A nonlinear trimmed moving average filter-based system can achieve over 99.5% accuracy in real-time QRS beat classification for identifying abnormal ventricular beats.
May enable real-time ventricular beat detection in monitoring devices; leaves open prospective clinical validation.
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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S.-W. Chen (2007) studied this question.
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