Time-frequency characterization of electrocorticographic recordings of epileptic patients using frequency-entropy similarity: A comparison to other bi-variate measures

T. Gazit, I. Doron, O. Sagher, M. H. Kohrman, V. L. Towle, M. Teicher, E. Ben-Jacob

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Expert evaluation of electrocorticographic (ECoG) recordings forms the linchpin of seizure onset zone localization in the evaluation of epileptic patients for surgical resection. Numerous methods have been developed to analyze these complex recordings, including uni-variate (characterizing single channels), bi-variate (comparing channel pairs) and multivariate measures. Developing reliable algorithms may be helpful in clinical tasks such as localization of epileptogenic zones and seizure anticipation, as well as enabling better understanding of neuronal function and dynamics. Recently we have developed the frequency-entropy (F-E) similarity measure, and have tested its capability in mapping the epileptogenic zones. The F-E similarity measure compares time-frequency characterizations of two recordings. In this study, we examine the method's principles and utility and compare it to previously described bi-variate correspondence measures such as correlation, coherence, mean phase coherence and spectral comparison methods. Specially designed synthetic signals were used for illuminating theoretical differences between the measures. Intracranial recordings of four epileptic patients were then used for the measures' comparative analysis by creating a mean inter-electrode matrix for each of the correspondence measures and comparing the structure of these matrices during the inter-ictal and ictal periods. We found that the F-E similarity measure is able to discover spectral and temporal features in data which are hidden for the other measures and are important for foci localization.

Original languageEnglish
Pages (from-to)358-373
Number of pages16
JournalJournal of Neuroscience Methods
Volume194
Issue number2
DOIs
StatePublished - 15 Jan 2011

Bibliographical note

Funding Information:
This research has been supported in part by the Tauber Family Foundation, the Maguy-Glass chair in Physics of Complex Systems at Tel Aviv University , NIH 5 R01 NS40514 , The Brain Research Foundation and the Susman and Asher Foundation . The authors would like to thank Tal Sela for discussions on the statistical analysis.

Funding

This research has been supported in part by the Tauber Family Foundation, the Maguy-Glass chair in Physics of Complex Systems at Tel Aviv University , NIH 5 R01 NS40514 , The Brain Research Foundation and the Susman and Asher Foundation . The authors would like to thank Tal Sela for discussions on the statistical analysis.

FundersFunder number
Susman and Asher Foundation
Tauber Family Foundation
National Institutes of Health
National Institute of Neurological Disorders and StrokeR01NS040514
Brain Research Foundation
Tel Aviv University

    Keywords

    • Coherence
    • Electrocorticography
    • Entropy
    • Epilepsy
    • Wavelet-Packets
    • Wavelets

    Fingerprint

    Dive into the research topics of 'Time-frequency characterization of electrocorticographic recordings of epileptic patients using frequency-entropy similarity: A comparison to other bi-variate measures'. Together they form a unique fingerprint.

    Cite this