Empirical mode decomposition based ECG features in classifying and tracking ventricular arrhythmias. (September 2019)

• Record Type: Journal Article
• Authors: Hotradat, M.; Balasundaram, K.; Masse, S.; Nair, K.; Nanthakumar, K.; Umapathy, K.
• Is Part Of: Computers in biology and medicine. Volume 112(2019)
• Publication Date: 2019-09
• Subjects: Dynamic features -- Empirical mode decomposition -- Pattern classification -- Ventricular fibrillation -- Ventricular tachycardia; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.103379
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Ventricular arrhythmias (VA) are life-threatening pathophysiological conditions that seriously impact the normal functioning of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two well known types of VA. VF is the lethal of the VAs and could be characterized by its organizational progression over time. The success of cardiac resuscitation strongly depends on the type of VA, its evolution over time and response to therapy. Due to the time critical nature of VF, computationally efficient quantification of VAs and swift feedback are essential. This work attempted to arrive at computationally efficient and data-driven techniques based on Empirical Mode Decomposition for classifying and tracking VAs over time. The approaches are divided into two aims: (1) 'in-hospital' scenarios for characterizing the dynamics of VA episodes to assist clinicians in planning long-term therapy options, and (2) 'out-of-hospital' scenarios for providing near real-time feedback to detect/track the progression of VAs over time to assist medical personnel select/modify therapy options. Using an ECG database of 61 60-s VA segments obtained for classifying VT vs. VF and sub-classifying VF into organized VF (OVF) and disorganized VF (DVF), maximum classification accuracies of 96.7% (AUC = 0.993) and 87.2% (AUC = 0.968) were obtained for classifying VT vs. VF and OVF vs. DVF during 'in-hospital' analysis. Additionally, two near real-time approaches were presented for 'out-of-hospital' analysis where average accuracies of 71% and 73% were achieved for VT/VF and OVF/DVF classification, as well as demonstrating strong potential for monitoring VA progressions over time. Highlights: Computationally efficient Dynamic Features for Ventricular Arrhythmia Segregation. Instantaneous Features from Empirical Mode Decomposition Based Hilbert Spectrum. Ventricular Arrhythmia Trend Analysis Using Instantaneous Time-frequency Features.