Chen S-W
Department of Electronic Engineering, Chang Gung University, Kwei-Shan, Tao-Yuan, Taiwan, Republic of China.
J Med Eng Technol. 2007 Nov-Dec;31(6):443-9. doi: 10.1080/03091900701234267.
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.
本文提出了一种基于非线性修剪移动平均滤波器的实时QRS波分类系统。该非线性系统旨在识别室性起源的异常搏动。所提出的搏动分类器设计为与实时QRS波检测器并行工作，使得在检测到QRS波群后能够立即执行搏动诊断任务。根据从麻省理工学院-贝斯以色列女执事医疗中心心律失常数据库获取的心电图记录对算法性能进行了评估。数值结果表明，该算法可实现超过99.5%的搏动分类率。
