Accurate and Fast ElectroCardioGram Classification Method Based on Adaptive Fast S-Transform and XGBoost

YUAN Lifen; LI Song; YIN Baiqiang; LI Bing; ZUO Lei

doi:10.11999/JEIT220217

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1464-1474

YUAN Lifen, LI Song, YIN Baiqiang, LI Bing, ZUO Lei. Accurate and Fast ElectroCardioGram Classification Method Based on Adaptive Fast S-Transform and XGBoost[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1464-1474. doi: 10.11999/JEIT220217

Citation:

YUAN Lifen, LI Song, YIN Baiqiang, LI Bing, ZUO Lei. Accurate and Fast ElectroCardioGram Classification Method Based on Adaptive Fast S-Transform and XGBoost[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1464-1474. doi: 10.11999/JEIT220217

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Accurate and Fast ElectroCardioGram Classification Method Based on Adaptive Fast S-Transform and XGBoost

doi: 10.11999/JEIT220217

School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China

Funds: The National Natural Science Foundation of China (61971175), The Fundamental Research Funds for the Central Universities (JZ2019YYPY0025)

Received Date: 2022-03-02
Rev Recd Date: 2022-07-10

Available Online: 2022-07-19

Publish Date: 2023-04-10

Abstract

Abstract

Considering the low efficiency of traditional ElectroCardioGram(ECG) classification methods, an accurate and fast ElectroCardioGram classification method based on Adaptive Fast S-Transform (AFST) and XGBoost is proposed. Firstly, the main feature points of the ECG signals are determined through a fast positioning algorithm, and then the S-Transform window width factor is adjusted adaptively according to the main feature points to enhance the time-frequency resolution of the S-transform while avoiding iterative calculation and reducing the running time greatly; Secondly, based on the time-frequency matrix of AFST, 12 eigenvalues are extracted to represent the characteristic information of 5 kinds of ECG signals, with low eigenvector dimension and strong recognition ability. Finally, XGBoost is used to identify the eigenvectors. The experimental studies based on the MIT-BIH arrhythmia database and the verification of patient measurement data show that, with the proposed method, the classification time of ECG signals is significantly shortened and classification accuracy of 99.59%, 97.32% is obtained respectively, which is suitable for the rapid diagnosis of abnormal diseases in the center rate of the actual detection system.
- ElectroCardioGram (ECG),
- Arrhythmias,
- S-Transform (ST),
- Adaptive Fast S-Transform (AFST),
- XGBoost algorithm

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References(25)

