Synchronization Analysis In Epileptic EEG Signals Via State Transfer Networks Based On Visibility Graph Technique

2021 ◽  
Author(s):  
Ali Olamat ◽  
Pinar Ozel ◽  
Aydin Akan
Keyword(s):  
Epileptic Seizures ◽  
Brain Regions ◽  
Visibility Graph ◽  
Range Analysis ◽  
Synchronization Likelihood ◽  
State Transfer ◽  
Analysis Technique ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Good Agreement

Epilepsy is a persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analysis and statistical rescale range analysis. In this study, two different states of epilepsy EEG data (preictal and ictal phases), obtained from 17 subjects (18 channels each), were analyzed by a new method called state transfer network (STN). The analysis performed by STN yields a network metric called motifs, which are averaged over all channels and subjects in terms of their persistence level in the network. The results showed an increase of overall motif persistence during the ictal over the preictal phase, reflecting the synchronization increase during the seizure phase (ictal). An evaluation of intermotif cross-correlation indicated a definite manifestation of such synchronization. Moreover, these findings are compared with several other well-known methods such as synchronization likelihood (SL), visibility graph similarity (VGS), and global field synchronization (GFS). It is hinted that the STN method is in good agreement with approaches in the literature and more efficient. The most significant contribution of this research is introducing a novel nonlinear analysis technique of generalized synchronization. The STN method can be used for classifying epileptic seizures based on the synchronization changes between multichannel data.

SIMULATION OF HEALTHY AND EPILEPTIFORM BRAIN ACTIVITY USING CELLULAR AUTOMATA

2012 ◽  
Vol 22 (09) ◽  
pp. 1250229 ◽  
Author(s):  
VASSILIOS TSOUTSOURAS ◽  
GEORGIOS Ch. SIRAKOULIS ◽  
GEORGIOS P. PAVLOS ◽  
AGGELOS C. ILIOPOULOS
Keyword(s):  
Cellular Automata ◽  
Chaotic Behavior ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Global Features ◽  
Healthy State ◽  
Eeg Data ◽  
Spatially Distributed ◽  
Low Dimensional

In this study, we first present a modeling mechanism for the loss of neurons in limbic brain regions (epileptogenic focus) that could cause epileptic seizures by spreading the pathological dynamics from the focal to healthy brain regions. Prior work has shown that Cellular Automata (CAs) are very effective in simulating physical systems and solving scientific problems by capturing essential global features of the systems resulting from the collective effect of simple system components that interact locally. Nontrivial CAs are obtained whenever the dependence on the values at each CA site is nonlinear. Consequently, in this study, we show that brain activity in a healthy and epileptic state can be simulated by CA long-range interactions. Results from analysis of CA simulation data, as well as real electroencephalographic (EEG) data clearly show the efficiency of the proposed CA algorithm for simulation of the transition to an epileptic state. The results are in agreement with ones from previous studies about the existence of high-dimensional stochastic behavior during the healthy state and low-dimensional chaotic behavior during the epileptic state. The correspondence of the CA simulation results with the ones from real EEG data analysis implies that the spatiotemporal chaotic dynamics of the epileptic brain are similar to observed nonequilibrium phase transition processes in spatially distributed complex systems.

Clinical Predictive Factors of Pathological EEG in Children with Febrile Seizures and Their Association with Subsequent Epileptic Seizures

2021 ◽  
Author(s):  
Khouloud Kchaou ◽  
Ines Kammoun ◽  
Sahar Chakroun ◽  
Asma Haddar ◽  
Kaouthar Masmoudi
Keyword(s):  
Febrile Seizure ◽  
Epileptic Seizures ◽  
Febrile Seizures ◽  
Psychomotor Retardation ◽  
Only Children ◽  
Psychomotor Delay ◽  
Eeg Abnormalities ◽  
Eeg Data ◽  
Eeg Recordings

AbstractThe objective of this study was to identify clinical parameters predicting either a pathological EEG or a subsequent epileptic seizure (SES), based on the relation between paroxysmal EEG abnormalities and clinical features in children who presented at least one febrile seizure (FS). We collected data of children who presented to our department during the period 2013 to 2018 for EEG recording as part of their febrile seizure assessment. Only children aged between 1 month to 5 years were included. Both the clinical and EEG data were retrospectively collected and statistically studied. We performed a detailed analysis of the EEG recordings. SES was identified for patients with sufficient follow-up. A total of 120 children were included in the study, of whom 48% had EEG abnormalities. Psychomotor retardation (p = 0.002), completion of an EEG within 7 days of the last FS (p = 0.046), and late age (> 3 years) of the first FS onset (p = 0.021) were significantly associated with a pathological EEG. In multivariate analysis, performing early EEG (< 7 days from the last FS) (odds ratio [OR]: 2.35; p = 0.043; confidence interval [CI]: 1.028–5.375) and psychomotor retardation (OR: 4.19; p = 0.008; CI: 1.46–12) were independent predictors of a pathological EEG. Of 120 patients, 45 had a follow-up. However, only 10 (22.22%) had SES. Children with SES tended more to have a psychomotor delay, compared with children without SES (50% vs. 14.28%, p = 0.029). Moreover, the percentage of initial abnormal EEG in patients with SES was significantly higher than those without SES (70% vs. 34.28%, p = 0.05). Even though some FS characteristics predict EEG abnormalities, they are not always associated with SES. We highlight the importance of performing an EEG in the group of children who had both FS and psychomotor retardation. This is most likely the group at the highest risk of developing epilepsy.

