TY - JOUR
T1 - Detecting ventricular tachycardia and fibrillation by complexity measure
AU - Zhang, Xu Sheng
AU - Zhu, Yi Sheng
AU - Thakor, Nitish V.
AU - Wang, Zhi Zhong
N1 - Funding Information:
Prof. Zhu is a recipient of five awards from the National Science Foundation of China and another one from SNSF.
Funding Information:
Manuscript received January 10, 1997; revised September 23, 1998. The work of X.-S. Zhang and Y.-S. Zhu was supported by the National Natural Science Foundation of China under Grant 69671006. Asterisk indicates corresponding author. *X.-S. Zhang is with the Department of Biomedical Engineering, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China (e-mail: zhangx5@rpi.edu).
PY - 1999
Y1 - 1999
N2 - 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).
AB - 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).
KW - Arrhythmia detection
KW - Automatic external defibrillators
KW - Complexity measure
KW - Ventricular fibrillation
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U2 - 10.1109/10.759055
DO - 10.1109/10.759055
M3 - Article
C2 - 10230133
AN - SCOPUS:0033004249
SN - 0018-9294
VL - 46
SP - 548
EP - 555
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 5
ER -