Context-based lossless and near-lossless compression of EEG signals

Nasir Memon, Xuan Kong, Judit Cinkler

    Research output: Contribution to journalArticle

    Abstract

    In this paper, 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.

    Original languageEnglish (US)
    Pages (from-to)231-238
    Number of pages8
    JournalIEEE Transactions on Information Technology in Biomedicine
    Volume3
    Issue number3
    DOIs
    StatePublished - 1999

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    Glossaries
    Electroencephalography

    ASJC Scopus subject areas

    • Health Informatics
    • Health Information Management
    • Information Systems
    • Computer Science Applications
    • Computational Theory and Mathematics

    Cite this

    Context-based lossless and near-lossless compression of EEG signals. / Memon, Nasir; Kong, Xuan; Cinkler, Judit.

    In: IEEE Transactions on Information Technology in Biomedicine, Vol. 3, No. 3, 1999, p. 231-238.

    Research output: Contribution to journalArticle

    Memon, Nasir ; Kong, Xuan ; Cinkler, Judit. / Context-based lossless and near-lossless compression of EEG signals. In: IEEE Transactions on Information Technology in Biomedicine. 1999 ; Vol. 3, No. 3. pp. 231-238.
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