Objective: Epilepsy is characterized by unpredictable and sudden paroxysmal neuronal firing occurrences and sometimes evolving in clinically evident seizure. To predict seizure event, small-world characteristic in nine minutes before seizure, divided in three 3-min periods (T0, T1, T2) were investigated.
Methods: Intracerebral recordings were obtained from 10 patients with drug resistant focal epilepsy examined by means of stereotactically implanted electrodes; analysis was focused in a period of low spiking (Baseline) and during two seizures. Networks' architecture is undirected and weighted. Electrodes' contacts close to epileptic focus are the vertices, edges are weighted by mscohere (=magnitude squared coherence).
Results: Differences were observed between Baseline and T1 and between Baseline and T2 in theta band; and between Baseline and T1, Baseline and T2, and near-significant difference between T0 and T2 in Alpha 2 band. Moreover, an intra-band index was computed for small worldness as difference between Theta and Alpha 2. It was found a growing index trend from Baseline to T2.
Conclusions: Cortical network features a specific pre-seizure architecture which could predict the incoming epileptic seizure.
Significance: Through this study future researches could investigate brain connectivity modifications approximating a clinical seizure also in order to address a preventive therapy.
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http://dx.doi.org/10.1016/j.clinph.2016.07.006 | DOI Listing |
J Healthc Inform Res
June 2024
KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 550, University Avenue, Toronto, M5G 2A2 Ontario Canada.
Epilepsy affects more than million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face.
View Article and Find Full Text PDFBiomed Eng Online
February 2020
Department of Electrical and Electronics Engineering, Faculty of Engineering, Izmir University of Economics, Balcova, Izmir, Turkey.
Background: Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose non-stationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs).
View Article and Find Full Text PDFClin Neurophysiol
October 2016
Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Rome, Italy; Institute of Neurology, Dept. Geriatrics, Neuroscience & Orthopedics, Catholic University, Policlinico A. Gemelli, Rome, Italy.
Objective: Epilepsy is characterized by unpredictable and sudden paroxysmal neuronal firing occurrences and sometimes evolving in clinically evident seizure. To predict seizure event, small-world characteristic in nine minutes before seizure, divided in three 3-min periods (T0, T1, T2) were investigated.
Methods: Intracerebral recordings were obtained from 10 patients with drug resistant focal epilepsy examined by means of stereotactically implanted electrodes; analysis was focused in a period of low spiking (Baseline) and during two seizures.
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