ECG signal classification based on DWT denoising and XGBoost

Research Article
Open access

ECG signal classification based on DWT denoising and XGBoost

Xinyi Yu 1*
  • 1 Department of Statistics, Nankai University    
  • *corresponding author goodyizai@163.com
Published on 12 October 2024 | https://doi.org/10.54254/2755-2721/95/2024BJ0057
ACE Vol.95
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-641-9
ISBN (Online): 978-1-83558-642-6

Abstract

Electrocardiogram signal (ECG) can directly reflect the health status of the heart, and is an important basis for prevention and treatment of heart disease. In order to realize ECG signal classification effectively, an ECG signal classification method based on discrete wavelet transform and Xgboost is proposed in this paper, which improves the accuracy of ECG signal classification. Specifically, we first divide, select and downsample the heart beat of the data, and then use the discrete wavelet transform to reduce the noise of the data set to improve the signal to noise ratio. Finally, we use Xgboost algorithm as the classifier to classify the data, and get 98.7% accuracy rate on the test set. In each module, we carried out comparative experiments to verify the correctness and rigor of our method. In addition, in order to make up for the lack of interpretability of traditional machine learning methods, we defined the importance of each feature according to the information gain generated by different features to the model during the training of XGBoost, and then got the key bands that should be paid attention to when distinguishing heart beats, which improved the interpretability of the model. It also provides a scientific basis for the classification of ECG signals and practical medical work.

Keywords:

Electrocardiogram, Discrete Wavelet Transform, XGBoost, machine learning

Yu,X. (2024). ECG signal classification based on DWT denoising and XGBoost. Applied and Computational Engineering,95,57-67.
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References

[1]. Guo Jianning and Li Wei. “Analysis of common cardiovascular and cerebrovascular diseases in elderly emergency patients”. In: Electronic Journal of Integrated Traditional and Western Medicine Cardiovascular Diseases 3.35 (2015), p. 2.

[2]. Liang Yisong. “Arrhythmia classification and signal time scale based on Deep learning”. MA thesis. Shandong University, 2024.

[3]. Yun-Chi Yeh, Che Chiou, and Lin Hong- Jhih. “Analyzing ECG for cardiac arrhythmia using cluster analysis”. In: Expert Systems with Applications: An International Journal 39 (Jan. 2012), pp. 1000–1010. DOI: 10.1016/j.eswa.2011.07.101.

[4]. Taiyong Li and Min Zhou. “ECG Classification Using Wavelet Packet Entropy and Random Forests”. In: Entropy 18 (Aug. 2016), p. 285. DOI: 10.3390/e18080285.

[5]. Ramachandran Varatharajan, Gunasekaran Manogaran, and Priyan M K. “A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing”. In: Multimedia Tools and Applications 77 (Nov. 2017). DOI: 10.1007/s11042-017-5318-1.

[6]. Liu Shu et al. “Ecg signal classification based on bispectral and spectral features”. In: Electronic Science and Technology (2021).

[7]. Serkan Kiranyaz, Turker Ince, and Moncef Gabbouj. “Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks”. In: IEEE Transactions on Biomedical Engineering 63.3 (2016), pp. 664–675. DOI: 10.1109/TBME.2015.2468589.

[8]. Chen Siyu. “Research on ECG signal denoising, Wave group detection and arrhythmia recognition algorithm”. MA thesis. Place of publication unknown]: Nanjing University of Finance and Economics, 2024.

[9]. Zhu Jinling. “Application of wavelet threshold denoising technology in ECG signal processing”. In: China High-Tech (2022), pp. 88–89. ISSN: 2096-4137. DOI: 10.13535/j.cnki.10- 1507/n.2022.04.36.

[10]. Song Xiguo and Deng Qinkai. “Understanding and application of MIT-BIH arrhythmia database”. In: Chinese Journal of Medical Physics 21.4 (2004), p. 3.

[11]. Cheng Xiangqian. “ECG signal classification based on fusion of CNN and SVR evidence theory”. MA thesis. Shandong University of Science and Technology, 2021.

Cite this article

Yu,X. (2024). ECG signal classification based on DWT denoising and XGBoost. Applied and Computational Engineering,95,57-67.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

ISBN：978-1-83558-641-9(Print) / 978-1-83558-642-6(Online)
Editor：Alan Wang, Roman Bauer
Conference website: https://2024.confcds.org/
Conference date: 12 September 2024
Series: Applied and Computational Engineering
Volume number: Vol.95
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Guo Jianning and Li Wei. “Analysis of common cardiovascular and cerebrovascular diseases in elderly emergency patients”. In: Electronic Journal of Integrated Traditional and Western Medicine Cardiovascular Diseases 3.35 (2015), p. 2.

[2]. Liang Yisong. “Arrhythmia classification and signal time scale based on Deep learning”. MA thesis. Shandong University, 2024.

[3]. Yun-Chi Yeh, Che Chiou, and Lin Hong- Jhih. “Analyzing ECG for cardiac arrhythmia using cluster analysis”. In: Expert Systems with Applications: An International Journal 39 (Jan. 2012), pp. 1000–1010. DOI: 10.1016/j.eswa.2011.07.101.

[4]. Taiyong Li and Min Zhou. “ECG Classification Using Wavelet Packet Entropy and Random Forests”. In: Entropy 18 (Aug. 2016), p. 285. DOI: 10.3390/e18080285.

[5]. Ramachandran Varatharajan, Gunasekaran Manogaran, and Priyan M K. “A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing”. In: Multimedia Tools and Applications 77 (Nov. 2017). DOI: 10.1007/s11042-017-5318-1.

[6]. Liu Shu et al. “Ecg signal classification based on bispectral and spectral features”. In: Electronic Science and Technology (2021).

[7]. Serkan Kiranyaz, Turker Ince, and Moncef Gabbouj. “Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks”. In: IEEE Transactions on Biomedical Engineering 63.3 (2016), pp. 664–675. DOI: 10.1109/TBME.2015.2468589.

[8]. Chen Siyu. “Research on ECG signal denoising, Wave group detection and arrhythmia recognition algorithm”. MA thesis. Place of publication unknown]: Nanjing University of Finance and Economics, 2024.

[9]. Zhu Jinling. “Application of wavelet threshold denoising technology in ECG signal processing”. In: China High-Tech (2022), pp. 88–89. ISSN: 2096-4137. DOI: 10.13535/j.cnki.10- 1507/n.2022.04.36.

[10]. Song Xiguo and Deng Qinkai. “Understanding and application of MIT-BIH arrhythmia database”. In: Chinese Journal of Medical Physics 21.4 (2004), p. 3.

[11]. Cheng Xiangqian. “ECG signal classification based on fusion of CNN and SVR evidence theory”. MA thesis. Shandong University of Science and Technology, 2021.

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