Research on high-efficiency electrocardiogram automatic classification based on autoregressive moving average model fitting_Journal of Biomedical Engineering

Authors：

YAN Huijun ,  MO Site , HUANG Hua , LIU Yan

School of Electrical Engineering, Sichuan University, Chengdu 610065, P.R.China;

Corresponding author：

MO Site, Email: mosite@126.com

Keywords：

arrhythmia; autoregressive moving average model; heart beat segmentation; R wave extraction; support vector machine

DOI：

10.7507/1001-5515.202101054

Video：

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The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the F₁ index was 98.3%. The algorithm has high performance, meets the needs of clinical diagnosis, and has low algorithm complexity. It can use low-power embedded processors for real-time calculations, and it’s suitable for real-time warning of wearable ECG monitoring equipment.

Citation： YAN Huijun, MO Site, HUANG Hua, LIU Yan. Research on high-efficiency electrocardiogram automatic classification based on autoregressive moving average model fitting. Journal of Biomedical Engineering, 2021, 38(5): 848-857, 868. doi: 10.7507/1001-5515.202101054 Copy

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2.	Wang D, Meng Q, Chen D, et al. Automatic detection of arrhythmia based on multi-resolution representation of ECG signal. Sensors, 2020, 20(6): 1579.
3.	钱龙, 王文波, 陈贵词, 等. 一种基于遗传算法优化的长短时记忆网络胎儿心电信号提取方法. 生物医学工程学杂志, 2021, 38(2): 257-267.
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6.	Merah M, Abdelmalik T A, Larbi B H. R-peaks detection based on stationary wavelet transform. Comput Meth Prog Bio, 2015, 121(3): 149-160.
7.	Jung Y, Kim H. Detection of PVC by using a wavelet-based statistical ECG monitoring procedure. Biomed Signal Proces, 2017, 36: 176-182.
8.	Gupta P, Sharma K K, Joshi S D. Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition. Healthc Technol Lett, 2015, 2(6): 164-166.
9.	Kabir M A, Shahnaz C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Proces, 2012, 7(5): 481-489.
10.	Lu L, Yan J, de Silva C W. Feature selection for ECG signal processing using improved genetic algorithm and empirical mode decomposition. Measurement, 2016, 94: 372-381.
11.	宋立新, 孙东梓, 王乾, 等. 基于判别式深度置信网络的心律失常自动分类方法. 生物医学工程学杂志, 2019, 36(3): 444-452.
12.	余明, 陈锋, 张广, 等. 应用遗传算法优化神经网络的致死性心电节律辨识算法研究. 生物医学工程学杂志, 2017, 34(3): 421-430.
13.	Muma M, Zoubir A M. Bounded influence propagation tau-estimation: A new robust method for ARMA model estimation. IEEE T Signal Proces, 2017, 65(7): 1712-1727.
14.	Welp B, Noujeim K, Pohl N. A wideband 20 to 28 GHz signal generator MMIC with 30.8 dBm output power based on a power amplifier cell with 31% PAE in SiGe. IEEE J Solid-St Circ, 2016, 51(9): 1975-1984.
15.	Giarré L, Argenti F. Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling. J Franklin I, 2018, 355(3): 1493-1511.
16.	Aguila J J, Arias E, Artigao M M, et al. A prediction of electrocardiography signals by combining ARMA model with nonlinear analysis methods//ACACOS'11: Proceedings of the 10th WSEAS International Conference on Applied Computer and Applied Computational Science. Venice: WSEAS, 2011: 31-37.
17.	Heurtefeux K, Mohsin N, Menouar H, et al. Prediction of time series using ARMA models in an energy-efficient body area network//2014 4th International Conference on Wireless Mobile Communication and Healthcare-Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH). Athens: IEEE, 2014: 230-233.
18.	王超超, 彭勇, 廖毅, 等. ECG信号改进阈值函数小波去噪算法研究.电子技术与软件工程, 2020, 171(1): 80-81.
19.	毛雪岷, 张婷婷, 蔡传晰, 等. 基于ARMA模型的心电聚类算法. 中国生物医学工程学报, 2012, 31(6): 816-821.
20.	葛丁飞, 李时辉. 基于ARMA模型的ECG分类和压缩. 浙江科技学院学报, 2004(1): 7-13.
21.	Van Der Maaten L, Postma E, Van den Herik J. Dimensionality reduction: a comparative. J Mach Learn Res, 2009, 10(66): 13-71.
22.	Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis. London: Cambridge University Press, 2004.
23.	周志华. 机器学习. 北京: 清华大学出版社, 2016.
24.	Oh S L, Ng E Y K, San Tan R, et al. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med, 2018, 102: 278-287.
25.	Yildirim O, Baloglu U B, Tan R S, et al. A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput Meth Prog Bio, 2019, 176: 121-133.
26.	Zhang Z, Dong J, Luo X, et al. Heartbeat classification using disease-specific feature selection. Comput Biol Med, 2014, 46: 79-89.
27.	Zhang Z, Luo X. Heartbeat classification using decision level fusion. Biomed Eng Lett, 2014, 4(4): 388-395.
28.	Garcia G, Moreira G, Menotti D, et al. Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Sci Rep-UK, 2017, 7(1): 1-11.

