EEG data compression to monitor DoA in telemedicine

Palendeng, Mario E. and Zhang, Qing and Pang, Chaoyi and Li, Yan (2012) EEG data compression to monitor DoA in telemedicine. In: 20th Australian National Health Informatics Conference (HIC 2012): Health Informatics: Building a Healthcare Future Through Trusted Information , 30 Jul-2 Aug 2012, Sydney, Australia.


Data compression techniques have been widely used to process and transmit huge amount of EEG data in real-time and remote EEG signal processing systems. In this paper we propose a lossy compression technique, F-shift, to compress EEG signals for remote depth of Anaesthesia (DoA) monitoring. Compared with traditional wavelet compression techniques, our method not only preserves valuable clinical information with high compression ratios, but also reduces high frequency noises in EEG signals. Moreover, our method has negligible compression overheads (less than 0.1 seconds), which can greatly benefit real-time EEG signal monitoring systems. Our extensive experiments demonstrate the efficiency and effectiveness of the proposed compression method.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: c. 2012 The authors and IOS Press. Permanent restricted access to published version due to publisher copyright policy. Series: Studies in Health Technology and Informatics v178
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering
Date Deposited: 25 Oct 2012 05:09
Last Modified: 27 Jun 2017 05:19
Uncontrolled Keywords: EEG data compression; depth of anaesthesia; telemedicine
Fields of Research : 11 Medical and Health Sciences > 1117 Public Health and Health Services > 111711 Health Information Systems (incl. Surveillance)
11 Medical and Health Sciences > 1103 Clinical Sciences > 110301 Anaesthesiology
08 Information and Computing Sciences > 0804 Data Format > 080403 Data Structures
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.3233/978-1-61499-078-9-163

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