Robust QRS complex detection in noisy electrocardiogram based on underdamped periodic stochastic resonance

Zheng Guo; Siqi Li; Kaicong Chen; Xuehui Zang; Zheng Guo; Siqi Li; Kaicong Chen; Xuehui Zang

doi:10.3934/bioeng.2023018

AIMS Bioengineering

2023, Volume 10, Issue 3: 283-299. doi: 10.3934/bioeng.2023018

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Research article

Robust QRS complex detection in noisy electrocardiogram based on underdamped periodic stochastic resonance

Zheng Guo ^{1
,
,},
Siqi Li ²,
Kaicong Chen ³,
Xuehui Zang ^{1
,
,}

1.
Orthopedics Department of The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan 528042, China
2.
Advanced Research Center, GD Midea Equipment Co.,Ltd, Foshan 528000, China
3.
Cardiovascular Department of The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan 528042, China
These authors contributed equally to this work and should be regarded as co-first authors.

Received: 05 July 2023 Revised: 27 August 2023 Accepted: 04 September 2023 Published: 15 September 2023

Robust QRS detection is crucial for accurate diagnosis and monitoring of cardiovascular diseases. During the detection process, various types of noise and artifacts in the electrocardiogram (ECG) can degrade the accuracy of algorithm. Previous QRS detectors have employed various filtering methods to minimize the negative impact of noise. However, their performance still significantly deteriorates in large-noise environments. To further enhance the robustness of QRS detectors on noisy electrocardiograms (ECGs), we proposed a QRS detection algorithm based on an underdamped. This method utilizes the period nonlinearity-induced stochastic resonance to enhance QRS complexes while suppressing noise and non-QRS components in the ECG. In contrast to neural network-based algorithms, our proposed algorithm does not rely on large datasets or prior knowledge. Through testing on three widely used ECG datasets, we demonstrated that the proposed algorithm achieves state-of-the-art detection performance. Furthermore, compared to traditional stochastic resonance-based method, our algorithm has increased noise robustness by 25% to 100% across various real-world environments. This enables the proposed method to maintain its optimal performance within a certain range even in the presence of additional injected noise, thus providing an excellent approach for robust QRS detection in noisy ECGs.
- noisy ECG,
- QRS detection,
- R peak localization,
- periodic nonlinearity,
- stochastic resonance
Citation: Zheng Guo, Siqi Li, Kaicong Chen, Xuehui Zang. Robust QRS complex detection in noisy electrocardiogram based on underdamped periodic stochastic resonance[J]. AIMS Bioengineering, 2023, 10(3): 283-299. doi: 10.3934/bioeng.2023018

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Abstract

Robust QRS detection is crucial for accurate diagnosis and monitoring of cardiovascular diseases. During the detection process, various types of noise and artifacts in the electrocardiogram (ECG) can degrade the accuracy of algorithm. Previous QRS detectors have employed various filtering methods to minimize the negative impact of noise. However, their performance still significantly deteriorates in large-noise environments. To further enhance the robustness of QRS detectors on noisy electrocardiograms (ECGs), we proposed a QRS detection algorithm based on an underdamped. This method utilizes the period nonlinearity-induced stochastic resonance to enhance QRS complexes while suppressing noise and non-QRS components in the ECG. In contrast to neural network-based algorithms, our proposed algorithm does not rely on large datasets or prior knowledge. Through testing on three widely used ECG datasets, we demonstrated that the proposed algorithm achieves state-of-the-art detection performance. Furthermore, compared to traditional stochastic resonance-based method, our algorithm has increased noise robustness by 25% to 100% across various real-world environments. This enables the proposed method to maintain its optimal performance within a certain range even in the presence of additional injected noise, thus providing an excellent approach for robust QRS detection in noisy ECGs.

Conflict of interest

The authors declare no conflict of interest.

Author Contributions:

The first two authors, Zheng Guo and Siqi Li, contributed equally in the conceptualization, methodology, software, writing (original draft preparation). Kaicong Chen contributed in the validation, visualization, writing (review and editing). Xuehui Zang contributed in validation, formal analysis, and supervision. All authors have read and agreed to the published version of the manuscript.

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