A novel approach for assessing quality of electrocardiogram signal by integrating multi-scale temporal features_Journal of Biomedical Engineering

Authors：

CHEN Cheng ^1,2,3 , ZHANG Aihua ^1,2,3 ,  MA Yurun ^1,2,3 , QI Yusheng ^1,2,3 , LI Jiaqi ^1,2,3

1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, P. R. China;
2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, P. R. China;
3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, P. R. China;

Corresponding author：

MA Yurun, Email: mayr@lut.edu.cn

Keywords：

12-lead electrocardiogram; Inter-lead information; Multi-scale features; Feature fusion; Quality assessment

DOI：

10.7507/1001-5515.202402026

Video：

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During long-term electrocardiogram (ECG) monitoring, various types of noise inevitably become mixed with the signal, potentially hindering doctors' ability to accurately assess and interpret patient data. Therefore, evaluating the quality of ECG signals before conducting analysis and diagnosis is crucial. This paper addresses the limitations of existing ECG signal quality assessment methods, particularly their insufficient focus on the 12-lead multi-scale correlation. We propose a novel ECG signal quality assessment method that integrates a convolutional neural network (CNN) with a squeeze and excitation residual network (SE-ResNet). This approach not only captures both local and global features of ECG time series but also emphasizes the spatial correlation among ECG signals. Testing on a public dataset demonstrated that our method achieved an accuracy of 99.5%, sensitivity of 98.5%, and specificity of 99.6%. Compared with other methods, our technique significantly enhances the accuracy of ECG signal quality assessment by leveraging inter-lead correlation information, which is expected to advance the development of intelligent ECG monitoring and diagnostic technology.

Citation： CHEN Cheng, ZHANG Aihua, MA Yurun, QI Yusheng, LI Jiaqi. A novel approach for assessing quality of electrocardiogram signal by integrating multi-scale temporal features. Journal of Biomedical Engineering, 2024, 41(6): 1169-1176. doi: 10.7507/1001-5515.202402026 Copy

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2.	Jin J. Screening for cardiovascular disease risk with ECG. JAMA, 2018, 319(22): 2346.
3.	Liu C, Zhang X, Zhao L, et al. Signal quality assessment and lightweight QRS detection for wearable ECG SmartVest system. IEEE Internet of Things Journal, 2019, 6(2): 1363-1374.
4.	Halcox J P J, Wareham K, Cardew A, et al. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation, 2017, 136(19): 1784-1794.
5.	Satija U, Ramkumar B, Manikandan M S. A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal//2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016: 779-783.
6.	Berkaya S K, Uysal A K, Gunal E S, et al. A survey on ECG analysis. Biomedical Signal Processing and Control, 2018, 43: 216-235.
7.	Zhang Y, Wei S, Zhang L, et al. A signal quality assessment method for mobile ECG using multiple features and fuzzy support vector machine//2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016: 966-971.
8.	Xia Y, Jia H. ECG quality assessment based on multi-feature fusion//2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017: 672-676.
9.	Prabhakararao E, Dandapat S. Automatic quality estimation of 12-lead ECG for remote healthcare monitoring systems//2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2018: 554-559.
10.	Sannino G, de Pietro G. A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Future Generation Computer Systems, 2018, 86: 446-455.
11.	Liu G, Han X, Tian L, et al. ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features. Computer Methods and Programs in Biomedicine, 2021, 208: 106269.
12.	Jin Y, Li Z, Qin C, et al. A novel attentional deep neural network-based assessment method for ECG quality. Biomedical Signal Processing and Control, 2023, 79: 104064.
13.	Liu Y, Zhang H, Zhao K, et al. An automatic ECG signal quality assessment method based on resnet and self-attention. Applied Sciences, 2023, 13(3): 1313.
14.	Morgado E, Alonso-Atienza F, Santiago-Mozos R, et al. Quality estimation of the electrocardiogram using cross-correlation among leads. Biomedical Engineering Online, 2015, 14: 59.
15.	Silva I, Moody G B, Celi L. Improving the quality of ECGs collected using mobile phones: The Physionet/Computing in Cardiology Challenge 2011//2011 Computing in Cardiology. IEEE, 2011: 273-276.
16.	Tan H, Lai J, Liu Y, et al. Neural architecture search for real-time quality assessment of wearable multi-lead ECG on mobile devices. Biomedical Signal Processing and Control, 2022, 74: 103495.
17.	Qin C, Xiao D, Tao J, et al. Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring. Measurement, 2022, 194: 111090.
18.	Hu J, Shen L, Sun G. Squeeze-and-excitation networks// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132-7141.
19.	Zhang J, Wang L, Zhang W, et al. A signal quality assessment method for electrocardiography acquired by mobile device//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018: 1-3.
20.	Hayn D, Jammerbund B, Schreier G. QRS detection based ECG quality assessment. Physiological Measurement, 2012, 33(9): 1449-1461.
21.	Liu C, Li P, Zhao L, et al. Real-time signal quality assessment for ECGs collected using mobile phones//2011 Computing in Cardiology. IEEE, 2011: 357-360.
22.	Maan A C, van Zwet E W, Man S C, et al. Assessment of signal quality and electrode placement in ECGs using a reconstruction matrix//2011 Computing in Cardiology. IEEE, 2011: 289-292.
23.	Johannesen L, Galeotti L. Automatic ECG quality scoring methodology: mimicking human annotators. Physiological Measurement, 2012, 33(9): 1479-1489.
24.	Shahriari Y, Fidler R, Pelter M M, et al. Electrocardiogram signal quality assessment based on structural image similarity metric. IEEE Transactions on Biomedical Engineering, 2018, 65(4): 745-753.

