Lossless and near-lossless compression of EEG signals

Judit Cinkler, Xuan Kong, Nasir Memon

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper we study compression techniques for electroencelograph (EEG) signals. A variety of lossless compression techniques, ranging from simple dictionary based approaches to more sophisticated context modeling techniques based on recent work in lossless image coding are investigated and compared. It is seen that compression ratios obtained by lossless compression are limited. 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 near-lossless compression techniques that give quantitative bounds on the errors introduced during compression. It is observed that such techniques give significantly higher compression ratios. Simulation results with a large variety of data sets are reported.

    Original languageEnglish (US)
    Title of host publicationConference Record of the Asilomar Conference on Signals, Systems and Computers
    EditorsM.P. Farques, R.D. Hippenstiel
    PublisherIEEE Comp Soc
    Pages1432-1436
    Number of pages5
    Volume2
    StatePublished - 1998
    EventProceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
    Duration: Nov 2 1997Nov 5 1997

    Other

    OtherProceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2)
    CityPacific Grove, CA, USA
    Period11/2/9711/5/97

    Fingerprint

    Glossaries
    Image coding

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Signal Processing
    • Electrical and Electronic Engineering

    Cite this

    Cinkler, J., Kong, X., & Memon, N. (1998). Lossless and near-lossless compression of EEG signals. In M. P. Farques, & R. D. Hippenstiel (Eds.), Conference Record of the Asilomar Conference on Signals, Systems and Computers (Vol. 2, pp. 1432-1436). IEEE Comp Soc.

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

    Conference Record of the Asilomar Conference on Signals, Systems and Computers. ed. / M.P. Farques; R.D. Hippenstiel. Vol. 2 IEEE Comp Soc, 1998. p. 1432-1436.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Cinkler, J, Kong, X & Memon, N 1998, Lossless and near-lossless compression of EEG signals. in MP Farques & RD Hippenstiel (eds), Conference Record of the Asilomar Conference on Signals, Systems and Computers. vol. 2, IEEE Comp Soc, pp. 1432-1436, Proceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2), Pacific Grove, CA, USA, 11/2/97.
    Cinkler J, Kong X, Memon N. Lossless and near-lossless compression of EEG signals. In Farques MP, Hippenstiel RD, editors, Conference Record of the Asilomar Conference on Signals, Systems and Computers. Vol. 2. IEEE Comp Soc. 1998. p. 1432-1436
    Cinkler, Judit ; Kong, Xuan ; Memon, Nasir. / Lossless and near-lossless compression of EEG signals. Conference Record of the Asilomar Conference on Signals, Systems and Computers. editor / M.P. Farques ; R.D. Hippenstiel. Vol. 2 IEEE Comp Soc, 1998. pp. 1432-1436
    @inproceedings{de63b7353f434ddab5ee45c2a638a21b,
    title = "Lossless and near-lossless compression of EEG signals",
    abstract = "In this paper we study compression techniques for electroencelograph (EEG) signals. A variety of lossless compression techniques, ranging from simple dictionary based approaches to more sophisticated context modeling techniques based on recent work in lossless image coding are investigated and compared. It is seen that compression ratios obtained by lossless compression are limited. 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 near-lossless compression techniques that give quantitative bounds on the errors introduced during compression. It is observed that such techniques give significantly higher compression ratios. Simulation results with a large variety of data sets are reported.",
    author = "Judit Cinkler and Xuan Kong and Nasir Memon",
    year = "1998",
    language = "English (US)",
    volume = "2",
    pages = "1432--1436",
    editor = "M.P. Farques and R.D. Hippenstiel",
    booktitle = "Conference Record of the Asilomar Conference on Signals, Systems and Computers",
    publisher = "IEEE Comp Soc",

    }

    TY - GEN

    T1 - Lossless and near-lossless compression of EEG signals

    AU - Cinkler, Judit

    AU - Kong, Xuan

    AU - Memon, Nasir

    PY - 1998

    Y1 - 1998

    N2 - In this paper we study compression techniques for electroencelograph (EEG) signals. A variety of lossless compression techniques, ranging from simple dictionary based approaches to more sophisticated context modeling techniques based on recent work in lossless image coding are investigated and compared. It is seen that compression ratios obtained by lossless compression are limited. 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 near-lossless compression techniques that give quantitative bounds on the errors introduced during compression. It is observed that such techniques give significantly higher compression ratios. Simulation results with a large variety of data sets are reported.

    AB - In this paper we study compression techniques for electroencelograph (EEG) signals. A variety of lossless compression techniques, ranging from simple dictionary based approaches to more sophisticated context modeling techniques based on recent work in lossless image coding are investigated and compared. It is seen that compression ratios obtained by lossless compression are limited. 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 near-lossless compression techniques that give quantitative bounds on the errors introduced during compression. It is observed that such techniques give significantly higher compression ratios. Simulation results with a large variety of data sets are reported.

    UR - http://www.scopus.com/inward/record.url?scp=0031674431&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=0031674431&partnerID=8YFLogxK

    M3 - Conference contribution

    VL - 2

    SP - 1432

    EP - 1436

    BT - Conference Record of the Asilomar Conference on Signals, Systems and Computers

    A2 - Farques, M.P.

    A2 - Hippenstiel, R.D.

    PB - IEEE Comp Soc

    ER -