Abstract

Describes a fast and very effective feature extraction technique for detection and discrimination of QRS on a microprocessor-based Holter ECG analysis system. The technique converts long term (up to 24 hours) recorded ECG into a positive waveform by signal preprocessing. Three characteristic factors, the duration, the areas, and the original slope of the positive waveform, are detected 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 three factors. This method has been examined using 10 different patients' ECG signals. The accuracy of QRS detection was 99.8 percent in validation. The identification sensitivity of PVC beats was 93.2 percent with 6 different arrhythmia patients. The method has also been implemented on a PC-486 based computer system. A three-channel 24-hour recorded ECG can be completely analyzed within 5 minutes for reporting the heart rate variations, ST level analysis, heartbeat classifications and arrhythmia analysis.

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