Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data

Julia Tzu Ya Weng; Jyun Jie Lin; Yi Cheng Chen; Pei Chann Chang

doi:10.1007/978-3-319-13186-3_61

Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data

Julia Tzu Ya Weng, Jyun Jie Lin, Yi Cheng Chen, Pei Chann Chang

Department of Information Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Rapid and accurate diagnosis of patients with acute myocardial infarction is vital. The ST segment in Electrocardiography (ECG) represents the change of electric potential during the period from the end of ventricular depolarization to the beginning of repolarization and plays an important role in the detection of myocardial infarction. However, ECG monitoring generates big volumes of data and the underlying complexity must be extracted by a combination of methods. This study combines the advantages of polynomial approximation and principal component analysis. The proposed approach is stable for the 12-lead ECG data collected from the PTB database and achieves an accuracy of 98.07%.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
Subtitle of host publication	DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers
Editors	Wen-Chih Peng, Haixun Wang, Zhi-Hua Zhou, Tu Bao Ho, Vincent S. Tseng, Arbee L.P. Chen, James Bailey
Publisher	Springer Verlag
Pages	689-699
Number of pages	11
ISBN (Electronic)	9783319131856
DOIs	https://doi.org/10.1007/978-3-319-13186-3_61
State	Published - 2014
Event	International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan Duration: 13 May 2014 → 16 May 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8643
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
Country/Territory	Taiwan
City	Tainan
Period	13/05/14 → 16/05/14

Keywords

12-lead ECG
Myocardial infarction
Polynomial approximation
Principal component analysis
Support vector machine

Access to Document

10.1007/978-3-319-13186-3_61

Cite this

Weng, J. T. Y., Lin, J. J., Chen, Y. C., & Chang, P. C. (2014). Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data. In W-C. Peng, H. Wang, Z-H. Zhou, T. B. Ho, V. S. Tseng, A. L. P. Chen, & J. Bailey (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers (pp. 689-699). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643). Springer Verlag. https://doi.org/10.1007/978-3-319-13186-3_61

Weng, Julia Tzu Ya ; Lin, Jyun Jie ; Chen, Yi Cheng et al. / Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. editor / Wen-Chih Peng ; Haixun Wang ; Zhi-Hua Zhou ; Tu Bao Ho ; Vincent S. Tseng ; Arbee L.P. Chen ; James Bailey. Springer Verlag, 2014. pp. 689-699 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data",

abstract = "Rapid and accurate diagnosis of patients with acute myocardial infarction is vital. The ST segment in Electrocardiography (ECG) represents the change of electric potential during the period from the end of ventricular depolarization to the beginning of repolarization and plays an important role in the detection of myocardial infarction. However, ECG monitoring generates big volumes of data and the underlying complexity must be extracted by a combination of methods. This study combines the advantages of polynomial approximation and principal component analysis. The proposed approach is stable for the 12-lead ECG data collected from the PTB database and achieves an accuracy of 98.07%.",

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Weng, JTY, Lin, JJ, Chen, YC & Chang, PC 2014, Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data. in W-C Peng, H Wang, Z-H Zhou, TB Ho, VS Tseng, ALP Chen & J Bailey (eds), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8643, Springer Verlag, pp. 689-699, International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, Tainan, Taiwan, 13/05/14. https://doi.org/10.1007/978-3-319-13186-3_61

Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data. / Weng, Julia Tzu Ya; Lin, Jyun Jie; Chen, Yi Cheng et al.

Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. ed. / Wen-Chih Peng; Haixun Wang; Zhi-Hua Zhou; Tu Bao Ho; Vincent S. Tseng; Arbee L.P. Chen; James Bailey. Springer Verlag, 2014. p. 689-699 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data

AU - Weng, Julia Tzu Ya

AU - Lin, Jyun Jie

AU - Chen, Yi Cheng

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N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

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AB - Rapid and accurate diagnosis of patients with acute myocardial infarction is vital. The ST segment in Electrocardiography (ECG) represents the change of electric potential during the period from the end of ventricular depolarization to the beginning of repolarization and plays an important role in the detection of myocardial infarction. However, ECG monitoring generates big volumes of data and the underlying complexity must be extracted by a combination of methods. This study combines the advantages of polynomial approximation and principal component analysis. The proposed approach is stable for the 12-lead ECG data collected from the PTB database and achieves an accuracy of 98.07%.

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KW - Myocardial infarction

KW - Polynomial approximation

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KW - Support vector machine

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BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops

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A2 - Wang, Haixun

A2 - Zhou, Zhi-Hua

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A2 - Bailey, James

PB - Springer Verlag

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Weng JTY, Lin JJ, Chen YC, Chang PC. Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data. In Peng W-C, Wang H, Zhou Z-H, Ho TB, Tseng VS, Chen ALP, Bailey J, editors, Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. Springer Verlag. 2014. p. 689-699. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-13186-3_61

Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data

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Keywords

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