Detecting ventricular tachycardia and fibrillation by complexity measure

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).