Detecting ventricular tachycardia and fibrillation by complexity measure

Sinus rhythm (SR), ventricular tachycardia (VT) and ventricular fibrillatio n (VF) belong to different nonlinear physiological processes with different complexity, In this study, we present a novel, and computationally fast me thod 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 compari ng the raw electrocardiogram (ECG) data to a selected suitable threshold, T he complexity measure can be obtained from the 0-1 string only using two si mple 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 a nd irregularity, VF-filter leakage, and sequential hypothesis testing, the new algorithm is simple, computationally efficient, and well suited for rea l-time implementation in automatic external defibrillators (AED's).