research-article

Design and implementation of a personal health monitoring system with an effective SVM-based PVC detection algorithm in cardiology

Authors:

Ting-Chun KuoAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 33 - 35

https://doi.org/10.1145/2554850.2555118

Published: 24 March 2014 Publication History

Get Access

Abstract

In this paper, we present a bio-health monitoring system prototype specialized in capturing the Premature Ventricular Contraction (PVC) event, one of the major cardiac disorder events. The proposed bio-health system comprises three parts: (1) the Electrocardiograph (ECG) sensing hardware, (2) the Android-based processing and communication device, and (3) the expert system on the cloud to detect PVC events. The expert system on the cloud is designed and implemented based on the support vector machine (SVM). The effective identification of PVC can help patients take care of the health quickly. The main purpose of recording information is to track the patient's health status, and allow the medical team to keep tracking the recovery status.

References

[1]

DeToledo, P., Jimenez, S., del Pozo, F., and Roca, J. Telemedicine Experience for Chronic Care in COPD. IEEE Transactions on Information Technology in Biomedicine, vol. 10, issue 3, July 2006, 567--573.

Digital Library

Google Scholar

[2]

Sung, W.-T., Chen, J.-H. and Chang, K.-W. Mobile Physiological Measurement Platform with Cloud and Analysis Functions Implemented via IPSO. IEEE Sensors Journal, vol. 14, issue 1, January 2014, 111--123.

Crossref

Google Scholar

[3]

Wang, L.-H., Chen, T.-Y., Lee, S.-Y. and Chen, H. Implementation of a Personal Health Monitoring System in Cardiology Application. 2012 IEEE Asia Pacific Conference on Circuits and Systems, pp. 236--239, December 2012.

Crossref

Google Scholar

[4]

Texas Instruments. MSP430x1xx Family User's Guide, 2006. http://www.ti.com/lit/ug/slau049f/slau049f.pdf

Google Scholar

[5]

Android. http://www.android.com/

Google Scholar

[6]

BIOPAC Systems, Inc. ECG Electrocardiogram Amplifier - ECG100C. http://www.biopac.com/ecg-electrocardiogram-amplifier

Google Scholar

[7]

So, H. H. and Chan, K. L. Development of QRS detection method for real-time ambulatory cardiac monitor. 19-th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, 1997, 289--292.

Google Scholar

[8]

Cortes, C. and Vapnik, V. Support-Vector Networks. Machine Learning, vol. 20, issue 3, September 1995, 273--297.

Digital Library

Google Scholar

[9]

Hsu, C. W. and Lin, C. J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, vol. 13, issue 2, March 2002, 415--425.

Digital Library

Google Scholar

[10]

Zhang, J. M., Zhang, Y., and Yang, B. Adaptive algorithm for PVC recognition based on multiple template matching. Computer Engineering and Design, August 2011.

Google Scholar

[11]

Chang, C.-C. and Lin, C.-J. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, issue 3, no. 27, April 2011.

Digital Library

Google Scholar

[12]

Wang, Y., Plataniotis, K. N. and Hatzinakos, D. Integrating Analytic and Appearance Attributes for Human Identification from ECG Signals. 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, 2006.

Crossref

Google Scholar

[13]

MIT-BIH, MIT-BIH Arrhythmia Database. http://www.physionet.org/physiobank/database/mitdb/

Google Scholar

[14]

Cooke, T. Two variations on fisher's linear discriminant for pattern recognition. IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 2, February 2002.

Digital Library

Google Scholar

Cited By

View all

Chen HCheng BLiao GKuo T(2014)Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare managementJournal of Visual Languages and Computing10.1016/j.jvlc.2014.09.01225:6(745-753)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.jvlc.2014.09.012

Index Terms

Design and implementation of a personal health monitoring system with an effective SVM-based PVC detection algorithm in cardiology
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

Recommendations

Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare management

The self-regulation ability of the elderly is largely degenerated with the age increases, and the elderly often expose to great potential hazards of heart disorders. In practice, the electrocardiography (ECG) is one of the well-known non-invasive ...
Automatic identification of cardiac health using modeling techniques: A comparative study

Heart rate variability (HRV), a widely adopted quantitative marker of the autonomic nervous system can be used as a predictor of risk of cardiovascular diseases. Moreover, decreased heart rate variability (HRV) has been associated with an increased risk ...
Detection of premature ventricular contraction (PVC) using linear and nonlinear techniques: an experimental study
Abstract
Cardiovascular diseases are identified as one of the most dangerous diseases and the major causes of the death around the world. One of the most common cardiac arrhythmias that have always been a concern for cardiologists is premature ventricular ...

Comments

Information & Contributors

Information

Published In

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Council Taiwan

Conference

SAC 2014

Sponsor:

SIGAPP

SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

Acceptance Rates

SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
192
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chen HCheng BLiao GKuo T(2014)Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare managementJournal of Visual Languages and Computing10.1016/j.jvlc.2014.09.01225:6(745-753)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.jvlc.2014.09.012

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare management

Automatic identification of cardiac health using modeling techniques: A comparative study

Detection of premature ventricular contraction (PVC) using linear and nonlinear techniques: an experimental study

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare management

Automatic identification of cardiac health using modeling techniques: A comparative study

Detection of premature ventricular contraction (PVC) using linear and nonlinear techniques: an experimental study

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations