Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity

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dc.contributor.authorCao, Jun
dc.contributor.authorGrajcar, Kacper
dc.contributor.authorShan, Xiaocai
dc.contributor.authorZhao, Yifan
dc.contributor.authorZou, Jiaru
dc.contributor.authorChen, Liangyu
dc.contributor.authorLi, Zhiqing
dc.contributor.authorGrunewald, Richard
dc.contributor.authorZis, Panagiotis
dc.contributor.authorDe Marco, Matteo
dc.contributor.authorUnwin, Zoe
dc.contributor.authorBlackburn, Daniel
dc.contributor.authorSarrigiannis, Ptolemaios G.
dc.date.accessioned2023-05-30T13:04:39Z
dc.date.available2023-05-30T13:04:39Z
dc.date.issued2021-03-12
dc.description.abstractMost seizures in adults with epilepsy occur rather infrequently and as a result, the interictal EEG plays a crucial role in the diagnosis and classification of epilepsy. However, empirical interpretation, of a first EEG in adult patients, has a very low sensitivity ranging between 29-55%. Useful EEG information remains buried within the signals in seizure-free EEG epochs, far beyond the observational capabilities of any specialised physician in this field. Unlike most of the existing works focusing on either seizure data or single-variate method, we introduce a multi-variate method to characterise sensor level brain functional connectivity from interictal EEG data to identify patients with generalised epilepsy. A total of 9 connectivity features based on 5 different measures in time, frequency and time frequency domains have been tested. The solution has been validated by the K-Nearest Neighbour algorithm, classifying an epilepsy group (EG) vs healthy controls (HC) and subsequently with another cohort of patients characterised by non-epileptic attacks (NEAD), a psychogenic type of disorder. A high classification accuracy (97%) was achieved for EG vs HC while revealing significant spatio temporal deficits in the frontocentral areas in the beta frequency band. For EG vs NEAD, the classification accuracy was only about 73%, which might be a reflection of the well-described coexistence of NEAD with epileptic attacks. Our work demonstrates that seizure-free interictal EEG data can be used to accurately classify patients with generalised epilepsy from HC and that more systematic work is required in this direction aiming to produce a clinically useful diagnostic method.en_UK
dc.identifier.citationCao J, Grajcar K, Shan X, et al., (2021) Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity, Biomedical Signal Processing and Control, Volume 67, May 2021, Article number 102554en_UK
dc.identifier.eissn1746-8108
dc.identifier.issn1746-8094
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2021.102554
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19743
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectqEEGen_UK
dc.subjectclassificationen_UK
dc.subjectbrain connectivityen_UK
dc.subjectcorrelationen_UK
dc.subjectcoherenceen_UK
dc.titleUsing interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivityen_UK
dc.typeArticleen_UK

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