1. Maron B J, Friedman R A, Kligfield P, et al. Assessment of the 12-lead ECG as a screening test for detection of cardiovascular disease in healthy general populations of young people (12–25 years of age): a scientific statement from the American Heart Association and the American College of Cardiology. Circulation, 2014, 130(15): 1303-1334.
2. Jin J. Screening for cardiovascular disease risk with ECG. JAMA, 2018, 319(22): 2346.
3. Liu C, Zhang X, Zhao L, et al. Signal quality assessment and lightweight QRS detection for wearable ECG SmartVest system. IEEE Internet of Things Journal, 2019, 6(2): 1363-1374.
4. Halcox J P J, Wareham K, Cardew A, et al. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation, 2017, 136(19): 1784-1794.
5. Satija U, Ramkumar B, Manikandan M S. A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal//2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016: 779-783.
6. Berkaya S K, Uysal A K, Gunal E S, et al. A survey on ECG analysis. Biomedical Signal Processing and Control, 2018, 43: 216-235.
7. Zhang Y, Wei S, Zhang L, et al. A signal quality assessment method for mobile ECG using multiple features and fuzzy support vector machine//2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016: 966-971.
8. Xia Y, Jia H. ECG quality assessment based on multi-feature fusion//2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017: 672-676.
9. Prabhakararao E, Dandapat S. Automatic quality estimation of 12-lead ECG for remote healthcare monitoring systems//2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2018: 554-559.
10. Sannino G, de Pietro G. A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Future Generation Computer Systems, 2018, 86: 446-455.
11. Liu G, Han X, Tian L, et al. ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features. Computer Methods and Programs in Biomedicine, 2021, 208: 106269.
12. Jin Y, Li Z, Qin C, et al. A novel attentional deep neural network-based assessment method for ECG quality. Biomedical Signal Processing and Control, 2023, 79: 104064.
13. Liu Y, Zhang H, Zhao K, et al. An automatic ECG signal quality assessment method based on resnet and self-attention. Applied Sciences, 2023, 13(3): 1313.
14. Morgado E, Alonso-Atienza F, Santiago-Mozos R, et al. Quality estimation of the electrocardiogram using cross-correlation among leads. Biomedical Engineering Online, 2015, 14: 59.
15. Silva I, Moody G B, Celi L. Improving the quality of ECGs collected using mobile phones: The Physionet/Computing in Cardiology Challenge 2011//2011 Computing in Cardiology. IEEE, 2011: 273-276.
16. Tan H, Lai J, Liu Y, et al. Neural architecture search for real-time quality assessment of wearable multi-lead ECG on mobile devices. Biomedical Signal Processing and Control, 2022, 74: 103495.
17. Qin C, Xiao D, Tao J, et al. Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring. Measurement, 2022, 194: 111090.
18. Hu J, Shen L, Sun G. Squeeze-and-excitation networks// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132-7141.
19. Zhang J, Wang L, Zhang W, et al. A signal quality assessment method for electrocardiography acquired by mobile device//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018: 1-3.
20. Hayn D, Jammerbund B, Schreier G. QRS detection based ECG quality assessment. Physiological Measurement, 2012, 33(9): 1449-1461.
21. Liu C, Li P, Zhao L, et al. Real-time signal quality assessment for ECGs collected using mobile phones//2011 Computing in Cardiology. IEEE, 2011: 357-360.
22. Maan A C, van Zwet E W, Man S C, et al. Assessment of signal quality and electrode placement in ECGs using a reconstruction matrix//2011 Computing in Cardiology. IEEE, 2011: 289-292.
23. Johannesen L, Galeotti L. Automatic ECG quality scoring methodology: mimicking human annotators. Physiological Measurement, 2012, 33(9): 1479-1489.
24. Shahriari Y, Fidler R, Pelter M M, et al. Electrocardiogram signal quality assessment based on structural image similarity metric. IEEE Transactions on Biomedical Engineering, 2018, 65(4): 745-753.

Journal of Biomedical Engineering

A novel approach for assessing quality of electrocardiogram signal by integrating multi-scale temporal features

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