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Near-lossless multichannel EEG compression based on matrix and tensor decompositions
Published in
2013
PMID: 24592471
Volume: 17
   
Issue: 3
Pages: 708 - 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. © 2013 IEEE.
About the journal
JournalIEEE Journal of Biomedical and Health Informatics
ISSN21682194
Open AccessYes
Concepts (21)
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    ARITHMETIC CODING
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    ELECTRO-ENCEPHALOGRAM (EEG)
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    MAXIMUM ABSOLUTE ERROR
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    MULTICHANNEL EEG
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    MULTICHANNEL ELECTROENCEPHALOGRAMS
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    NEAR LOSSLESS COMPRESSION
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    PARAFAC
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    TEMPORAL AND SPATIAL CORRELATION
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    Algorithms
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    Compaction
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    Digital arithmetic
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    Singular value decomposition
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    Electroencephalography
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    Algorithm
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    Article
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    Equipment
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    Human
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    Methodology
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    Signal processing
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    Humans
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    Signal processing, computer-assisted