Exploring prediction techniques for compression of EEG signals
Journal of Computing Sciences in Colleges
Context Based Error Modeling for Lossless Compression of EEG Signals Using Neural Networks
Journal of Medical Systems
Lossless and Near-Lossless Compression of Ecg Signals with Block-Sorting Techniques
International Journal of High Performance Computing Applications
Compressive sampling of EEG signals with finite rate of innovation
EURASIP Journal on Advances in Signal Processing
International Journal of Telemedicine and Applications
International Journal of Telemedicine and Applications
Design of heart rate variability processor for portable 3-lead ECG monitoring system-on-chip
Expert Systems with Applications: An International Journal
An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression
Computers in Biology and Medicine
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We study compression techniques for electroencephalograph (EEG) signals. A variety of lossless compression techniques, including compress, gzip, bzip, shorten, and several predictive coding methods, are investigated and compared. The methods range from simple dictionary based approaches to more sophisticated context modeling techniques. It is seen that compression ratios obtained by lossless compression are limited even with sophisticated context based bias cancellation and activity based conditional coding. Though lossy compression can yield significantly higher compression ratios while potentially preserving diagnostic accuracy, it is not usually employed due to legal concerns. Hence, we investigate a near lossless compression technique that gives quantitative bounds on the errors introduced during compression. It is observed that such a technique gives significantly higher compression ratios (up to 3-bit/sample saving with less than 1% error). Compression results are reported for EEG's recorded under various clinical conditions.