Interictal State Research Articles

Predictive analytics & modeling for modern health care system for cerebral palsy patients

The time-shifting elements and high between individual changeability make early forecast of a seizure express a difficult errand. Numerous examinations have demonstrated that EEG signals do have profitable data that if effectively broke down, could help in the expectation of seizures in epileptic patients before their event. A few numerical changes have been broke down for its connection with seizure beginning forecast and a progression of analyses were done to ensure their qualities. New calculations are exhibited to help elucidate, screen, and cross-approve the order of EEG signs to foresee the ictal (for example seizure) states, explicitly the preictal, interictal, and postictal states in the mind. These new strategies show promising outcomes in distinguishing the nearness of a preictal stage before the ictal state. Artificial Intelligence and Machine Learning are playing major role in Diagnosis and Predicting the diseases, EEG signal were recorded with 16 channel system from brain scalp and stored in system. From recorded database seizure levels were analyzed from each channel by articrafting with ICA and PCA algorithms and processed through Band pass filter to identify the Delta, Theta, Alpha and Beta levels. After collecting the brain signals from each channel they were bided with FFT to get the data in time domain series. A machine learning model is developed using python programming by sampling on obtained EEG data, sampled data is trained on to the system. Machine Learning algorithms were applied to the data to accuracy and predict the accuracy on developed model neural network is applied to identify the True-False values. The analyzed data is integrated with IOT framework to Diagnosis the Cerebral Palsy patient remotely. Remote monitoring system for the persons with Intellectual disabilities and update the health status to care takers time to time while they are at work. It’s also reduces the patient work load by checking his details staying at home rather going to a hospital. If system identifies any changes in patient heart rate, brain signals, body temperature, the system alerts the doctor and respective relatives about patient’s status over IOT and also stores the patient details in the cloud.

Endogenous Na+, K+-ATPase inhibitors and CSF [Na+] contribute to migraine formation.

There is strong evidence that neuronal hyper-excitability underlies migraine, and may or may not be preceded by cortical spreading depression. However, the mechanisms for cortical spreading depression and/or migraine are not established. Previous studies reported that cerebrospinal fluid (CSF) [Na+] is higher during migraine, and that higher extracellular [Na+] leads to hyper-excitability. We raise the hypothesis that altered choroid plexus Na+, K+-ATPase activity can cause both migraine phenomena: inhibition raises CSF [K+] and initiates cortical spreading depression, while activation raises CSF [Na+] and causes migraine. In this study, we examined levels of specific Na+, K+-ATPase inhibitors, endogenous ouabain-like compounds (EOLC), in CSF from migraineurs and controls. CSF EOLC levels were significantly lower during ictal migraine (0.4 nM +/- 0.09) than from either controls (1.8 nM +/- 0.4) or interictal migraineurs (3.1 nM +/- 1.9). Blood plasma EOLC levels were higher in migraineurs than controls, but did not differ between ictal and interictal states. In a Sprague-Dawley rat model of nitroglycerin-triggered central sensitization, we changed the concentrations of EOLC and CSF sodium, and measured aversive mechanical threshold (von Frey hairs), trigeminal nucleus caudalis activation (cFos), and CSF [Na+] (ultra-high field 23Na MRI). Animals were sensitized by three independent treatments: intraperitoneal nitroglycerin, immunodepleting EOLC from cerebral ventricles, or cerebroventricular infusion of higher CSF [Na+]. Conversely, nitroglycerin-triggered sensitization was prevented by either vascular or cerebroventricular delivery of the specific Na+, K+-ATPase inhibitor, ouabain. These results affirm our hypothesis that higher CSF [Na+] is linked to human migraine and to a rodent migraine model, and demonstrate that EOLC regulates them both. Our data suggest that altered choroid plexus Na+, K+-ATPase activity is a common source of these changes, and may be the initiating mechanism in migraine.

Early Detection of Human Epileptic Seizures Based on Intracortical Microelectrode Array Signals.

