DocumentCode :
78266
Title :
Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions
Author :
Dauwels, Justin ; Srinivasan, K. ; Reddy, M.R. ; Cichocki, Andrzej
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
Volume :
17
Issue :
3
fYear :
2013
fDate :
May-13
Firstpage :
708
Lastpage :
714
Abstract :
A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.
Keywords :
electroencephalography; encoding; matrix decomposition; medical signal processing; signal reconstruction; signal resolution; signal sampling; tensors; MC-EEG; arithmetic coding; intracranial EEG dataset; lossy plus residual coding; matrix-tensor decomposition models; matrix-tensor decomposition-based coder; multichannel electroencephalogram; near-lossless multichannel EEG compression algorithm; reconstructed signals; signal resolution; signal sampling rate; Compression algorithms; Correlation; Distortion measurement; Electroencephalography; Encoding; Matrix decomposition; Tensile stress; Arithmetic coding; compression; electroencephalogram (EEG); multichannel EEG; parallel factor decomposition (PARAFAC); singular value decomposition (SVD);
fLanguage :
English
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
Publisher :
ieee
ISSN :
2168-2194
Type :
jour
DOI :
10.1109/TITB.2012.2230012
Filename :
6363503
Link To Document :
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