Key result
QRS detection algorithm using Shannon energy envelope and Hilbert transform achieves ~99.7% average accuracy.
Why the study?
Existing QRS complex detection algorithms are greatly affected by ECG signal quality, with some unable to work properly under poor signal quality.
Does the proposed QRS detection algorithm based on Shannon energy envelope and Hilbert transform accurately detect QRS complexes in ECG signals?
Does the proposed QRS detection algorithm based on Shannon energy envelope and Hilbert transform accurately detect QRS complexes in ECG signals?
A novel QRS complex detection algorithm using Shannon energy envelope and Hilbert transform demonstrates high accuracy and robustness, even under complex interference.
QRS complex detection plays an important role in electrocardiogram (ECG) automatic analysis. The accuracy and robustness of the detection algorithm greatly affect its practicability. However, the existing detection algorithms are greatly affected by ECG signal quality, and some detection algorithms cannot even work properly due to the poor signal quality. In this paper, a robust QRS complex detection algorithm is proposed based on Shannon energy envelope and Hilbert transform. The detection algorithm extracts the Shannon energy envelope of the preprocessed ECG signal, performs Hilbert transform on the envelope signal, then detects the suspected [Formula: see text]-peaks on the envelope by detecting the position of zero pass and screens the real [Formula: see text]-peaks by using a combination of ECG refractory period and backtracking mechanism. The proposed detection algorithm is validated using MIT-BIH Arrhythmia Database, and achieves the average detection accuracy of 99.69%, sensitivity of 99.81% and positive predictivity of 99.88%. Experimental results show that the proposed detection algorithm can still detect QRS complex correctly under complex interference, and the performance of the algorithm is hardly affected.
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XU et al. (2022) studied ECG QRS complex detection. QRS complex detection algorithm based on Shannon energy envelope and Hilbert transform was evaluated on Detection accuracy, sensitivity, and positive predictivity. A robust QRS complex detection algorithm based on Shannon energy envelope and Hilbert transform achieved an average detection accuracy of 99.69%, sensitivity of 99.81%, and positive predictivity of 99.88%.
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