sudden cardiac deaths. Recent studies have shown VF can exhibit strong regional organizations, which makes the overlap zone between the fast paced rhythmic VT and VF even more ambiguous. Considering that implantable cardioverter-defibrillator (ICD) are primarily rate dependent detectors of arrhythmias and that there may be patients who suffer from arrhythmias that fall in the overlap zone, it is essential to identify the degree of affinity of the arrhythmia toward VT or organized/disorganized VF. The method proposed in this work better categorizes the overlap zone using Wavelet analysis of surface ECGs. Sixty-three surface ECG signal segments from the MIT-BIH database were used to classify between VT, organized VF (OVF), and disorganized VF (DVF). A two-level binary classifier was used to first extract VT with an overall accuracy of 93.7 % and then the separation between OVF and DVF with an accuracy of 80.0 %. The proposed approach could assist clinicians to provide optimal therapeutic solutions for patients in the overlap zone of VT and VF.