Browsing by Author "Zis, Panagiotis"
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Item Open Access The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings(Elsevier, 2018-01-08) Sarrigiannis, Ptolemaios G.; Zhao, Yifan; He, Fei; Billings, Stephen A.; Baster, Kathleen; Rittey, Chris; Yianni, John; Zis, Panagiotis; Wei, Hua-Liang; Hadjivassiliou, Marios; Grünewald, RichardObjective To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). Methods We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags. Results A frontal/fronto-central onset of the absences is detected in 13 of the 17 cases with the highest ictal strength of association between homologous frontal followed by centro-temporal and fronto-central areas. Delays consistently in excess of 4 ms occur at the very onset between these regions, swiftly followed by the emergence of “isochronous” (0-2ms) synchronisation but dynamic time lag changes occur during SW discharges. Conclusions In absences an initial cortico-cortical spread leads to dynamic lag changes to include periods of isochronous interhemispheric synchronisation, which we hypothesize is mediated by the thalamus. Significance Absences from CAE show ictal epileptic network dynamics remarkably similar to those observed in WAG/Rij rats which guided the formulation of the cortical focus theory.Item Open Access A dementia classification framework using frequency and time-frequency features based on EEG signals(IEEE, 2019-04-04) Durongbhan, Pholpat; Zhao, Yifan; Chen, Liangyu; Zis, Panagiotis; De Marco, Matteo; Unwin, Zoe C.; Venneri, Annalena; He, Xiongxiong; Li, Sheng; Zhao, Yitian; Blackburn, Daniel J.; Sarrigiannis, Ptolemaios G.Alzheimer’s disease (AD) accounts for 60%–70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This paper aims to explore a routine to gain such biomarkers using the quantitative analysis of electroencephalography (QEEG). This paper proposes a supervised classification framework that uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-nearest neighbor (KNN) classification, quantitative evaluation, and topographic visualization. Considering the human brain either as a stationary or a dynamical system, both the frequency-based and time–frequency-based features were tested in 40 participants. The results show that: 1) the proposed method can achieve up to a 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared with alternative machine learning approaches; 2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; and 3) in the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and the localization of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.Item Open Access 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 GeorgiosIntroduction: 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.Item Open Access Imaging of nonlinear and dynamic functional brain connectivity based on EEG recordings with the application on the diagnosis of Alzheimer's disease(IEEE, 2019-11-14) Zhao, Yifan; Zhao, Yitian; Durongbhan, Pholpat; Chen, Liangyu; Liu, Jiang; Billings, S. A.; Zis, Panagiotis; Unwin, Zoe C.; De Marco, Matteo; Venneri, Annalena; Blackburn, Daniel J.; Sarrigiannis, Ptolemaios G.Since age is the most significant risk factor for the development of Alzheimer’s disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This paper proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification.Item Open Access Tremor after long term lithium treatment; is it cortical myoclonus?(BMC (part of Springer Nature), 2019-05-22) Sarrigiannis, Ptolemaios Georgios; Zis, Panagiotis; Unwin, Zoe Charlotte; Blackburn, Daniel J.; Hoggard, Nigel; Zhao, Yifan; Billings, Stephen A.; Khan, Aijaz A.; Yianni, John; Hadjivassiliou, MariosIntroduction: Tremor is a common side effect of treatment with lithium. Its characteristics can vary and when less rhythmical, distinction from myoclonus can be difficult. Methods: We identified 8 patients on long-term treatment with lithium that developed upper limb tremor. All patients were assessed clinically and electrophysiologically, with jerk-locked averaging (JLA) and cross-correlation (CC) analysis, and five of them underwent brain MRI examination including spectroscopy (MRS) of the cerebellum. Results: Seven patients (6 female) had action and postural myoclonus and one a regular postural and kinetic tremor that persisted at rest. Mean age at presentation was 58 years (range 42–77) after lengthy exposure to lithium (range 7–40 years). During routine monitoring all patients had lithium levels within the recommended therapeutic range (0.4-1 mmol/l). There was clinical and/or radiological evidence (on cerebellar MRS) of cerebellar dysfunction in 6 patients. JLA and/or CC suggested a cortical generator of the myoclonus in seven patients. All seven were on antidepressants and three additionally on neuroleptics, four of them had gluten sensitivity and two reported alcohol abuse. Conclusions: A synergistic effect of different factors appears to be contributing to the development of cortical myoclonus after chronic exposure to lithium. We hypothesise that the cerebellum is involved in the generation of cortical myoclonus in these cases and factors aetiologically linked to cerebellar pathology like gluten sensitivity and alcohol abuse may play a role in the development of myoclonus. Despite the very limited evidence in the literature, lithium induced cortical myoclonus may not be so rare.Item Open Access 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.