A nonlinear trimmed moving averaging-based system with its application to real-time QRS beat classification.
In this paper, a real-time QRS beat classification system based on a nonlinear trimmed moving average filter is presented. This nonlinear system aims to identify abnormal beats of ventricular origin. The proposed beat classifier is designed to work in parallel with a real-time QRS detector, allowing the task of beat diagnosis to be performed immediately after a QRS complex is detected. Algorithm performance was evaluated against the ECG recordings drawn from the MIT-BIH arrhythmia database. Numerical results demonstrated that a beat classification rate of over 99.5% can be achieved by the algorithm.
Chen
S-W
SW
Department of Electronic Engineering, Chang Gung University, Kwei-Shan, Tao-Yuan, Taiwan, Republic of China. chensw@mail.cgu.edu.tw
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D008955:Models, Cardiovascular; D017711:Nonlinear Dynamics