Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity. (May 2021)

• Record Type: Journal Article
• Authors: Cao, Jun; Grajcar, Kacper; Shan, Xiaocai; Zhao, Yifan; Zou, Jiaru; Chen, Liangyu; Li, Zhiqing; Grunewald, Richard; Zis, Panagiotis; De Marco, Matteo; Unwin, Zoe; Blackburn, Daniel; Sarrigiannis, Ptolemaios G.
• Is Part Of: Biomedical signal processing and control. Volume 67(2021)
• Publication Date: 2021-05
• Subjects: qEEG -- Classification -- Brain connectivity -- Correlation -- Coherence; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102554
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Recognise epileptic patients using interictal seizure-free EEG data. Achieved 97 % classification accuracy for EG vs HC using functional connectivity. Achieved 73 % classification accuracy for EG vs NEAD using functional connectivity. The proposed multivariant method outperforms the univariant method. Abstract: Most 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.