This Article 
 Bibliographic References 
 Add to: 
Adaptive Electrocardiogram Feature Extraction on Distributed Embedded Systems
August 2006 (vol. 17 no. 8)
pp. 797-807

Abstract—Tiny embedded systems have not been an ideal outfit for high performance computing due to their constrained resources. Limitations in processing power, battery life, communication bandwidth, and memory constrain the applicability of existing complex medical analysis algorithms such as the Electrocardiogram (ECG) analysis. Among various limitations, battery lifetime has been a major key technological constraint. In this paper, we address the issue of partitioning such a complex algorithm while the energy consumption due to wireless transmission is minimized. ECG analysis algorithms normally consist of preprocessing, pattern recognition, and classification. Considering the orientation of the ECG leads, we devise a technique to perform preprocessing and pattern recognition locally in small embedded systems attached to the leads. The features detected in the pattern recognition phase are considered for the classification. Ideally, if the features detected for each heartbeat reside in a single processing node, the transmission will be unnecessary. Otherwise, to perform classification, the features must be gathered on a local node and, thus, the communication is inevitable. We perform such a feature grouping by modeling the problem as a hypergraph and applying partitioning schemes which yield a significant power saving in wireless communications. Furthermore, we utilize dynamic reconfiguration by software module migration. This technique, with respect to partitioning, enhances the overall power saving in such systems. Moreover, it adaptively alters the system configuration in various environments and on different patients. We evaluate the effectiveness of our proposed techniques on MIT/BIH benchmarks and, on average, achieve 70 percent energy saving.

