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Design and evaluation of a novel wireless three-pad ECG system for generating conventional 12-lead signals

Authors:

Victor C. M. LeungAuthors Info & Claims

BodyNets '10: Proceedings of the Fifth International Conference on Body Area Networks

Pages 84 - 90

https://doi.org/10.1145/2221924.2221942

Published: 10 September 2010 Publication History

Abstract

Electrocardiography (ECG) is a widely accepted approach for monitoring of cardiac activity and clinical diagnosis of heart diseases. In order to make ECG systems portable, easy to setup, comfortable to patients and tolerant of artifacts, wireless single-pad ECG systems have been developed. To tackle the problems raised by wireless single-pad ECG systems, we propose an upgraded version, the wireless three-pad ECG system (W3ECG). W3ECG furthers the pad design idea of the single-pad approach. We add two more pads to the W3ECG to gain spatial variety of heart activity. Signals obtained from these three pads, plus their placement information, make it possible to synthesize conventional 12-lead ECG signals. We provide one example of pad placement and evaluate its performance by examining ECG data of four patients available from online database. Feasibility test of our selected pad placement positions show comparable results with respect to the EASI lead system. Experimental results also exhibit high correlations between synthesized and directly observed 12-lead signals (9 out of 12 cross-correlation coefficients higher than 0.75).

References

[1]

PhysioNet/CinC Challenge 2007 Data Sets. http://www.physionet.org/challenge/2007/data/.

[2]

H. Cao, S. González-Valenzuela, and V. Leung. Employing ieee 802.15.4 for quality of service provisioning in wireless body area sensor networks. In Proceedings of The IEEE 24th International Conference on Advanced Information Networking and Application, 2010.

Digital Library

[3]

H. Cao, H. Li, L. Stocco, and V. Leung. Demonstration of a novel wireless three-pad ECG system for generating conventional 12-lead signals. In Proceedings of The 5th International Conference on Body Area Networks, 2010.

Digital Library

[4]

D. Q. Feild, C. L. Feldman, and B. M. Horacek. Improved EASI coefficients: Their derivation, values, and performance. Journal of Electrocardiology, 35:23--33, 2002.

[5]

R. Fensli, E. Gunnarson, and T. Gundersen. A wearable ECG-recording system for continuous arrhythmia monitoring in a wireless tele-home-care situation. In Proceedings of 18th IEEE Symposium on Computer-Based Medical Systems, 2005.

Digital Library

[6]

D. D. Finlay, C. D. Nugent, J. G. Kellett, M. P. Donnelly, P. J. McCullagh, and N. D. Black. Synthesising the 12-lead electrocardiogram: Trends and challenges. European Journal of Internal Medicine, 18(8):566--570, 2007.

[7]

D. D. Finlay, C. D. Nugent, P. J. McCullagh, and N. D. Black. Mining for diagnostic information in body surface potential maps: A comparison of feature selection techniques. BioMedical Engineering OnLine, 4, 2005.

[8]

E. Frank. General theory of heart-vector projection. Circulation Research, 2:258--270, 1954.

[9]

A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215--e220, 2000 (June 13). Circulation Electronic Pages: http://circ.ahajournals.org/cgi/content/full/101/23/e215.

[10]

R. Hoekema, G. J. H. Uijen, and A. van Oosterom. On selecting a body surface mapping procedure. Journal of Electrocardiology, 32(2):93--101, 1999.

[11]

B. M. Horacek, J. W. Warren, D. Q. Feild, and C. L. Feldman. Statistical and deterministic approaches to designing transformations of electrocardiographic leads. Journal of Electrocariology, 35:41--52, 2002.

[12]

B. M. Horacek, J. W. Warren, C. J. Penney, R. S. MacLeod, L. M. Title, M. J. Gardner, and C. L. Feldman. Optimal electrocardiographic leads for detecting acute myocardial ischemia. Journal of Electrocardiology, 34:97--111, 2001.

[13]

IMEC. http://www.imec.be/.

[14]

B. Khaddoumi, H. Rix, O. Meste, M. Fereniec, and R. Maniewski. Body surface ECG signal shape dispersion. Biomedical Engineering, IEEE Transactions on, 53:2491--2500, 2006.

[15]

C. Levkov. Derived 12 channel electrocardiogram from 4 channel holter electrocardiogram. In XI International Scientific and Applied Conference, 2002.

