title: Detecting ventricular tachycardia and fibrillation by complexity measure.
abstract: Sinus rhythm (SR), ventricular tachycardia (VT) and ventricular fibrillation (VF) belong to different nonlinear physiological processes with different complexity. In this study, we present a novel, and computationally fast method to detect VT and VF, which utilizes a complexity measure suggested by Lempel and Ziv [1]. For a specific window length (i.e., the length of data segment to be analyzed), the method first generates a 0-1 string by comparing the raw electrocardiogram (ECG) data to a selected suitable threshold. The complexity measure can be obtained from the 0-1 string only using two simple operations, comparison and accumulation. When the window length is 7 s, the detection accuracy for each of SR, VT, and VF is 100% for a test set of 204 body surface records (34 SR, 85 monomorphic VT, and 85 VF). Compared with other conventional time- and frequency-domain methods, such as rate and irregularity, VF-filter leakage, and sequential hypothesis testing, the new algorithm is simple, computationally efficient, and well suited for real-time implementation in automatic external defibrillators (AED's).
authors.affiliation: Department of Biomedical Engineering, College of Life Science and Biotechnology, Shanghai Jiao Tong University, China. zhangx5@rpi.edu
authors.forename: X S
authors.initials: XS
authors.lastname: Zhang
authors.forename: Y S
authors.initials: YS
authors.lastname: Zhu
authors.forename: N V
authors.initials: NV
authors.lastname: Thakor
authors.forename: Z Z
authors.initials: ZZ
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D004554:Electric Countershock; D004562:Electrocardiography; D006801:Humans; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000628:therapy; D014693:Ventricular Fibrillation / Q000175:diagnosis* / Q000628:therapy