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.
Original language | English |
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Pages (from-to) | 443-449 |
Number of pages | 7 |
Journal | Journal of Medical Engineering and Technology |
Volume | 31 |
Issue number | 6 |
DOIs | |
State | Published - 11 2007 |
Keywords
- Beat diagnosis
- Electrocardiogram (ECG)
- Premature ventricular contraction (PVC)