A novel method for the identification of synchronization effects in multichannel ECoG with an application to epilepsy

In this paper, we present a novel method for the identification of synchronization effects in multichannel electrocorticograms (ECoG). Based on autoregressive modeling, we define a dependency measure termed extrinsic-to-intrinsic power ratio (EIPR) which quantifies directed coupling effects in the t...

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Veröffentlicht in:Biological cybernetics 2013-06, Vol.107 (3), p.321-335
Hauptverfasser: Graef, A., Hartmann, M., Flamm, C., Baumgartner, C., Deistler, M., Kluge, T.
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container_end_page 335
container_issue 3
container_start_page 321
container_title Biological cybernetics
container_volume 107
creator Graef, A.
Hartmann, M.
Flamm, C.
Baumgartner, C.
Deistler, M.
Kluge, T.
description In this paper, we present a novel method for the identification of synchronization effects in multichannel electrocorticograms (ECoG). Based on autoregressive modeling, we define a dependency measure termed extrinsic-to-intrinsic power ratio (EIPR) which quantifies directed coupling effects in the time domain. Hereby, a dynamic input channel selection algorithm assures the estimation of the model parameters despite the strong spatial correlation among the high number of involved ECoG channels. We compare EIPR to the partial directed coherence, show its ability to indicate Granger causality and successfully validate a signal model. Applying EIPR to ictal ECoG data of patients suffering from temporal lobe epilepsy allows us to identify the electrodes of the seizure onset zone. The results obtained by the proposed method are in good accordance with the clinical findings.
doi_str_mv 10.1007/s00422-013-0552-8
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Based on autoregressive modeling, we define a dependency measure termed extrinsic-to-intrinsic power ratio (EIPR) which quantifies directed coupling effects in the time domain. Hereby, a dynamic input channel selection algorithm assures the estimation of the model parameters despite the strong spatial correlation among the high number of involved ECoG channels. We compare EIPR to the partial directed coherence, show its ability to indicate Granger causality and successfully validate a signal model. Applying EIPR to ictal ECoG data of patients suffering from temporal lobe epilepsy allows us to identify the electrodes of the seizure onset zone. 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subjects Algorithms
Bioinformatics
Biological and medical sciences
Biomedical and Life Sciences
Biomedicine
Brain
Brain Mapping
Channels
Complex Systems
Computer Appl. in Life Sciences
Convulsions & seizures
Electrodes
Electroencephalography
Electroencephalography Phase Synchronization - physiology
Epilepsy
Epilepsy - diagnosis
Epilepsy - physiopathology
Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy
Humans
Medical imaging
Medical sciences
Models, Biological
Multichannel
Nervous system (semeiology, syndromes)
Neurobiology
Neurology
Neurosciences
Nonlinear Dynamics
Original Paper
Patients
Regression Analysis
Synchronism
Synchronization
title A novel method for the identification of synchronization effects in multichannel ECoG with an application to epilepsy
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