Synchronization Analysis of Epilepsy Data Using Global Field Synchronization

Multivariate time series analysis is of primary importance for the estimation of starting time of epilepsy seizures. Nonlinear synchronization analysis tool called Global Field Synchronization (GFS) was used to estimate the change in synchronization between the two time series. In this study, GFS is applied to the detection of epileptic seizures. Two sets of EEG data were used; first set was obtained from an unhealthy part of the brain prior to the seizure to take place (free seizure interval) and the second set obtained from the opposite healthy hemisphere of the brain. Results show a significant difference on GFS value between selected data sets, where for the unhealthy part shows a lower value of GFS than the healthy part

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

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

State Transfer Network of Time Series Based on Visibility Graph Analysis for Classifying and Prediction of Epilepsy Seizures

Visibility graph analysis of time series became widely used as a time series analysis in the recent years. State transfer network is a network of mapping mono/multivariate time series into a network of local states based on visibility graph, it was used to study the evolutionary behavior of time series and in this study, we applied this principle to the detection of epileptic seizures. Two sets of EEG data were used; first set was obtained from subjects with the healthy brain and the second set obtained from an unhealthy part of the brain during existence of epileptic seizures. Results show a clear discrepancy between the two groups of data with a dominantly appearance of particular nodes in the networks of an epileptic group called hubs or motif, accordingly, the visibility graph network analysis based analysis can be considered as a prediction way of epilepsy seizures

Synchronization Analysis of EEG Epilepsy by Visibility Graph Similarity

Epilepsy is a neurological disorder of different types characterized by recurrent of seizures which affects people of all ages. This paper presents visibility graph similarity as a nonlinear model to analyze the epilepsy EEG data from different brain region of healthy and patient subjects with epilepsy seizures. All EEG segments are mapped into a corresponding graph to obtain the corresponding degree of sequence for each segment, and then the difference between these degrees is constructed as a similarity between two segments. The results showed that seizure activity presented strongest nonlinear dynamic response in the form of similarity level decreasing from healthy subjects to patients. Results of other sets were found to be in agreement with our results

Synchronization Analysis of EEG Epilepsy by Visibility Network Graph and Cross-correlation

Epilepsy is a chronic neurological disorder affects people of all ages; this paper presents cross-correlation and state transfer network method to analyze synchronization between EEG data-channels from subjects with epilepsy seizures. The datasets are in two phases, preictal and ictal phase. All EEG segments are mapped into a corresponding state network graph to obtain the corresponding motifs and then the cross-correlation is applied to exhibit the synchronization changing during epilepsy seizures. The results showed that ictal phase presented high synchronization between channels, where low synchronization level is observed within preictal phase

Analyses of Changes in Electrocardiogram Signals during Hookah Smoking

Hookah Smoking results to escalation in premature beats of the ventricles, which tend to be a leading risk factor that results to sudden cardiac death (SCA). Hookah smokers tend to be at high risk for cardiovascular diseases due to tobacco consumption from a hookah device. The primary objective of this research is to analyze immediate consequences of hookah smoking on ECG. A secondary aim is to compare changes that occur in ECG before and after hookah smoking. ECG changes powerfully predict future cardiovascular disorders (CVD) events. Twenty Male volunteers who are sound in health and are in the age bracket of 18-30 years were recruited for the study. The ECG of the subjects was recorded using a 3-lead electrocardiogram. From the lead to, PR interval, QRS (amplitude) and RR interval were recorded and heart rate was also determined, (P, Q, R and S are ECG signal's parts). Various changes observed in this study were results of persistent and terrible consequences of hookah smoking that may result into chronic CVD. These abnormalities could be identified with the help of a simple noninvasive tool by determining the wave amplitude and duration of ECG parameters

Intrinsic Synchronization Analysis of Brain Activity in Obsessive-compulsive Disorders

Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample t-test and U-test has shown the significance of such new methodology

Neuroimaging tools in multimedia learning: a systematic review

This study aims to conduct a systematic review of studies on neuroimaging measurements used in multimedia learning research. The particular aim of the review is to explore how cognitive processes in multimedia learning are studied with relevant variables through neuroimaging technology. Studies that met the inclusive criteria were selected and analyzed with the data entry tool. Forty articles were reviewed based on the research questions about the research characteristics, the type of learning environments, the variables, the types of cognitive load, the other cognitive load measurements, the types of neuroimaging measures, the techniques that should be known in the field of neuroimaging to study cognitive load in multimedia learning. The results revealed that most of the studies preferred using both subjective and other objective measures to assess cognitive load in addition to neuroimaging measures. The studies examined learning outcomes, cognitive processes, and some other variables besides measuring cognitive load. The most striking observation to emerge from the analysis is that Electroencephalography, Functional Magnetic Resonance Imaging, Functional Near-Infrared Spectroscopy, and Transcranial Doppler Ultrasonography have been found as the most preferred neuroimaging tools utilized in multimedia learning research. Research results were interpreted, and several gaps in research relating to multimedia learning were identified