[16]

G. E. Mailloux and R. M. Gulraajani. Theoretical evaluation of the McFee and Frank vectorcardiographic lead systems using a numerical inhomogeneous torso model. Biomedical Engineering, IEEE Transactions on, 29:322--332, 1982.

[17]

M. Munshi, X. Xu, X. Zou, E. Soetiono, C. S. Teo, and Y. Lian. Wireless ECG plaster for body sensor network. In Proceedings of the 5th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2008) in conjunction with the 5th International Summer School and Symposium on Medical Devices and Biosensors (ISSS-MDBS 2008), 2008.

[18]

J. G. Webster, editor. Medical Instrumentation: Application and Design. Wiley, 3rd edition, 1997.

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https://doi.org/10.1109/EMBC40787.2023.10340565
Nankani DDutta Baruah R(2022)Improved Diagnostic Performance of Arrhythmia Classification Using Conditional GAN Augmented HeartbeatsGenerative Adversarial Learning: Architectures and Applications10.1007/978-3-030-91390-8_12(275-304)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-91390-8_12
Dasgupta SDas SBhattacharya U(2021)CardioGAN: An Attention-based Generative Adversarial Network for Generation of Electrocardiograms2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412905(3193-3200)Online publication date: 10-Jan-2021
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Index Terms

Design and evaluation of a novel wireless three-pad ECG system for generating conventional 12-lead signals
1. Information systems
  1. Information systems applications

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Information

Published In

cover image ACM Other conferences

BodyNets '10: Proceedings of the Fifth International Conference on Body Area Networks

September 2010

251 pages

ISBN:9781450300292

DOI:10.1145/2221924

General Chairs:
Victor C. M. Leung
The University of British Columbia
,
Athanasios V. Vasilakos
University of Western Macedonia
,
Program Chairs:
Thomas Falck
Philips Research Europe
,
Karim Qayumi
The University of British Columbia
,
Xinbing Wang
Shanghai Jiaotong University

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 September 2010

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Natural Sciences and Engineering Research Council of Canada

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BodyNets '10

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BodyNets '10: The Fifth International Conference on Body Area Networks

September 10 - 12, 2010

Corfu, Greece

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Cited By

Wang XSahiner BScully CCha K(2023) AFE-GAN: Synthesizing Electrocardiograms with Atrial Fibrillation Characteristics Using Generative Adversarial Networks * 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10340565(1-5)Online publication date: 24-Jul-2023
https://doi.org/10.1109/EMBC40787.2023.10340565
Nankani DDutta Baruah R(2022)Improved Diagnostic Performance of Arrhythmia Classification Using Conditional GAN Augmented HeartbeatsGenerative Adversarial Learning: Architectures and Applications10.1007/978-3-030-91390-8_12(275-304)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-91390-8_12
Dasgupta SDas SBhattacharya U(2021)CardioGAN: An Attention-based Generative Adversarial Network for Generation of Electrocardiograms2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412905(3193-3200)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9412905
Nankani DBaruah R(2020)Investigating Deep Convolution Conditional GANs for Electrocardiogram Generation2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207613(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/IJCNN48605.2020.9207613
Ye FZhu FFu YShen B(2019)ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy GradientIEEE Access10.1109/ACCESS.2019.29503837(159369-159378)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2950383
Zhu FYe FFu YLiu QShen B(2019)Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial networkScientific Reports10.1038/s41598-019-42516-z9:1Online publication date: 1-May-2019
https://doi.org/10.1038/s41598-019-42516-z
Arumugam MSangaiah A(2015)System for the Detection and Reporting of Cardiac Event Using Embedded SystemsEmerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 110.1007/978-3-319-13728-5_66(587-594)Online publication date: 2015
https://doi.org/10.1007/978-3-319-13728-5_66
Crosby GChin CGhosh TMurimi R(2015)Wireless Body Area Networks in mHealthMobile Health10.1007/978-3-319-12817-7_37(873-915)Online publication date: 2015
https://doi.org/10.1007/978-3-319-12817-7_37
Hadjem MSalem ONait Abdesselam FMehaoua A(2013)Early detection of Myocardial Infarction using WBAN2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013)10.1109/HealthCom.2013.6720654(135-139)Online publication date: Oct-2013
https://doi.org/10.1109/HealthCom.2013.6720654
González-Valenzuela SLiang XCao HChen MLeung V(2012)Body Area NetworksAutonomous Sensor Networks10.1007/5346_2012_26(17-37)Online publication date: 14-Aug-2012
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