References

[1]	KREATSOUS C and ANAND S S. The impact of social determinants on cardiovascular disease[J]. Canadian Journal of Cardiology, 2010, 26 Suppl C: 8C–13C.
[2]	World Health Organization. Cardiovascular disease (CVDs)[EB/OL].https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds), 2021.
[3]	AMORIM P, MORAES T, FAZANARO D, et al. Shearlet and contourlet transforms for analysis of electrocardiogram signals[J]. Computer Methods and Programs in Biomedicine, 2018, 161: 125–132. doi: 10.1016/j.cmpb.2018.04.021
[4]	吴志勇, 丁香乾, 许晓伟, 等. 基于深度学习和模糊C均值的心电信号分类方法[J]. 自动化学报, 2018, 44(10): 1913–1920. doi: 10.16383/j.aas.2018.c170417 WU Zhiyong, DING Xiangqian, XU Xiaowei, et al. A method for ECG classification using deep learning and fuzzy C-means[J]. Acta Automatica Sinica, 2018, 44(10): 1913–1920. doi: 10.16383/j.aas.2018.c170417
[5]	DE CHAZAL P, O’DWYER M, and REILLY R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(7): 1196–1206. doi: 10.1109/TBME.2004.827359
[6]	MAR T, ZAUNSEDER S, MARTÍNEZ J P, et al. Optimization of ECG classification by means of feature selection[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(8): 2168–2177. doi: 10.1109/TBME.2011.2113395
[7]	王金海, 史梦颖, 张兴华. 基于EMD和ApEn特征提取的心律失常分类研究[J]. 仪器仪表学报, 2016, 37(S1): 168–173. doi: 10.19650/j.cnki.cjsi.2016.s1.028 WANG Jinhai, SHI Mengying, and ZHANG Xinghua. Classification of arrhythmias based on EMD and ApEn feature extraction[J]. Chinese Journal of Scientific Instrument, 2016, 37(S1): 168–173. doi: 10.19650/j.cnki.cjsi.2016.s1.028
[8]	JACOB N and JOSEPH L A. Classification of ECG beats using cross wavelet transform and support vector machines[C]. 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, India, 2015.
[9]	尹柏强, 邓影, 王署东, 等. 时频广义S变换和VL-MOBP神经网络在人体动作识别中的应用[J]. 电子测量与仪器学报, 2020, 34(11): 1–9. doi: 10.13382/j.jemi.B2003032 YIN Baiqiang, DENG Ying, WANG Shudong, et al. Application of time-frequency generalized S transform and VL-MOBP neural network in human motion recognition[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(11): 1–9. doi: 10.13382/j.jemi.B2003032
[10]	DAS M K and ARI S. Electrocardiogram beat classification using S-transform based feature set[J]. Journal of Mechanics in Medicine and Biology, 2014, 14(5): 1450066. doi: 10.1142/S0219519414500663
[11]	YANG Jianyong and YAN Ruqiang. A multidimensional feature extraction and selection method for ECG arrhythmias classification[J]. IEEE Sensors Journal, 2021, 21(13): 14180–14190. doi: 10.1109/JSEN.2020.3047962
[12]	RAJ S and RAY K C. ECG signal analysis using DCT-Based DOST and PSO optimized SVM[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(3): 470–478. doi: 10.1109/TIM.2016.2642758
[13]	SINHA N and DAS A. Identification and localization of myocardial infarction based on analysis of ECG signal in cross spectral domain using boosted SVM classifier[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 4007409. doi: 10.1109/TIM.2021.3117663
[14]	HOU Borui, YANG Jianyong, WANG Pu, et al. LSTM-based auto-encoder model for ECG arrhythmias classification[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(4): 1232–1240. doi: 10.1109/TIM.2019.2910342
[15]	柯丽, 王丹妮, 杜强, 等. 基于卷积长短时记忆网络的心律失常分类方法[J]. 电子与信息学报, 2020, 42(8): 1990–1998. doi: 10.11999/JEIT190712 KE Li, WANG Danni, DU Qiang, et al. Arrhythmia classification based on convolutional long short term memory network[J]. Journal of Electronics &Information Technology, 2020, 42(8): 1990–1998. doi: 10.11999/JEIT190712
[16]	BHATTACHARYYA S, MAJUMDER S, DEBNATH P, et al. Arrhythmic heartbeat classification using ensemble of random forest and support vector machine algorithm[J]. IEEE Transactions on Artificial Intelligence, 2021, 2(3): 260–268. doi: 10.1109/TAI.2021.3083689
[17]	KUNG B H, HU P Y, HUANG C C, et al. An efficient ECG classification system using resource-saving architecture and random forest[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(6): 1904–1914. doi: 10.1109/JBHI.2020.3035191
[18]	王帅, 赵钟瑶, 张翔宇, 等. 穿戴式心电信号质量的三分类评估方法[J]. 中国生物医学工程学报, 2020, 39(5): 550–556. doi: 10.3969/j.issn.0258-8021.2020.05.005 WANG Shuai, ZHAO Zhongyao, ZHANG Xiangyu, et al. Three-type classification method for wearable ECG signal quality[J]. Chinese Journal of Biomedical Engineering, 2020, 39(5): 550–556. doi: 10.3969/j.issn.0258-8021.2020.05.005
[19]	喻玲, 符玲, 熊思宇, 等. 基于最优Kaiser窗的动态同步相量测量算法[J]. 电网技术, 2022, 46(3): 1100–1108. doi: 10.13335/j.1000-3673.pst.2021.0602 YU Ling, FU Ling, XIONG Siyu, et al. A dynamic synchrophasor estimation algorithm based on optimal Kaiser window[J]. Power System Technology, 2022, 46(3): 1100–1108. doi: 10.13335/j.1000-3673.pst.2021.0602
[20]	尹柏强, 何怡刚, 朱彦卿. 一种广义S变换及模糊SOM网络的电能质量多扰动检测和识别方法[J]. 中国电机工程学报, 2015, 35(4): 866–872. doi: 10.13334/j.0258-8013.pcsee.2015.04.013 YIN Baiqiang, HE Yigang, and ZHU Yanqing. Detection and classification of power quality multi-disturbances based on generalized s-transform and fuzzy SOM neural network[J]. Proceedings of the CSEE, 2015, 35(4): 866–872. doi: 10.13334/j.0258-8013.pcsee.2015.04.013
[21]	王奕森, 夏树涛. 集成学习之随机森林算法综述[J]. 信息通信技术, 2018, 12(1): 49–55. doi: 10.3969/j.issn.1674-1285.2018.01.009 WANG Yisen and XIA Shutao. A survey of random forests algorithms[J]. Information and Communications Technologies, 2018, 12(1): 49–55. doi: 10.3969/j.issn.1674-1285.2018.01.009
[22]	赵洪山, 闫西慧, 王桂兰, 等. 应用深度自编码网络和XGBoost的风电机组发电机故障诊断[J]. 电力系统自动化, 2019, 43(1): 81–86. doi: 10.7500/AEPS20180708001 ZHAO Hongshan, YAN Xihui, WANG Guilan, et al. Fault diagnosis of wind turbine generator based on deep autoencoder network and XGBoost[J]. Automation of Electric Power Systems, 2019, 43(1): 81–86. doi: 10.7500/AEPS20180708001
[23]	PAN Jiapu and TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Transactions on Biomedical Engineering, 1985, BME-32(3): 230–236. doi: 10.1109/TBME.1985.325532
[24]	CHIA N G, HAU Y W, and JAMALUDIN M N. Robust arrhythmia classifier using wavelet transform and support vector machine classification[C]. The 13th International Colloquium on Signal Processing & its Applications (CSPA), Penang, Malaysia, 2017.
[25]	HERRY C L, FRASCH M, SEELY A J, et al. Heart beat classification from single-lead ECG using the synchrosqueezing transform[J]. Physiological Measurement, 2017, 38(2): 171–187. doi: 10.1088/1361-6579/aa5070