Key result
Hybrid decision tree and ANN algorithm achieves ~99.9% QRS detection sensitivity.
Why the study?
Achieving low-error QRS detection without increasing computation remains a major challenge for real-time healthcare monitoring and diagnostic applications.
Population
ECG signals from the MIT-BIH Arrhythmia Database
Design
Algorithm development and validation study
Authors
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Hybrid QRS detector may aid real-time wearable monitoring; leaves open prospective clinical validation before adoption.
A novel hybrid decision tree and ANN-based QRS detection algorithm demonstrates high sensitivity and predictivity with low error rates for real-time ECG monitoring.
Khalaf et al. (2020) studied Arrhythmia (ECG signal processing) (n=109,494). Hybrid decision tree and artificial neural network QRS detection algorithm vs. Existing QRS detection algorithms was evaluated on QRS detection sensitivity. The proposed hybrid decision tree and artificial neural network algorithm achieved a QRS detection sensitivity of 99.88% and positive predictivity of 99.89% with a low detection error rate of 0.22%.
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