1. Zadeh F J, Akbari T, Samimi A, et al. The role of molecular mechanism of Ten-Eleven Translocation2 (TET2) family proteins in pathogenesis of cardiovascular diseases (CVDs). Mol Bio Rep, 2020, 47(7): 5503-5509.
2. Wang D, Meng Q, Chen D, et al. Automatic detection of arrhythmia based on multi-resolution representation of ECG signal. Sensors, 2020, 20(6): 1579.
3. 钱龙, 王文波, 陈贵词, 等. 一种基于遗传算法优化的长短时记忆网络胎儿心电信号提取方法. 生物医学工程学杂志, 2021, 38(2): 257-267.
4. Raj S, Ray K C. ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE T Instrum Meas, 2017, 66(3): 470-478.
5. Mazaheri V, Khodadadi H. Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm. Expert Syst Appl, 2020, 161: 113697.
6. Merah M, Abdelmalik T A, Larbi B H. R-peaks detection based on stationary wavelet transform. Comput Meth Prog Bio, 2015, 121(3): 149-160.
7. Jung Y, Kim H. Detection of PVC by using a wavelet-based statistical ECG monitoring procedure. Biomed Signal Proces, 2017, 36: 176-182.
8. Gupta P, Sharma K K, Joshi S D. Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition. Healthc Technol Lett, 2015, 2(6): 164-166.
9. Kabir M A, Shahnaz C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Proces, 2012, 7(5): 481-489.
10. Lu L, Yan J, de Silva C W. Feature selection for ECG signal processing using improved genetic algorithm and empirical mode decomposition. Measurement, 2016, 94: 372-381.
11. 宋立新, 孙东梓, 王乾, 等. 基于判别式深度置信网络的心律失常自动分类方法. 生物医学工程学杂志, 2019, 36(3): 444-452.
12. 余明, 陈锋, 张广, 等. 应用遗传算法优化神经网络的致死性心电节律辨识算法研究. 生物医学工程学杂志, 2017, 34(3): 421-430.
13. Muma M, Zoubir A M. Bounded influence propagation tau-estimation: A new robust method for ARMA model estimation. IEEE T Signal Proces, 2017, 65(7): 1712-1727.
14. Welp B, Noujeim K, Pohl N. A wideband 20 to 28 GHz signal generator MMIC with 30.8 dBm output power based on a power amplifier cell with 31% PAE in SiGe. IEEE J Solid-St Circ, 2016, 51(9): 1975-1984.
15. Giarré L, Argenti F. Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling. J Franklin I, 2018, 355(3): 1493-1511.
16. Aguila J J, Arias E, Artigao M M, et al. A prediction of electrocardiography signals by combining ARMA model with nonlinear analysis methods//ACACOS'11: Proceedings of the 10th WSEAS International Conference on Applied Computer and Applied Computational Science. Venice: WSEAS, 2011: 31-37.
17. Heurtefeux K, Mohsin N, Menouar H, et al. Prediction of time series using ARMA models in an energy-efficient body area network//2014 4th International Conference on Wireless Mobile Communication and Healthcare-Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH). Athens: IEEE, 2014: 230-233.
18. 王超超, 彭勇, 廖毅, 等. ECG信号改进阈值函数小波去噪算法研究.电子技术与软件工程, 2020, 171(1): 80-81.
19. 毛雪岷, 张婷婷, 蔡传晰, 等. 基于ARMA模型的心电聚类算法. 中国生物医学工程学报, 2012, 31(6): 816-821.
20. 葛丁飞, 李时辉. 基于ARMA模型的ECG分类和压缩. 浙江科技学院学报, 2004(1): 7-13.
21. Van Der Maaten L, Postma E, Van den Herik J. Dimensionality reduction: a comparative. J Mach Learn Res, 2009, 10(66): 13-71.
22. Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis. London: Cambridge University Press, 2004.
23. 周志华. 机器学习. 北京: 清华大学出版社, 2016.
24. Oh S L, Ng E Y K, San Tan R, et al. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med, 2018, 102: 278-287.
25. Yildirim O, Baloglu U B, Tan R S, et al. A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput Meth Prog Bio, 2019, 176: 121-133.
26. Zhang Z, Dong J, Luo X, et al. Heartbeat classification using disease-specific feature selection. Comput Biol Med, 2014, 46: 79-89.
27. Zhang Z, Luo X. Heartbeat classification using decision level fusion. Biomed Eng Lett, 2014, 4(4): 388-395.
28. Garcia G, Moreira G, Menotti D, et al. Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Sci Rep-UK, 2017, 7(1): 1-11.

Journal of Biomedical Engineering

Research on high-efficiency electrocardiogram automatic classification based on autoregressive moving average model fitting

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