Browsing by Author "Unwin, Zoe"

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    EEG recordings as biomarkers of pain perception: where do we stand and where to go?
    (Springer, 2022-03-23) Zis, Panagiotis; Liampas, Andreas; Artemiadis, Artemios; Tsalamandris, Gabriela; Neophytou, Panagiota; Unwin, Zoe; Kimiskidis, Vasilios K.; Hadjigeorgiou, Georgios M.; Varrassi, Giustino; Zhao, Yifan; Sarrigiannis, Ptolemaios Georgios
    Introduction: The universality and complexity of pain, which is highly prevalent, yield its significance to both patients and researchers. Developing a non-invasive tool that can objectively measure pain is of the utmost importance for clinical and research purposes. Traditionally electroencephalography (EEG) has been mostly used in epilepsy; however, over the recent years EEG has become an important non-invasive clinical tool that has helped increase our understanding of brain network complexities and for the identification of areas of dysfunction. This review aimed to investigate the role of EEG recordings as potential biomarkers of pain perception. Methods: A systematic search of the PubMed database led to the identification of 938 papers, of which 919 were excluded as a result of not meeting the eligibility criteria, and one article was identified through screening of the reference lists of the 19 eligible studies. Ultimately, 20 papers were included in this systematic review. Results: Changes of the cortical activation have potential, though the described changes are not always consistent. The most consistent finding is the increase in the delta and gamma power activity. Only a limited number of studies have looked into brain networks encoding pain perception. Conclusion: Although no robust EEG biomarkers of pain perception have been identified yet, EEG has potential and future research should be attempted. Designing strong research protocols, controlling for potential risk of biases, as well as investigating brain networks rather than isolated cortical changes will be crucial in this attempt.
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    Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity
    (Elsevier, 2021-03-12) 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.
    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.