scholarly journals Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients

2015 ◽  
Vol 373 (2034) ◽  
pp. 20140094 ◽  
Author(s):  
Klaus Lehnertz ◽  
Henning Dickten
Keyword(s):  
Information Content ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Symbolic Analysis ◽  
Crucial Importance ◽  
Time Resolved ◽  
Complex Information ◽  
Eeg Recordings ◽  
Temporal Interdependencies

Inferring strength and direction of interactions from electroencephalographic (EEG) recordings is of crucial importance to improve our understanding of dynamical interdependencies underlying various physiological and pathophysiological conditions in the human epileptic brain. We here use approaches from symbolic analysis to investigate—in a time-resolved manner—weighted and directed, short- to long-ranged interactions between various brain regions constituting the epileptic network. Our observations point to complex spatial–temporal interdependencies underlying the epileptic process and their role in the generation of epileptic seizures, despite the massive reduction of the complex information content of multi-day, multi-channel EEG recordings through symbolization. We discuss limitations and potential future improvements of this approach.

Electroencephalographic Recordings of Focal Seizures in Patients Affected by Periventricular Nodular Heterotopia: Role of the Heterotopic Nodules in the Genesis of Epileptic Discharges

2004 ◽  
Vol 19 (3) ◽  
pp. 369-377
Author(s):  
Giorgio Battaglia ◽  
Silvana Franceschetti ◽  
Luisa Chiapparini ◽  
Elena Freri ◽  
Stefania Bassanini ◽  
...  
Keyword(s):  
Brain Regions ◽  
Periventricular Nodular Heterotopia ◽  
Focal Seizures ◽  
Ictal Eeg ◽  
Epileptic Discharges ◽  
Nodular Heterotopia ◽  
Eeg Recordings ◽  
Drug Resistant Epilepsy ◽  
Video Eeg

Patients affected by periventricular nodular heterotopia are frequently characterized by focal drug-resistant epilepsy. To investigate the role of periventricular nodules in the genesis of seizures, we analyzed the electroencephalographic (EEG) features of focal seizures recorded by means of video-EEG in 10 patients affected by different types of periventricular nodular heterotopia and followed for prolonged periods of time at the epilepsy center of our institute. The ictal EEG recordings with surface electrodes revealed common features in all patients: all seizures originated from the brain regions where the periventricular nodular heterotopia were located; EEG patterns recorded on the leads exploring the periventricular nodular heterotopia were very similar both at the onset and immediately after the seizure's end in all patients. Our data suggest that seizures are generated by abnormal anatomic circuitries, including the heterotopic nodules and adjacent cortical areas. The major role of heterotopic neurons in the genesis and propagation of epileptic discharges must be taken into account when planning surgery for epilepsy in patients with periventricular nodular heterotopia. ( J Child Neurol 2005;20:369—377).

scholarly journals Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain: a combined fMRI–EEG study

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130473 ◽  
Author(s):  
Tobias Larsen ◽  
John P. O'Doherty
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Temporal Dynamics ◽  
Brain Regions ◽  
Choice Task ◽  
Binary Choice ◽  
Intraparietal Sulcus ◽  
Trial Onset ◽  
Eeg Data ◽  
Spatio Temporal

While there is a growing body of functional magnetic resonance imaging (fMRI) evidence implicating a corpus of brain regions in value-based decision-making in humans, the limited temporal resolution of fMRI cannot address the relative temporal precedence of different brain regions in decision-making. To address this question, we adopted a computational model-based approach to electroencephalography (EEG) data acquired during a simple binary choice task. fMRI data were also acquired from the same participants for source localization. Post-decision value signals emerged 200 ms post-stimulus in a predominantly posterior source in the vicinity of the intraparietal sulcus and posterior temporal lobe cortex, alongside a weaker anterior locus. The signal then shifted to a predominantly anterior locus 850 ms following the trial onset, localized to the ventromedial prefrontal cortex and lateral prefrontal cortex. Comparison signals between unchosen and chosen options emerged late in the trial at 1050 ms in dorsomedial prefrontal cortex, suggesting that such comparison signals may not be directly associated with the decision itself but rather may play a role in post-decision action selection. Taken together, these results provide us new insights into the temporal dynamics of decision-making in the brain, suggesting that for a simple binary choice task, decisions may be encoded predominantly in posterior areas such as intraparietal sulcus, before shifting anteriorly.

