A novel real-time feature extraction method demonstrates high accuracy for QRS detection and moderate sensitivity for PVC identification in bedside monitors.
Supports bedside QRS monitoring; leaves open larger validation of PVC sensitivity.
This paper describes a fast and very efficient feature extraction method for discrimination of QRS and Premature Ventricular Contraction (PVC) beats in a microprocessor‐based bedside monitoring system. It converts each QRS and PVC beat into a positive‐pulse waveform by signal preprocessing. Two characteristic factors, the positive‐pulse and the pulse duration, are calculated when the onset and end points of each pulse have been detected by threshold detection. The prominent feature is extracted from a product of these two factors. This algorithm has been examined using 40 different patients’ electrocardiograph (ECG). The accuracy of QRS detection was 99.3 percent in the tests performed. The identification sensitivity of PVC was 81.2 percent with 18‐ different arrhythmia patients.
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Chang et al. (1987) studied this question.
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