A computational biomarker of idiopathic generalized epilepsy from resting state EEG
© 2016 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Epilepsy is one of the commonest serious neurological conditions. It is characterized by the tendency to have recurrent seizures, which arise against a backdrop of apparently normal brain activity. At present, clinical diagnosis relies on: (i) case history, which can be unreliable; (ii) observing transient abnormal activity during electroencephalography (EEG), which may not be present during clinical evaluation; (iii) if diagnostic uncertainty occurs, undertaking prolonged monitoring in an attempt to observe EEG abnormalities, which is costly. Herein, we describe the discovery and validation of an epilepsy biomarker based on computational analysis of a short segment of resting-state (inter-ictal) EEG. Our method utilizes a computer model of dynamic networks, where the network is inferred from the extent of synchrony between EEG channels (functional networks) and the normalized power spectrum of the clinical data. We optimize model parameters using a leave-one-out classification on a dataset comprising 30 people with idiopathic generalized epilepsy (IGE) and 38 normal controls. Applying this scheme to all 68 subjects we find 100% specificity at 56.7% sensitivity, and 100% sensitivity at 65.8% specificity. We believe this biomarker could readily provide additional support to the diagnostic process
Helmut Schmidt, Mark P. Richardson and John R. Terry received financial support from Epilepsy Research UK (via Grant A1002). Marc Goodfellow, Mark P. Richardson and John R. Terry received financial support from the Medical Research Council (via Programme Grant MR/K013998/1) and the EPSRC (via Centre Grant EP/N014391/1). John R. Terry further acknowledges the generous support of the Wellcome Trust Institutional Strategic Support Award (WT105618MA). Mark P. Richardson is part-funded by the National Institute of Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust.
This is the author accepted manuscript. This is an open access article. The final version is available from Wiley via the DOI in this record.
Vol. 57 (10), pp. e200–e204