Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal

Mohamad Mahmoud Al Rahhal; Naif Al Ajlan; Yakoub Bazi; Haikel Al Hichri; Timon Rabczuk

doi:10.1109/EIT.2018.8500197

Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal

Mohamad Mahmoud Al Rahhal, Naif Al Ajlan, Yakoub Bazi, Haikel Al Hichri, Timon Rabczuk

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

16 Citations (Scopus)

Abstract

In this paper, we propose an electrocardiogram (ECG) technique for the automatic detection of Premature Ventricular Contractions (PVC) based on multi-lead signals and on a deep learning architecture which is built using Stacked Denoising Autoencoders (SDAEs) networks. The proposed method consists of two main stages; feature learning and classification. In the first stage, we learn a new feature representation from data using SDAEs. Regarding the classification, we add a softmax regression layer on the top of the resulting hidden representation layer yielding a deep neural network (DNN). The proposed method fuses the results of several ECG leads (up to 12) in order to increase the detection accuracy. In the experiments, we use INCART database to test the proposed DNN multi-lead method. The obtained results are 98.6%, 91.4%, and 97.7% respectively for overall accuracy (OA), average sensitivity (Se), and average positive productivity (Pp).

Original language	English
Title of host publication	2018 IEEE International Conference on Electro/Information Technology, EIT 2018
Publisher	IEEE Computer Society
Pages	169-173
Number of pages	5
ISBN (Electronic)	9781538653982
DOIs	https://doi.org/10.1109/EIT.2018.8500197
Publication status	Published - 2018 Oct 18
Event	2018 IEEE International Conference on Electro/Information Technology, EIT 2018 - Rochester, United States Duration: 2018 May 3 → 2018 May 5

Publication series

Name	IEEE International Conference on Electro Information Technology
Volume	2018-May
ISSN (Print)	2154-0357
ISSN (Electronic)	2154-0373

Other

Other	2018 IEEE International Conference on Electro/Information Technology, EIT 2018
Country/Territory	United States
City	Rochester
Period	18/5/3 → 18/5/5

Bibliographical note

Funding Information:
The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saud University for funding this work.

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/EIT.2018.8500197

Cite this

Rahhal, M. M. A., Ajlan, N. A., Bazi, Y., Hichri, H. A., & Rabczuk, T. (2018). Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal. In 2018 IEEE International Conference on Electro/Information Technology, EIT 2018 (pp. 169-173). Article 8500197 (IEEE International Conference on Electro Information Technology; Vol. 2018-May). IEEE Computer Society. https://doi.org/10.1109/EIT.2018.8500197

Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal. / Rahhal, Mohamad Mahmoud Al; Ajlan, Naif Al; Bazi, Yakoub et al.
2018 IEEE International Conference on Electro/Information Technology, EIT 2018. IEEE Computer Society, 2018. p. 169-173 8500197 (IEEE International Conference on Electro Information Technology; Vol. 2018-May).

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

Rahhal, MMA, Ajlan, NA, Bazi, Y, Hichri, HA & Rabczuk, T 2018, Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal. in 2018 IEEE International Conference on Electro/Information Technology, EIT 2018., 8500197, IEEE International Conference on Electro Information Technology, vol. 2018-May, IEEE Computer Society, pp. 169-173, 2018 IEEE International Conference on Electro/Information Technology, EIT 2018, Rochester, United States, 18/5/3. https://doi.org/10.1109/EIT.2018.8500197

Rahhal MMA, Ajlan NA, Bazi Y, Hichri HA, Rabczuk T. Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal. In 2018 IEEE International Conference on Electro/Information Technology, EIT 2018. IEEE Computer Society. 2018. p. 169-173. 8500197. (IEEE International Conference on Electro Information Technology). doi: 10.1109/EIT.2018.8500197

