An approach to detect QRS complex using backpropagation neural network
Backpropagation Neural Network is used to learn the characteristics of R peak to detect QRS complex. This allows R peak to be differentiated from large peaked T and P waves with higher accuracy and minimizes the problem associated with the noises in the ECG signal includes power line interference, m...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2006
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/36663/ http://irep.iium.edu.my/36663/ http://irep.iium.edu.my/36663/1/c-9_523-116.pdf |
| Summary: | Backpropagation Neural Network is used to learn the characteristics of R peak to detect QRS complex. This allows R peak to be differentiated from large peaked T and P waves with higher accuracy and minimizes the problem associated with the noises in the ECG signal includes power line interference, motion artifacts, baseline drift, ECG amplitude modulation and other composite noises. The features that trains the network includes amplitude, differentiation value, duration exceed threshold, RR interval and crossing-zero. The performance was tested and resulting in accuracy to detect the correct positive peak was 91.16%. |
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