Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 60, Issue 5, Pages 1318-1327Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2012.2234125
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Funding
- National Basic Research Program of China [2011CB707904]
- National Natural Science Foundation [61073138, 61271312, 60911130370]
- Ministry of Education of China [20110092110023]
- Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education
- Natural Science Foundation of Jiangsu Province [SBK200910055]
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The background objective of this study is to analyze electrenocephalographic (EEG) signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure evolution, including a fast onset activity. We aim to ascertain how cerebral structures get involved during this phase, in particular whether some structures drive other ones. Regarding a recent theoretical information measure, namely the transfer entropy (TE), we propose two criteria, the first one is based on Akaike's information criterion, the second on the Bayesian information criterion, to derive models' orders that constitute crucial parameters in the TE estimation. A normalized index, named partial transfer entropy (PTE), allows for quantifying the contribution or the influence of a signal to the global information flow between a pair of signals. Experiments are first conducted on linear autoregressive models, then on a physiology-based model, and finally on real intracerebral EEG epileptic signals to detect and identify directions of causal interdependence. Results support the relevance of the new measures for characterizing the information flow propagation whatever unidirectional or bidirectional interactions.
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