D-ECG: a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs

Jinyuan He; Jia Rong; Le Sun; Hua Wang; Yanchun Zhang; Jiangang Ma

doi:10.1007/978-3-030-02925-8_6

D-ECG: a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs

Jinyuan He, Jia Rong, Le Sun, Hua Wang, Yanchun Zhang, Jiangang Ma

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

15 Citations (Scopus)

Abstract

Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12 % of all deaths globally. The current progress on arrhythmia detection based on ECG recordings is facing a bottleneck for adopting single classifier and static ensemble methods. Besides, most of the work tend to use a static feature set for characterizing all types of heartbeats, which may limit the classification performance. To fill in the gap, a novel framework called D-ECG is proposed to introduce dynamic ensemble selection (DES) technique to provide accurate detection of cardiac arrhythmia. In addition, the proposed D-ECG develops a result regulator that use different features to refine the classification result from the DES technique. The results reported in this paper have shown visible improvement on the overall heartbeat classification accuracy as well as the sensitivity of disease heartbeats.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2018
Subtitle of host publication	19th International Conference Dubai, United Arab Emirates, November 12–15, 2018 Proceedings, Part II
Editors	Hakim Hacid, Wojciech Cellary, Hua Wang, Hye-Young Paik, Rui Zhou
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	85-99
Number of pages	15
ISBN (Electronic)	9783030029258
ISBN (Print)	9783030029241
DOIs	https://doi.org/10.1007/978-3-030-02925-8_6
Publication status	Published - 2018
Externally published	Yes
Event	International Conference on Web Information Systems Engineering 2018 - Dubai, United Arab Emirates Duration: 12 Nov 2018 → 15 Nov 2018 Conference number: 19th http://wise2018.connect.rs/index.html https://link.springer.com/book/10.1007/978-3-030-02922-7 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11234
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Web Information Systems Engineering 2018
Abbreviated title	WISE 2018
Country/Territory	United Arab Emirates
City	Dubai
Period	12/11/18 → 15/11/18
Internet address	http://wise2018.connect.rs/index.html https://link.springer.com/book/10.1007/978-3-030-02922-7 (Proceedings)

Keywords

Cardiac arrhythmia detection
Dynamic ensemble selection
ECG

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-02925-8_6

Cite this

He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., & Ma, J. (2018). D-ECG: a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs. In H. Hacid, W. Cellary, H. Wang, H.-Y. Paik, & R. Zhou (Eds.), Web Information Systems Engineering – WISE 2018: 19th International Conference Dubai, United Arab Emirates, November 12–15, 2018 Proceedings, Part II (pp. 85-99). (Lecture Notes in Computer Science ; Vol. 11234 ). Springer. https://doi.org/10.1007/978-3-030-02925-8_6

He, Jinyuan ; Rong, Jia ; Sun, Le et al. / D-ECG : a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs. Web Information Systems Engineering – WISE 2018: 19th International Conference Dubai, United Arab Emirates, November 12–15, 2018 Proceedings, Part II. editor / Hakim Hacid ; Wojciech Cellary ; Hua Wang ; Hye-Young Paik ; Rui Zhou. Cham Switzerland : Springer, 2018. pp. 85-99 (Lecture Notes in Computer Science ).

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title = "D-ECG: a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs",

abstract = "Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12 % of all deaths globally. The current progress on arrhythmia detection based on ECG recordings is facing a bottleneck for adopting single classifier and static ensemble methods. Besides, most of the work tend to use a static feature set for characterizing all types of heartbeats, which may limit the classification performance. To fill in the gap, a novel framework called D-ECG is proposed to introduce dynamic ensemble selection (DES) technique to provide accurate detection of cardiac arrhythmia. In addition, the proposed D-ECG develops a result regulator that use different features to refine the classification result from the DES technique. The results reported in this paper have shown visible improvement on the overall heartbeat classification accuracy as well as the sensitivity of disease heartbeats.",

keywords = "Cardiac arrhythmia detection, Dynamic ensemble selection, ECG",

author = "Jinyuan He and Jia Rong and Le Sun and Hua Wang and Yanchun Zhang and Jiangang Ma",

year = "2018",

doi = "10.1007/978-3-030-02925-8_6",

language = "English",

isbn = "9783030029241",

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editor = "Hakim Hacid and Wojciech Cellary and Hua Wang and Hye-Young Paik and Rui Zhou",

booktitle = "Web Information Systems Engineering – WISE 2018",

note = "International Conference on Web Information Systems Engineering 2018, WISE 2018 ; Conference date: 12-11-2018 Through 15-11-2018",

