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.