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Updated: Jul 10, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
Published on: May 23, 2021
A nonlinear trimmed moving averaging-based system with its application to real-time QRS beat classification
1Department of Electronic Engineering, Chang Gung University, Kwei-Shan, Tao-Yuan, Taiwan, Republic of China. chensw@mail.cgu.edu.tw
Abstract:
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.
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