@inproceedings{433c85b4679a4452b522a182001995e4,

title = "Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal",

abstract = "In this paper, we propose an electrocardiogram (ECG) technique for the automatic detection of Premature Ventricular Contractions (PVC) based on multi-lead signals and on a deep learning architecture which is built using Stacked Denoising Autoencoders (SDAEs) networks. The proposed method consists of two main stages; feature learning and classification. In the first stage, we learn a new feature representation from data using SDAEs. Regarding the classification, we add a softmax regression layer on the top of the resulting hidden representation layer yielding a deep neural network (DNN). The proposed method fuses the results of several ECG leads (up to 12) in order to increase the detection accuracy. In the experiments, we use INCART database to test the proposed DNN multi-lead method. The obtained results are 98.6%, 91.4%, and 97.7% respectively for overall accuracy (OA), average sensitivity (Se), and average positive productivity (Pp).",

author = "Rahhal, {Mohamad Mahmoud Al} and Ajlan, {Naif Al} and Yakoub Bazi and Hichri, {Haikel Al} and Timon Rabczuk",

note = "Funding Information: The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saud University for funding this work. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Electro/Information Technology, EIT 2018 ; Conference date: 03-05-2018 Through 05-05-2018",

year = "2018",

month = oct,

day = "18",

doi = "10.1109/EIT.2018.8500197",

language = "English",

series = "IEEE International Conference on Electro Information Technology",

publisher = "IEEE Computer Society",

pages = "169--173",

booktitle = "2018 IEEE International Conference on Electro/Information Technology, EIT 2018",

}

TY - GEN

T1 - Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal

AU - Rahhal, Mohamad Mahmoud Al

AU - Ajlan, Naif Al

AU - Bazi, Yakoub

AU - Hichri, Haikel Al

AU - Rabczuk, Timon

N1 - Funding Information: The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saud University for funding this work. Publisher Copyright: © 2018 IEEE.

PY - 2018/10/18

Y1 - 2018/10/18

N2 - In this paper, we propose an electrocardiogram (ECG) technique for the automatic detection of Premature Ventricular Contractions (PVC) based on multi-lead signals and on a deep learning architecture which is built using Stacked Denoising Autoencoders (SDAEs) networks. The proposed method consists of two main stages; feature learning and classification. In the first stage, we learn a new feature representation from data using SDAEs. Regarding the classification, we add a softmax regression layer on the top of the resulting hidden representation layer yielding a deep neural network (DNN). The proposed method fuses the results of several ECG leads (up to 12) in order to increase the detection accuracy. In the experiments, we use INCART database to test the proposed DNN multi-lead method. The obtained results are 98.6%, 91.4%, and 97.7% respectively for overall accuracy (OA), average sensitivity (Se), and average positive productivity (Pp).

AB - In this paper, we propose an electrocardiogram (ECG) technique for the automatic detection of Premature Ventricular Contractions (PVC) based on multi-lead signals and on a deep learning architecture which is built using Stacked Denoising Autoencoders (SDAEs) networks. The proposed method consists of two main stages; feature learning and classification. In the first stage, we learn a new feature representation from data using SDAEs. Regarding the classification, we add a softmax regression layer on the top of the resulting hidden representation layer yielding a deep neural network (DNN). The proposed method fuses the results of several ECG leads (up to 12) in order to increase the detection accuracy. In the experiments, we use INCART database to test the proposed DNN multi-lead method. The obtained results are 98.6%, 91.4%, and 97.7% respectively for overall accuracy (OA), average sensitivity (Se), and average positive productivity (Pp).

UR - http://www.scopus.com/inward/record.url?scp=85057131453&partnerID=8YFLogxK

U2 - 10.1109/EIT.2018.8500197

DO - 10.1109/EIT.2018.8500197

M3 - Conference contribution

AN - SCOPUS:85057131453

T3 - IEEE International Conference on Electro Information Technology

SP - 169

EP - 173

BT - 2018 IEEE International Conference on Electro/Information Technology, EIT 2018

PB - IEEE Computer Society

T2 - 2018 IEEE International Conference on Electro/Information Technology, EIT 2018

Y2 - 3 May 2018 through 5 May 2018

ER -

Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal

Abstract

Publication series

Other

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this