Key result
The Particle Swarm Optimization-adapted Pan-Tompkins algorithm achieved an overall accuracy of 99.83% (error rate 0.17%) for QRS detection, outperforming the original algorithm.
Why the study?
The Pan-Tompkins algorithm relies on empirically selected parameters, which could achieve better performance if optimized.
Does optimizing the Pan-Tompkins algorithm parameters using Particle Swarm Optimization improve QRS detection accuracy in ECG signals?
Does optimizing the Pan-Tompkins algorithm parameters using Particle Swarm Optimization improve QRS detection accuracy in ECG signals?
Absolute Event Rate: 0.17% vs 0.68%
Optimizing the parameters of the Pan-Tompkins algorithm using Particle Swarm Optimization significantly improves QRS detection accuracy while maintaining computational efficiency suitable for real-time applications.
The QRS detection is a crucial step in ECG signal analysis; it has a great impact on the beats segmentation and in the final classification of the ECG signal. The Pan-Tompkins is one of the first and best-performing algorithms for QRS detection. It performs filtering for noise suppression, differentiation for slope dominance, and thresholding for decision making. All of the parameters of the Pan-Tompkins algorithm are selected empirically. However, we think that the Pan-Tompkins method can achieve better performance if the parameters were optimized. Therefore, we propose an adaptive algorithm that looks for the best set of parameters that improves the Pan-Tompkins algorithm performance. For this purpose, we formulate the parameter design as an optimization problem within a particle swarm optimization framework. Experiments conducted on the 24 hours recording of the MIT/BIH arrhythmia benchmark dataset achieved an overall accuracy of 99.83% which outperforms the state-of-the-art time-domain algorithms
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Belkadi et al. (2020) studied Arrhythmia (ECG signal analysis) (n=47). Particle Swarm Optimization (PSO) of Pan-Tompkins algorithm vs. Original Pan-Tompkins algorithm and other state-of-the-art time-domain algorithms was evaluated on QRS detection error rate. The Particle Swarm Optimization-adapted Pan-Tompkins algorithm achieved an overall accuracy of 99.83% (error rate 0.17%) for QRS detection, outperforming the original algorithm.
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