University of Birmingham A computational biomarker of idiopathic generalized epilepsy from resting state EEG Schmidt, H.; Woldman, W.; Goodfellow, M.; Chowdhury, F.A.; Koutroumanidis, M.; Jewell, S.; Richardson, M.P.; Terry, J.R. DOI: 10.1111/epi.13481 License: Creative Commons: Attribution (CC BY) Document Version Publisher's PDF, also known as Version of record Citation for published version (Harvard): Schmidt, H, Woldman, W, Goodfellow, M, Chowdhury, FA, Koutroumanidis, M, Jewell, S, Richardson, MP & Terry, JR 2016, 'A computational biomarker of idiopathic generalized epilepsy from resting state EEG', Epilepsia, vol. 57, pp. e200-e204. https://doi.org/10.1111/epi.13481 Link to publication on Research at Birmingham portal Publisher Rights Statement: Schmidt, H. , Woldman, W. , Goodfellow, M. , Chowdhury, F. A., Koutroumanidis, M. , Jewell, S. , Richardson, M. P. and Terry, J. R. (2016), A computational biomarker of idiopathic generalized epilepsy from resting state EEG. Epilepsia, 57: e200-e204. doi:10.1111/epi.13481 General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. •Users may freely distribute the URL that is used to identify this publication. •Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. •User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) •Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive. If you believe that this is the case for this document, please contact [email protected] providing details and we will remove access to the work immediately and investigate. Download date: 25. Sep. 2021 BRIEF COMMUNICATION A computational biomarker of idiopathic generalized epilepsy from resting state EEG *†‡1Helmut Schmidt, *†‡1Wessel Woldman, *†‡Marc Goodfellow, §Fahmida A. Chowdhury, §¶Michalis Koutroumanidis, §Sharon Jewell, ‡§2Mark P. Richardson, and *†‡2John R. Terry Epilepsia, 57(10):e200–e204, 2016 doi: 10.1111/epi.13481 SUMMARY Epilepsy is one of the most common serious neurologic conditions. It is characterized by the tendency to have recurrent seizures, which arise against a backdrop of appar- ently normal brain activity. At present, clinical diagnosis relies on the following: (1) case history, which can be unreliable; (2) observation of transient abnormal activity during electroencephalography (EEG), which may not be present during clinical evalu- ation; and (3) if diagnostic uncertainty occurs, undertaking prolonged monitoring in an attempt to observe EEG abnormalities, which is costly. Herein, we describe the discov- ery and validation of an epilepsy biomarker based on computational analysis of a short segment of resting-state (interictal) EEG. Our method utilizes a computer model of Helmut Schmidt is an dynamic networks, where the network is inferred from the extent of synchrony applied mathematician between EEG channels (functional networks) and the normalized power spectrum of working in the field of the clinical data. We optimize model parameters using a leave-one-out classification computational on a dataset comprising 30 people with idiopathic generalized epilepsy (IGE) and 38 neuroscience. 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. KEY WORDS: Biomarker, Diagnosis, Resting-state EEG, Computational model, IGE. Wessel Woldman is an MRC Research Fellow working on mathematical models of seizure generation. Accepted July 5, 2016; Early View publication August 8, 2016. *College of Engineering, Mathematics & Physical Sciences, University of Exeter, Exeter, United Kingdom; †Wellcome Trust ISSF Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom; ‡EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom; §Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom; and ¶Department of EEG and Epilepsy, St. Thomas’s Hospital, Guy’s and St. Thomas’s NHS Foundation Trust, London, United Kingdom Address correspondence to John R. Terry, College of Engineering, Mathematics & Physical Sciences, University of Exeter, Exeter EX4 4QJ, U.K. E-mail: [email protected] 1Denotes equal contribution as first author. 2Denotes equal contribution as last author. © 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, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. e200 e201 A Computational Biomarker of IGE At present, a confirmed diagnosis of epilepsy is made state” EEG activity from the initial stage of the recordings through a case history and a positive electroencephalogra- from each participant. These data were band-pass filtered phy (EEG), confirming the presence of epileptiform dis- using a Butterworth filter between 0.5 and 70 Hz, and band- charges. However, a positive EEG occurs at best in only stop filtered between 48 and 52 Hz to remove power-line 60% of cases, resulting in diagnostic uncertainty for many artifacts. Because signal amplitude may vary between indi- people,1 with significant associated costs.2 These costs pre- viduals due to different anatomic features (such as the size dominantly result from additional longer-term EEG moni- and shape of the cranium) the data were normalized by toring, repeated hospital admissions, as well as unnecessary dividing the power spectrum in each channel by the total prescription of antiepileptic drugs (AEDs). power in the spectrum averaged across all channels. This Idiopathic generalized epilepsy (IGE) is one of the main normalized power preserves relative differences in power classes of epilepsy. In recent years, studies comparing between channels. We then band-pass filtered the EEG seg- cohorts of people with IGE and cohorts of healthy controls ments into either the alpha (8–13 Hz) or low alpha bands7 have shown statistically significant alterations at the group (6–9 Hz). For segments band-pass filtered in the low alpha level when examining resting-state features of the EEG band, we further inferred functional networks using the using power spectrum,3 functional networks,4 and a model- Phase-Locking Factor8 (PLF) and phase-lags (as described driven analysis of functional networks.5 However, substan- previously).5 tial overlap of these markers between groups may render the For the purpose of biomarker discovery, we consider measurement unsuitable as a diagnostic test or biomarker6 measures that have demonstrated group-level differences for any one individual. Our aim therefore is to assess the between people with IGE and healthy controls using rest- performance of each of these methods as a classifier that has ing-state EEG. First, the peak in alpha power across occipi- three outcomes for each individual: unequivocally IGE, tal EEG channels, which is known to shift toward lower unequivocally normal, or uncertain. Such a classifier could frequencies in people with IGE.3 Second, the mean degree be used as a screening test in a nonspecialist primary care of the PLF-inferred low alpha functional network, which is setting, as well as a diagnostic validation test in a specialist elevated in people with IGE.4 Third, a model-driven analy- epilepsy setting. This would focus further medical investi- sis where the low alpha functional network inferred from gation and resources on a smaller subgroup, producing effi- the EEG of each individual is integrated within a phase ciency gains and cost savings. oscillator model (of Kuramoto type).5 Here the local cou- pling constant within each node of the network is inferred Methods by multiplying the variance of the signal in the correspond- ing EEG channel by a uniform parameter K, to give a sub- We studied data from 38 healthy controls and 30 people ject-specific dynamic network model of the brain. The with IGE between the ages of 16 and 59 years. The individu- seizure-generating capability of each region within this als with IGE were drug naive and recruited through clinics model is then evaluated computationally, as the average at St Thomas’s Hospital. A diagnosis of epilepsy was con- level of emergent seizure activity across the whole network firmed in each case by an experienced epilepsy specialist driven by the region of interest (see Fig. 1A). through observation of typical generalized spike-wave The performance of all three candidate biomarkers was (GSW) activity on EEG either spontaneously or following evaluated