ACM Home Page
Please provide us with feedback. Feedback
MPSoC ECG biochip: a multiprocessor system-on-chip for real-time human heart monitoring and analysis
Full text PdfPdf (353 KB)
Source Conference On Computing Frontiers archive
Proceedings of the 3rd conference on Computing frontiers table of contents
Ischia, Italy
SESSION: Multithreaded, multicore, and SoC systems table of contents
Pages: 21 - 28  
Year of Publication: 2006
ISBN:1-59593-302-6
Authors
Iyad Al Khatib  Royal Institute of Technology, Stockholm, Sweden
Davide Bertozzi  University of Ferrara, Ferrara, Italy
Francesco Poletti  University of Bologna, Bologna, Italy
Luca Benini  University of Bologna, Bologna, Italy
Axel Jantsch  Royal Institute of Technology (KTH), Stockholm, Sweden
Mohamed Bechara  American University of Beirut Beirut, Lebanon
Hasan Khalifeh  American University of Beirut Beirut, Lebanon
Mazen Hajjar  American University of Beirut Beirut, Lebanon
Rustam Nabiev  Karolinska University Hospital, Sweden
Sven Jonsson  Karolinska University Hospital, Sweden
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 13,   Downloads (12 Months): 117,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1128022.1128028
What is a DOI?

ABSTRACT

The interest in high performance chip architectures for biomedical applications is on the rise. Heart diseases remain by far the main cause of death and a challenging problem for biomedical engineers to monitor and analyze. Electrocardiography (ECG) is an essential practice in heart medicine, which faces computational challenges, especially when 12 lead signals are to be analyzed in parallel, in real time, and under increasing sampling frequencies. Another challenge is the analysis of huge amounts of data that may grow to days of recordings. Nowadays, doctors use eyeball monitoring of the 12-lead ECG paper readout, which may seriously impair analysis accuracy. Our solution leverages the advance in multi-processor system-on-chip architectures, and is centered on the parallelization of the ECG computation kernel. It improves upon state-of-the-art mostly for its capability to perform real-time analysis of input data, leveraging the computation horsepower provided by many concurrent DSPs, more accurate diagnosis of cardiac diseases, and prompter reaction to abnormal heart alterations. The design methodology to go from the 12-lead ECG application specification to the final hardware/software architecture, modeling, and simulation is the focus of this paper. Our system model is based on industrial components. The architectural template we employ is scalable and flexible.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Fuster, V., Epidemic of Cardiovascular Disease and Stroke: The Three Main Challenges, Circulation, Vol. 99, Issue 9, March 1999, pp. 1132--1137.
 
2
Heart and Stroke Foundation of Canada, The Changing Face of Heart Disease and Stroke in Canada 2000, Annal report, 1999.
 
3
Chan, C., Han, J., and Ramjeet, D., LabVIEWTM Design of a Vectorcardiograph and 12-Lead ECG Monitor, Final Year Project for the Bachelor of Science Degree in the University of Manitoba, March 2003.
 
4
Ambu, Inc. biomedical devices company, www.ambuusa.com
 
5
Harland, C., Clark T., and Prance, R., Electric Potential Probes- New Directions in the remote sensing of the human body, Measurement Science and Technology, Vol. 13, 2002, pp. 163--169.
 
6
Harland, C., Clark, T., and Prance, R., High resolution ambulatory electrocardiographic monitoring using wrist-mounted electric potential sensors, Measurement Science and Technology, Vol. 14, 2003, pp. 923--928.
 
7
Malmivuo, J., and Plonsey, R., Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields, Oxford University Press, 1995.
 
8
Chevrollier, N., and Golmie, N., On the Use of Wireless Network Technologies in Healthcare Environments, In Proceedings of the fifth IEEE workshop on Applications and Services in Wireless Networks (ASWN 2005), June 2005, pp. 147--152.Loghi, M., Poncino, M., and Benini, L., Cycle-Accurate Power Analysis for Multiprocessor Systems-on-a-Chip, In GLSVLSI04: Great Lake Symposium on VLSI, April 2004, pp.401--406.
9
 
10
 
11
Lo, B., Thiemjarus, S., King R., and Yang, G., Body Sensor Network-A Wireless Sensor Platform for Pervasive Healthcare Monitoring, In Adjunct Proceedings of the 3rd International Conference on Pervasive Computing (PERVASIVE'05), May 2005, pp.77--80.
 
12
Association of Cardiac Technology in Victoria, ACTIV, http://www.activinc.org.au/
 
13
Code Blue, Wireless Sensor Networks for Medical Care, http://www.eecs.harvard.edu/~mdw/proj/codeblue/
 
14
BIOPAC Systems Inc., http://biopac.com/
 
15
Company-Bosch, E., Hartmann, E., ECG Front-End Design is Simplified with MicroConverter, Journal of Analog Dialogue, Vol. 37, November 2003.
 
16
PhysioBank, physiologic signal archives, for biomedical research, http://www.physionet.org/physiobank/database/ptbdb/
 
17
 
18
ARM DAI 0033A, Note 33, Fixed Point Arithmetic on the ARM, September 1996.


Collaborative Colleagues:
Iyad Al Khatib: colleagues
Davide Bertozzi: colleagues
Francesco Poletti: colleagues
Luca Benini: colleagues
Axel Jantsch: colleagues
Mohamed Bechara: colleagues
Hasan Khalifeh: colleagues
Mazen Hajjar: colleagues
Rustam Nabiev: colleagues
Sven Jonsson: colleagues