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

• Published in: Journal of neuroscience methods 2011-01, Vol.194 (2), p.358-373
• Main Authors: Gazit, T., Doron, I., Sagher, O., Kohrman, M.H., Towle, V.L., Teicher, M., Ben-Jacob, E.
• Format: Article
• Language: English
• Subjects: Adolescent; Algorithms; Brain - physiopathology; Brain Mapping; Child; Coherence; Electrocorticography; Electroencephalography - methods; Entropy; Epilepsy; Epilepsy - pathology; Epilepsy - physiopathology; Female; Humans; Male; Models, Neurological; Nonlinear Dynamics; Signal Processing, Computer-Assisted; Time Factors; Wavelet-Packets; Wavelets; Young Adult
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2010.10.011

▶ F-E similarity can track similarities in time-frequency features neglected by other measures. ▶ F-E similarity differentiates the seizure onset zone from other areas using interictal activity. F-E similarity can follow the shift between interictal and ictal activity. 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.