scholarly journals Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

2021 ◽  
Author(s):  
David Pascucci ◽  
Maria Rubega ◽  
Joan Rue-Queralt ◽  
Sebastien Tourbier ◽  
Patric Hagmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Meta Analysis ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Noise Perturbation ◽  
Eeg Data ◽  
Functional Brain Networks ◽  
Anatomical Priors ◽  
And Diffusion

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

scholarly journals Pengaruh Atribut Produk Wisata dan Citra Destinasi Terhadap Keputusan Berkunjung Pada Destinasi Wisata Taman Sri Baduga Kabupaten Purwakarta

2021 ◽  
Vol 4 (2) ◽  
pp. 656-664
Author(s):  
Aulia Rafika ◽  
Edi Suswardji Nugroho
Keyword(s):  
Sampling Technique ◽  
Statistical Technique ◽  
Destination Image ◽  
Research Subjects ◽  
Product Attributes ◽  
Range Analysis ◽  
Tourism Product ◽  
Analysis Technique ◽  
Scale Range ◽  
Quantitative Descriptive

This study aims to determine the effect of tourism product attributes and destination image, either directly, indirectly, partially and simultaneously on the decision to visit the Taman Sri Baduga Kabupaten Purwakarta tourist destination, and the results of this study can be useful for Stake Holders in the field of tourism and can contribute for science.The paradigm of this research is a quantitative descriptive verification paradigm with a focus on testing theory through measuring research variables using the statistical technique / SPSS 24 approach, data collection using the incidental sampling technique on 398 respondents with research subjects who are visitors or who have visited Taman Sri Baduga Purwakarta tourist destinations. Before testing the hypothesis, this study conducted a data validity test. Data analysis technique used is the scale range analysis technique and path analysis.There is a significant effect of the partial attributes of tourism products on the decision to visit by 55.3% and the partial effect of the image of the destination on the decision to visit by 28%. Then there is a simultaneous influence of tourism product attributes and destination image on the decision to visit Taman Sri Baduga Purwakarta of 83.3%, the remaining 16.7% is influenced by other factors not included in this study. Keywords: Tourism Product Attributes, Destination Image, and Decision to Visit

scholarly journals Signal-space projection suppresses the tACS artifact in EEG recordings

2019 ◽  
Author(s):  
Johannes Vosskuhl ◽  
Tuomas P. Mutanen ◽  
Toralf Neuling ◽  
Risto J. Ilmoniemi ◽  
Christoph S. Herrmann
Keyword(s):  
Brain Activity ◽  
Current Source ◽  
Oscillatory Activity ◽  
Signal Space ◽  
Artifact Correction ◽  
Eeg Data ◽  
Before And After ◽  
Transcranial Alternating Current Stimulation ◽  
Space Projection ◽  
Eeg Recordings

1.AbstractBackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the huge tACS-caused artifact in electroencephalography (EEG) signals, tACS–EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on a human subject to validate the best-performing data-correction approach.ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.Comparison with existing methodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction.ConclusionsOur results demonstrate the feasibility of SSP by applying it to human tACS–EEG data.

scholarly journals Hyperspherical Manifold for EEG Signals of Epileptic Seizures

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Tahir Ahmad ◽  
Vinod Ramachandran
Keyword(s):  
Measure Theory ◽  
Epileptic Seizures ◽  
Physical Symptoms ◽  
Measure Data ◽  
Dimensional Manifold ◽  
Eeg Signals ◽  
Discrete Probability ◽  
Eeg Data ◽  
Manifold Theory ◽  
The Brain

The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.

Attention-based Graph ResNet with focal loss for epileptic seizure detection

2021 ◽  
pp. 1-13
Author(s):  
Changxu Dong ◽  
Yanna Zhao ◽  
Gaobo Zhang ◽  
Mingrui Xue ◽  
Dengyu Chu ◽  
...  
Keyword(s):  
Class Imbalance ◽  
Seizure Detection ◽  
Brain Regions ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Topological Relationships ◽  
Average Accuracy ◽  
Proposed Model ◽  
Eeg Data ◽  
The Central Nervous System

Epilepsy is a chronic brain disease resulted from the central nervous system lesion, which leads to repeated seizure occurs for the patients. Automatic seizure detection with Electroencephalogram (EEG) has witnessed great progress. However, existing methods paid little attention to the topological relationships of different EEG electrodes. Latest neuroscience researches have demonstrated the connectivity between different brain regions. Besides, class-imbalance is a common problem in EEG based seizure detection. The duration of epileptic EEG signals is much shorter than that of normal signals. In order to deal with the above mentioned two challenges, we propose to model the multi-channel EEG data using the Attention-based Graph ResNet (AGRN). In particular, each channel of the EEG signal represents a node of the graph and the inter-channel relations are modeled via the adjacency matrix in the graph. The loss function of the ARGN model is re-designed using focal loss to cope with the class-imbalance problem. The proposed ARGN with focal model could learn discriminative features from the raw EEG data. Experiments are carried out on the CHB-MIT dataset. The proposed model achieves an average accuracy of 98.70%, a sensitivity of 97.94%, a specificity of 98.66% and a precision of 98.62%. The Area Under the ROC Curve (AUC) is 98.69%.

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