Abstract
Recently, wireless sensor networks have been proposed for assisted living and residential monitoring. In such networks, physiological sensors are used to monitor vital signs e.g. heartbeats, pulse rates, oxygen saturation of senior citizens. Sensor data is sent periodically via wireless links to a personal computer that analyzes the data. In this paper, we propose an anomaly detection scheme based on time series analysis that will allow the computer to determine whether a stream of real-time sensor data contains any abnormal heartbeats. If anomaly exists, that time series segment will be transmitted via the network to a physician so that he/she can further diagnose the problem and take appropriate actions. When tested against the heartbeat data readings stored at the MIT database, our ECG anomaly scheme is shown to have better performance than another scheme that has been recently proposed. Our scheme enjoys an accuracy rate that varies from 70-90% while the other scheme has an accuracy that varies from 40-70%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wood, et al.: ALARM-NET: Wireless Sensor Networks for Assisted-Living Residential Monitoring, University of Virginia Computer Science Department Technical Report (2006)
Shnayer, V., et al.: Sensor Networks for Medical Care, Harvard University Division of Engineering and Applied Sciences Technical Report, TR-08-05 (2005)
MIT-BIH Arrhythmia Database: http://www.physionet.org/physiobank/database/mitdb/
Keogh, E., Lin, J., Fu, A.: HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In: The 5th IEEE International Conference on Data Mining (ICDM), IEEE Computer Society Press, Los Alamitos (2005)
Thaler, M.S.: The only EKG book you’ll ever need, 3rd edn. Lippincott Williams & Wilkins, Philadelphia, PA (1999)
Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic Classification of Heartbeats Using ECG Morpholgy and Heartbeat Interval Features. IEEE transaction on biomedical engineering 51(7) (July 2004)
Evans, S., Hastings, H., Bodenheimer, M.: Differentiation of beats of ventricular and sinus origin using a self-training neural network. PACE 17, 611–626 (1994)
Clayton, R., Murray, A., Campbell, R.: Recognition of ventricular fibrillation using neural networks. Med. Biological Engineering and Computing 32, 217–220 (1994)
Lehigh’s Sensor-Based Medical Information System (SBMIS)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH Arrhythmia Database. IEEE Eng. in Med. and Biol. 20(3), 45–50 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chuah, M.C., Fu, F. (2007). ECG Anomaly Detection via Time Series Analysis. In: Thulasiraman, P., He, X., Xu, T.L., Denko, M.K., Thulasiram, R.K., Yang, L.T. (eds) Frontiers of High Performance Computing and Networking ISPA 2007 Workshops. ISPA 2007. Lecture Notes in Computer Science, vol 4743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74767-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-74767-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74766-6
Online ISBN: 978-3-540-74767-3
eBook Packages: Computer ScienceComputer Science (R0)