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He, J, Rong, J, Sun, L, Wang, H, Zhang, Y & Ma, J 2018, D-ECG: a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs. in H Hacid, W Cellary, H Wang, H-Y Paik & R Zhou (eds), Web Information Systems Engineering – WISE 2018: 19th International Conference Dubai, United Arab Emirates, November 12–15, 2018 Proceedings, Part II. Lecture Notes in Computer Science , vol. 11234 , Springer, Cham Switzerland, pp. 85-99, International Conference on Web Information Systems Engineering 2018, Dubai, United Arab Emirates, 12/11/18. https://doi.org/10.1007/978-3-030-02925-8_6

D-ECG: a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs. / He, Jinyuan; Rong, Jia; Sun, Le et al.
Web Information Systems Engineering – WISE 2018: 19th International Conference Dubai, United Arab Emirates, November 12–15, 2018 Proceedings, Part II. ed. / Hakim Hacid; Wojciech Cellary; Hua Wang; Hye-Young Paik; Rui Zhou. Cham Switzerland: Springer, 2018. p. 85-99 (Lecture Notes in Computer Science ; Vol. 11234 ).

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

TY - GEN

T1 - D-ECG

T2 - International Conference on Web Information Systems Engineering 2018

AU - He, Jinyuan

AU - Rong, Jia

AU - Sun, Le

AU - Wang, Hua

AU - Zhang, Yanchun

AU - Ma, Jiangang

N1 - Conference code: 19th

PY - 2018

Y1 - 2018

N2 - Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12 % of all deaths globally. The current progress on arrhythmia detection based on ECG recordings is facing a bottleneck for adopting single classifier and static ensemble methods. Besides, most of the work tend to use a static feature set for characterizing all types of heartbeats, which may limit the classification performance. To fill in the gap, a novel framework called D-ECG is proposed to introduce dynamic ensemble selection (DES) technique to provide accurate detection of cardiac arrhythmia. In addition, the proposed D-ECG develops a result regulator that use different features to refine the classification result from the DES technique. The results reported in this paper have shown visible improvement on the overall heartbeat classification accuracy as well as the sensitivity of disease heartbeats.

AB - Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12 % of all deaths globally. The current progress on arrhythmia detection based on ECG recordings is facing a bottleneck for adopting single classifier and static ensemble methods. Besides, most of the work tend to use a static feature set for characterizing all types of heartbeats, which may limit the classification performance. To fill in the gap, a novel framework called D-ECG is proposed to introduce dynamic ensemble selection (DES) technique to provide accurate detection of cardiac arrhythmia. In addition, the proposed D-ECG develops a result regulator that use different features to refine the classification result from the DES technique. The results reported in this paper have shown visible improvement on the overall heartbeat classification accuracy as well as the sensitivity of disease heartbeats.

KW - Cardiac arrhythmia detection

KW - Dynamic ensemble selection

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SN - 9783030029241

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BT - Web Information Systems Engineering – WISE 2018

A2 - Hacid, Hakim

A2 - Cellary, Wojciech

A2 - Wang, Hua

A2 - Paik, Hye-Young

A2 - Zhou, Rui

PB - Springer

CY - Cham Switzerland

Y2 - 12 November 2018 through 15 November 2018

ER -

He J, Rong J, Sun L, Wang H, Zhang Y, Ma J. D-ECG: a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs. In Hacid H, Cellary W, Wang H, Paik HY, Zhou R, editors, Web Information Systems Engineering – WISE 2018: 19th International Conference Dubai, United Arab Emirates, November 12–15, 2018 Proceedings, Part II. Cham Switzerland: Springer. 2018. p. 85-99. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-030-02925-8_6

D-ECG: a dynamic framework for cardiac arrhythmia detection from IoT-based ECGs

Abstract

Publication series

Conference

Keywords

UN SDGs

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A framework for cardiac arrhythmia detection from IoT-based ECGs

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