skip to main content
article

An autonomic cloud environment for hosting ECG data analysis services

Published: 01 January 2012 Publication History

Abstract

Advances in sensor technology, personal mobile devices, wireless broadband communications, and Cloud computing are enabling real-time collection and dissemination of personal health data to patients and health-care professionals anytime and from anywhere. Personal mobile devices, such as PDAs and mobile phones, are becoming more powerful in terms of processing capabilities and information management and play a major role in peoples daily lives. This technological advancement has led us to design a real-time health monitoring and analysis system that is Scalable and Economical for people who require frequent monitoring of their health. In this paper, we focus on the design aspects of an autonomic Cloud environment that collects peoples health data and disseminates them to a Cloud-based information repository and facilitates analysis on the data using software services hosted in the Cloud. To evaluate the software design we have developed a prototype system that we use as an experimental testbed on a specific use case, namely, the collection of electrocardiogram (ECG) data obtained at real-time from volunteers to perform basic ECG beat analysis.

References

[1]
National Academy of Engineering, Grand challenges for engineering. URL: http://www.engineeringchallenges.org (accessed July 2010).
[2]
Jones, V., Halteren, A.V., Widya, I., Dokovsky, N., Bults, R., Konstantas, D. and Herzog, R., . In: Topics in Biomedical Engineering, Springer US, Boston, MA.
[3]
The principles of software QRS detection. IEEE Engineering in Medicine and Biology Magazine. v21 i1. 42-57.
[4]
DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis. Computer Methods and Programs in Biomedicine. v52 i1. 35-44.
[5]
Hu, Y., Tompkins, W., Urrusti, J. and Afonso, V., Applications of artificial neural networks for ECG signal detection and classification. Journal of Electrocardiology. v26. 66-73.
[6]
Deelman, E., Singh, G., Livny, M., Berriman, B. and Good, J., The cost of doing science on the cloud: the montage example. In: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, IEEE Press. pp. 1-12.
[7]
Qiu, X., Ekanayake, J., Beason, S., Gunarathne, T., Fox, G., Barga, R. and Gannon, D., Cloud technologies for bioinformatics applications. In: Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers, ACM. pp. 1-10.
[8]
Varia, J., Cloud Computing: Principles and Paradigms. 2010. Wiley, New York, USA.
[9]
A. Ranabahu, M. Maximilien, A best practice model for cloud middleware systems, in: Proceedings of the Best Practices in Cloud Computing: Designing for the Cloud, ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA, Orlando, FL, USA, 2009.
[10]
Buyya, R., Pandey, S. and Vecchiola, C., Cloudbus toolkit for market-oriented cloud computing. In: LNCS, vol. 5931. Springer, Germany. pp. 24-44.
[11]
Chow, R., Golle, P., Jakobsson, M., Shi, E., Staddon, J., Masuoka, R. and Molina, J., Controlling data in the cloud: outsourcing computation without outsourcing control. In: Proceedings of the 2009 ACM Workshop on Cloud Computing Security, ACM, New York, NY, USA. pp. 85-90.
[12]
Li, W. and Ping, L., Trust model to enhance security and interoperability of cloud environment. In: Proceedings of the 1st International Conference on Cloud Computing, Springer-Verlag, Berlin, Heidelberg. pp. 69-79.
[13]
Pearson, S., Shen, Y. and Mowbray, M., A privacy manager for cloud computing. In: Proceedings of the 1st International Conference on Cloud Computing, Springer-Verlag, Berlin, Heidelberg. pp. 90-106.
[14]
PhysioNet. URL: http://www.physionet.org/tutorials/hrv/¿(accessed April 2010).
[15]
G.D. Clifford, Advanced methods & tools for ECG data analysis. URL: http://www.robots.ox.ac.uk/~gari/ecgbook.html'(accessed April 2010).
[16]
F.G. Yanowitz, A method of ECG interpretation. http://library.med.utah.edu/kw/ecg/ecg_outline/Lesson2/index.html'(accessed April 2010).
[17]
Pandey, S., Voorsluys, W., Rahman, M., Buyya, R., Dobson, J. and Chiu, K., A Grid workflow environment for brain imaging analysis on distributed systems. Concurrency and Computation: Practice & Experience. v21 i16. 2118-2139.
[18]
Vecchiola, C., Chu, X. and Buyya, R., High Speed and Large Scale Scientific Computing. 2009. ISBN:978-1-60750-073-5. IOS Press.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Future Generation Computer Systems
Future Generation Computer Systems  Volume 28, Issue 1
January, 2012
338 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2012

Author Tags

  1. Autonomic middleware
  2. Cloud computing
  3. ECG analysis

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 26 Dec 2024

Other Metrics

Citations

Cited By

View all

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media