ST segment morphological classification based on support vector machine multi feature fusion_Journal of Biomedical Engineering

Authors：

DU Haiman , BIAN Ting , XIONG Peng , YANG Jianli , ZHANG Jieshuo ,  LIU Xiuling

Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China;

Corresponding author：

LIU Xiuling, Email: liuxiuling121@hotmail.com

Keywords：

Electrocardiogram signal; Support vector machine; ST segment morphology; Feature fusion; Gradient

DOI：

10.7507/1001-5515.202110015

Video：

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Abstract Full text Figures/Tables Video References Cited by

ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.

Citation： DU Haiman, BIAN Ting, XIONG Peng, YANG Jianli, ZHANG Jieshuo, LIU Xiuling. ST segment morphological classification based on support vector machine multi feature fusion. Journal of Biomedical Engineering, 2022, 39(4): 702-712. doi: 10.7507/1001-5515.202110015 Copy

1.	Chávez G, Rose C E, Serna M T, et al. ECG feature extraction for automatic classification of ischemic events. IEEE Latin America Transactions, 2019, 17(6): 945-952.
2.	Kayikcioglu I, Ulutas G, Kayıkçıoğlu T. Time-frequency analysis based detection of ECG ST segment change using large feature set// 2017 25th Signal Processing and Communications Applications Conference (SIU), Antalya: IEEE, 2017.
3.	Shen Z, Hu C, Liao J, et al. An algorithm of ST segment classification and detection//2010 IEEE International Conference on Automation and Logistics, Hong Kong: IEEE, 2010: 559-564.
4.	黎承涛. 心电信号特征提取及ST段形态识别的方法研究. 合肥: 安徽大学, 2015.
5.	师黎, 杨岑玉, 费敏锐. 基于小波变换的心电信R波及ST段的提取. 仪器仪表学报, 2008, 29(4): 804-809.
6.	Firoozabadi R, Gregg R E, Babaeizadeh S. Modeling and classification of the ST segment morphology for enhanced detection of acute myocardial infarction// 2019 Computing in Cardiology (CinC), Singapore: IEEE, 2019.
7.	Wang Hongmei, Zhao Wei, Xu Yanwu, et al. ST segment change classification based on multiple feature extraction using ECG//2018 Computing in Cardiology Conference (CinC), 2018, 45: 1-4.
8.	Harun-Ar-Rashid M, Mahmud G, Rahman M M, et al. Classification of ST segment in ECG signals based on cross correlated supervised data. SN Appl Sci, 2020, 2(7): 1224.
9.	Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE trans biomed eng, 1985, 32(3): 230-236.
10.	李阳, 洪跃, 王俊杰. 基于角度与幅值特征的QRS波群起止点识别. 生物医学工程学杂志, 2014, 31(3): 572-576.
11.	Kayikcioglu I, Akdeniz F, Köse C, et al. Time-frequency approach to ECG classification of myocardial infarction. Computers & Electrical Engineering, 2020, 84: 106621.
12.	杨宇, 司玉娟, 宋晓洋. 基于小波变换和支持向量机的心电信号ST段分类. 吉林大学学报:信息科学版, 2016, 34(3): 315-319.
13.	Taddei A, Distante G, Emdin M, et al. The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. Eur Heart J, 1992, 13(9): 1164-1172.
14.	Ansari S, Farzaneh N, Duda M, et al. A review of automated methods for detection of myocardial ischemia and infarction using electrocardiogram and electronic health records. IEEE Rev Biomed Eng, 2017, 10: 264-298.
15.	Ariyarajah V, Spodick D H. Acute pericarditis: diagnostic cues and common electrocardiographic manifestations. Cardiol Rev, 2007, 15(1): 24-30.
16.	Sharma H, Sharma K K. ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition. Australas Phys Eng Sci Med, 2018, 41(2): 429-443.
17.	Wenger E. Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Berkeley: Morgan Kaufmann, 2014: 510.
18.	Hussein A F, Hashim S J, Rokhani F Z, et al. An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and choi-williams time-frequency analysis incorporating a multi-class SVM classifier. Sensors, 2021, 21(7): 2311.
19.	Huang Y, Wu D, Zhang Z, et al. Emd-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM. Journal of Materials Processing Technology, 2017, 239: 92-102.

1. Chávez G, Rose C E, Serna M T, et al. ECG feature extraction for automatic classification of ischemic events. IEEE Latin America Transactions, 2019, 17(6): 945-952.
2. Kayikcioglu I, Ulutas G, Kayıkçıoğlu T. Time-frequency analysis based detection of ECG ST segment change using large feature set// 2017 25th Signal Processing and Communications Applications Conference (SIU), Antalya: IEEE, 2017.
3. Shen Z, Hu C, Liao J, et al. An algorithm of ST segment classification and detection//2010 IEEE International Conference on Automation and Logistics, Hong Kong: IEEE, 2010: 559-564.
4. 黎承涛. 心电信号特征提取及ST段形态识别的方法研究. 合肥: 安徽大学, 2015.
5. 师黎, 杨岑玉, 费敏锐. 基于小波变换的心电信R波及ST段的提取. 仪器仪表学报, 2008, 29(4): 804-809.
6. Firoozabadi R, Gregg R E, Babaeizadeh S. Modeling and classification of the ST segment morphology for enhanced detection of acute myocardial infarction// 2019 Computing in Cardiology (CinC), Singapore: IEEE, 2019.
7. Wang Hongmei, Zhao Wei, Xu Yanwu, et al. ST segment change classification based on multiple feature extraction using ECG//2018 Computing in Cardiology Conference (CinC), 2018, 45: 1-4.
8. Harun-Ar-Rashid M, Mahmud G, Rahman M M, et al. Classification of ST segment in ECG signals based on cross correlated supervised data. SN Appl Sci, 2020, 2(7): 1224.
9. Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE trans biomed eng, 1985, 32(3): 230-236.
10. 李阳, 洪跃, 王俊杰. 基于角度与幅值特征的QRS波群起止点识别. 生物医学工程学杂志, 2014, 31(3): 572-576.
11. Kayikcioglu I, Akdeniz F, Köse C, et al. Time-frequency approach to ECG classification of myocardial infarction. Computers & Electrical Engineering, 2020, 84: 106621.
12. 杨宇, 司玉娟, 宋晓洋. 基于小波变换和支持向量机的心电信号ST段分类. 吉林大学学报:信息科学版, 2016, 34(3): 315-319.
13. Taddei A, Distante G, Emdin M, et al. The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. Eur Heart J, 1992, 13(9): 1164-1172.
14. Ansari S, Farzaneh N, Duda M, et al. A review of automated methods for detection of myocardial ischemia and infarction using electrocardiogram and electronic health records. IEEE Rev Biomed Eng, 2017, 10: 264-298.
15. Ariyarajah V, Spodick D H. Acute pericarditis: diagnostic cues and common electrocardiographic manifestations. Cardiol Rev, 2007, 15(1): 24-30.
16. Sharma H, Sharma K K. ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition. Australas Phys Eng Sci Med, 2018, 41(2): 429-443.
17. Wenger E. Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Berkeley: Morgan Kaufmann, 2014: 510.
18. Hussein A F, Hashim S J, Rokhani F Z, et al. An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and choi-williams time-frequency analysis incorporating a multi-class SVM classifier. Sensors, 2021, 21(7): 2311.
19. Huang Y, Wu D, Zhang Z, et al. Emd-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM. Journal of Materials Processing Technology, 2017, 239: 92-102.

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ST segment morphological classification based on support vector machine multi feature fusion

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