A High Performance Scheme for EEG Compression Using a Multichannel Model
The amount of data contained in electroencephalogram (EEG) recordings is quite massive and this places constraints on bandwidth and storage. The requirement of online transmission of data needs a scheme that allows higher performance with lower computation. Single channel algorithms, when applied on multichannel EEG data fail to meet this requirement. While there have been many methods proposed for multichannel ECG compression, not much work appears to have been done in the area of multichannel EEG compression. In this paper, we present an EEG compression algorithm based on a multichannel model, which gives higher performance compared to other algorithms. Simulations have been performed on both normal and pathological EEG data and it is observed that a high compression ratio with very large SNR is obtained in both cases. The reconstructed signals are found to match the original signals very closely, thus confirming that diagnostic information is being preserved during transmission.
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- EEG data compression techniques, Antoniol, G.; Tonella, P., IEEE Transactions on Biomedical Engineering, Volume: 44 Issue: 2, Feb. 1997, Page(s): 105–114Google Scholar
- Recurrent neural network predictors for EEG signal compression, Bartolini, F.; Cappellini, V.; Nerozzi, S.; Mecocci, A., International Conference on Acoustics, Speech, and Signal Processing, 1995. ICASSP-95 Volume: 5, 1995 Page(s): 3395–3398.Google Scholar
- Tree structured filter bank for time-frequency decomposition of EEG signals, Sijercic, Z.; Agarwal, G., IEEE 17th Annual Conference Engineering in Medicine and Biology Society 1995, Volume: 2, 1995, Page(s): 991–992.Google Scholar
- Spatio-temporal EEG information transfer in an episode of epilepsy, A.M. Albano et al Nonlinear Dynamics and Brain Functioning (eds.-N. Pradhan, P.E. Rapp and R. Sreenivasan), Nova Science Publishers, Newyork, 199, Page(s): 411–434.Google Scholar
- Entropy of brain rhythms: normal versus injury EEG, Thakor, N.V.; Paul, J.; Tong, S.; Zhu, Y.; Bezerianos, A., Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, 2001, Page(s): 261–264Google Scholar
- A multichannel template based data compression algorithm, Paggetti, C.; Lusini, M.; Varanini, M.; Taddei, A.; Marchesi, C., Computers in Cardiology 1994, Page(s): 629–632Google Scholar
- Multichannel ECG data compression method based on a new modeling method Prieto, A.; Mailhes, C., Computers in Cardiology, 2001, Page(s): 261–264Google Scholar
- Gilbert Strang and Truong Ngugen.: Wavelets and Filterbanks, Cambridge University Press, 1996.Google Scholar