Key result
The proposed genetic algorithm-based adaptive filter for real-time ECG QRS detection achieved a sensitivity of 98.97% and positive predictivity of 99.70% on the MIT-BIH Arrhythmia database.
Why the study?
Does a genetic algorithm-based adaptive filter accurately detect QRS complexes in real-time ECG monitoring?
Does a genetic algorithm-based adaptive filter accurately detect QRS complexes in real-time ECG monitoring?
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A novel genetic algorithm-based adaptive filter demonstrates high sensitivity and positive predictivity for real-time QRS complex detection in ECG signals.
Real-time detection of QRS complexes is quintessential for continuous electrocardiograph (ECG) monitoring. In this paper, a real-time ECG peaks detection algorithm is proposed. The paper proposes a new genetic algorithm (GA) based adaptive filter (GA-AF) and an effective real-time peak detection algorithm, which is coupled with search-back mechanism for missed peaks. The proposed methodology is tested on the standard MIT-BIH Arrhythmia database. The GA-AF based QRS detector gives good detection performance by taking an average duration of 0.3s for processing a signal block of 0.83s duration. The proposed methodology achieves sensitivity (%Se) and positive predictivity (%P+) of 98.97% and 99.70% respectively.
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Jain et al. (2017) studied Arrhythmia. Genetic algorithm (GA) based adaptive filter (GA-AF) and real-time peak detection algorithm was evaluated on Sensitivity (%Se) and positive predictivity (%P+) for QRS detection. The proposed genetic algorithm-based adaptive filter for real-time ECG QRS detection achieved a sensitivity of 98.97% and positive predictivity of 99.70% on the MIT-BIH Arrhythmia database.
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