DocumentCode :
517787
Title :
Low power compression of EEG signals using JPEG2000
Author :
Higgins, Garry ; McGinley, B. ; Glavin, Martin ; Jones, Edward
Author_Institution :
Bioelectronics Res. Cluster, Nat. Univ. of Ireland Galway, Galway, Ireland
fYear :
2010
fDate :
22-25 March 2010
Firstpage :
1
Lastpage :
4
Abstract :
This paper outlines a scheme for compressing EEG signals based on the JPEG2000 image compression algorithm. Such a scheme could be used to compress signals in an ambulatory system, where low-power operation is important to conserve battery life; therefore, a high compression ratio is desirable to reduce the amount of data that needs to be transmitted. The JPEG2000 specification makes use of the wavelet transform, which can be efficiently implemented in embedded systems. The standard was broken down to its core components and adapted for use on EEG signals with additional compression steps added. Variations on the components were tested to maximize compression ratio (CR) while maintaining a low percentage root-mean-squared difference (PRD) and minimize power requirements. Initial tests indicate that the algorithm performs well in relation to other EEG compression methods proposed in the literature.
Keywords :
data compression; electroencephalography; image coding; mean square error methods; medical image processing; wavelet transforms; EEG signal compression; JPEG2000; image compression; low power compression; multichannel electroencephalogram; percentage root mean squared difference; wavelet transform; Chromium; Electric variables measurement; Electroencephalography; Energy consumption; Image coding; Medical services; Patient monitoring; Remote monitoring; Testing; Transform coding; EEG compression; JPEG2000; Wavelets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on-NO PERMISSIONS
Conference_Location :
Munich
Print_ISBN :
978-963-9799-89-9
Electronic_ISBN :
978-963-9799-89-9
Type :
conf
DOI :
10.4108/ICST.PERVASIVEHEALTH2010.8861
Filename :
5482256
Link To Document :
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