[1] D. Meoli and T. May-Plumlee, “Interactive Electronic Textile Development: A Review of Technologies,” J. Textile and Apparel, Technology and Management, vol. 2, no. 2, 2002.
[2] Sensatex, http:/, 2006.
[3] S. Park, K. Mackenzie, and S. Jayaraman, “The Wearable Motherboard: A Framework for Personalized Mobile Information Processing (PMIP),” Proc. 39th Design Automation Conf., pp. 170-174, 2002.
[4] R. DeVaul, J.G.M. Sung, and A. Pentland, “Mithril 2003: Applications and Architecture,” Wearable Computers, Proc. Seventh IEEE Int'l Symp., pp. 4-11, 2003.
[5] D. Marculescu, R. Marculescu, and P. Khosla, “Challenges and Opportunities in Electronic Textiles Modeling and Optimization,” Proc. 39th Design Automation Conf., pp. 175-180, 2002.
[6] T. Martin, M. Jones, J. Edmison, and R. Shenoy, “Towards a Design Framework for Wearable Electronic Textiles,” Wearable Computers, Proc. Seventh IEEE Int'l Symp., pp. 190-199, 2003.
[7] R. Jafari, A. Encarnacao, A. Zahoory, F. Dabiri, H. Noshadi, and M. Sarrafzadeh, “Wireless Sensor Networks for Health Monitoring,” MobiQuitous '05: Proc. Second Ann. Int'l Conf. Mobile and Ubiquitous Systems, 2005.
[8] R. Jafari, F. Dabiri, P. Brisk, and M. Sarrafzadeh, “Adaptive and Fault Tolerant Medical Vest for Life-Critical Medical Monitoring,” SAC '05: Proc. 2005 ACM Symp. Applied Computing, pp. 272-279, 2005.
[9] Lifeguard Monitoring System, http:/, 2006.
[10] H. Kautz, O. Etzioni, D. Fox, and D. Weld, “Foundations of Assisted Cognition Systems,” technical report, Univ. of Washington, Computer Science Dept., 2003.
[11] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, second ed., John Wiley and Sons, Inc., Jan. 2000.
[12] A.K. Jain, R.P.W. Duin, and J. Mao, “Statistical Pattern Recognition: A Review,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, 2000.
[13] A. Graps, “An Introduction to Wavelets,” IEEE Computational Sciences and Eng., vol. 2, no. 2, pp. 50-61, 1995.
[14] T. Pavlidis, Structural Pattern Recognition, series in electrophysics, Springer-Verlag, vol. 1, 1977.
[15] Crossbow Technology Inc., http:/, 2006.
[16] J. Pan and W.J. Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Trans. Biomedical Eng., vol. 32, no. 3, pp. 230-236, 1985.
[17] P. Laguna, R.G. Mark, A. Goldberger, and G.B. Moody, “A Database for Evaluation of Algorithms for Measurement of QT and Other Waveform Intervals in the ECG,” pp. 673-676, 1997.
[18] P. de Chazal, M. O'Dwyer, and R.B. Reilly, “Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features,” IEEE Trans. Biomedical Eng., vol. 51, no. 7, pp. 1196-1206, 2004.
[19] I. Christov and G. Bortolan, “Ranking of Pattern Recognition Parameters for Premature Ventricular Contractions Classification by Neural Networks,” Physiological Measurement, vol. 25, no. 5, pp. 1281-1290, 2004.
[20] G.B. Moody and R.G. Mark, “The MIT-BIH Arrhythmia Database on CD-ROM and Software for Use with It,” Computers in Cardiology, pp. 185-188, 1990.
[21] B.L. Titzer, D. Lee, and J. Palsberg, “Avrora: Scalable Sensor Network Simulation with Precise Timing,” IPSN '05, Proc. Fourth Int'l Conf. Information Processing in Sensor Networks, 2005.
[22] V. Shnayder, M. Hempstead, B. Rong Chen, G.W. Allen, and M. Welsh, “Simulating the Power Consumption of Large-Scale Sensor Network Applications,” SenSys '04: Proc. Second Int'l Conf. Embedded Networked Sensor Systems, pp. 188-200, 2004.
[23] P. Levis, S. Madden, D. Gay, J. Polastre, R. Szewczyk, A. Woo, E. Brewer, and D. Culler, “The Emergence of Networking Abstractions and Techniques in Tiny OS,” Proc. First Symp. Networked System Design and Implementation (NSDI '04), pp. 1-14, 2004.
[24] D. Gay, P. Levis, R. von Behren, M. Welsh, E. Brewer, and D. Culler, “The NESC Language: A Holistic Approach to Networked Embedded Systems,” PLDI '03: Proc. ACM SIGPLAN 2003 Conf. Programming Language Design and Implementation, pp. 1-11, 2003.
[25] C.-C. Han, R. Kumar, R. Shea, E. Kohler, and M. Srivastava, “A Dynamic Operating System for Sensor Nodes,” MobiSys '05: Proc. Third Int'l Conf. Mobile Systems, Applications, and Services, pp. 163-176, 2005.
[26] S. Dutt and W. Deng, “A Probability-Based Approach to VLSI Circuit Partitioning,” DAC '96: Proc. 33rd Ann. Conf. Design Automation, pp. 100-105, 1996.
[27] M.R. Garey and D.S. Johnson, Computers and Instractability: A Guide to the Theory of NP-Completeness. San Francisco, Calif.: W.H. Freeman, 1979.
[28] G. Karypis, R. Aggarwal, V. Kumar, and S. Shekhar, “Multilevel Hypergraph Partitioning: Application in VLSI Domain,” DAC '97: Proc. 34th Ann. Conf. Design Automation, pp. 526-529, 1997.
[29] “Physiobank— Physiologic Signal Archives for Biomedical Research,” http://www.physionet.orgphysiobank/, 2006.

Index Terms:
Computational biology, ECG analysis, embedded systems, feature extraction.
Roozbeh Jafari, Hyduke Noshadi, Soheil Ghiasi, Majid Sarrafzadeh, "Adaptive Electrocardiogram Feature Extraction on Distributed Embedded Systems," IEEE Transactions on Parallel and Distributed Systems, vol. 17, no. 8, pp. 797-807, Aug. 2006, doi:10.1109/TPDS.2006.96
Usage of this product signifies your acceptance of the Terms of Use.