We examine, for the first time, the use of intracortical microelectrode array (MEA) signals for early detection of human epileptic seizures. 4×4 mm2 96-channel-MEA recordings were obtained during neuro-monitoring preceding resective surgery in five participants. The participant-specific seizure-detection framework consisted of: first, feature extraction from local field potentials (LFPs) and multiunit activity (MUA); second, nonlinear cost-sensitive support vector machine (SVM) classification of ictal and interictal states based on LFP, MUA, and combined LFP-MUA (a SVM was trained for each participant separately); and third, Kalman filter postprocessing of SVM scoring functions. Performance was assessed on data including 17 seizures and 39.0h interictal and preictal recordings. The use of combined LFP-MUA features resulted in 100% sensitivity with short detection latency (average: 2.7 s; median: 2.5 s) and five false alarms (0.13/h). The average detection performance based on the area under the receiver operating characteristic corresponded to 0.97. Importantly, technically false alarms were related to epileptiform activity, subclinical seizures, and recording artifacts. Extreme gradient boosting classifiers ranked features based on LFP spectral coherence or MUA count among the top features for seizures characterized by spike-wave complexes, whereas features related to LFP power spectra were ranked higher for seizures characterized by sustained gamma LFP oscillations. The combination of intracortical LFP and MUA signals may allow reliable detection of human epileptic seizures by improving latency and false alarm rate. Intracortical MEAs provide promising signals for closed-loop seizure-control systems based on seizure early-detection in people with pharmacologically resistant epilepsies.

It's All About the Networks.

Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies Lagarde S, Roehri N, Lambert I, et al. Brain. 2018;141(10):2966-2980. doi:10.1093/brain/awy214Drug-refractory focal epilepsies are network diseases associated with functional connectivity alterations both during ictal and interictal periods. A large majority of studies on the interictal/resting state have focused on functional magnetic resonance imaging (MRI)-based functional connectivity. Few studies have used electrophysiology, despite its high-temporal capacities. In particular, stereotactic-electroencephalogram (EEG) is highly suitable to study functional connectivity because it permits direct intracranial electrophysiological recordings with relative large-scale sampling. Most previous studies in stereotactic-EEG have been directed toward temporal lobe epilepsy, which does not represent the whole spectrum of drug-refractory epilepsies. The present study aims at filling this gap, investigating interictal functional connectivity alterations behind cortical epileptic organization and its association with postsurgical prognosis. To this purpose, we studied a large cohort of 59 patients with malformation of cortical development explored by stereotactic-EEG with a wide spatial sampling (76 distinct brain areas were recorded, median of 13.2 per patient). We computed functional connectivity using nonlinear correlation. We focused on 3 zones defined by stereotactic-EEG ictal activity: the epileptogenic zone (EZ), the propagation zone (PZ), and the noninvolved zone. First, we compared within-zone and between-zones functional connectivity. Second, we analyzed the directionality of functional connectivity between these zones. Third, we measured the associations between functional connectivity measures and clinical variables, especially postsurgical prognosis. Our study confirms that functional connectivity differs according to the zone under investigation. We found: (1) a gradual decrease in the within-zone functional connectivity with higher values for EZ and PZ, and lower for noninvolved zones; (2) preferential coupling between structures of the EZ; (3) preferential coupling between EZ and PZ; and (4) poorer postsurgical outcome in patients with higher functional connectivity of non-involved zone (within-noninvolved zone, between noninvolved zone, and PZ functional connectivity). Our work suggests that, even during the interictal state, functional connectivity is reinforced within epileptic cortices (EZ and PZ) with a gradual organization. Moreover, larger functional connectivity alterations, suggesting more diffuse disease, are associated with poorer postsurgical prognosis. This is consistent with computational studies suggesting that connectivity is crucial in order to model the spatiotemporal dynamics of seizures. Dynamic brain network states in human generalized spike-wave discharges Tangwiriyasakul C, Perani S, Centeno M, et al. Brain. 2018;141(10):2981-2994.Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < .01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < .01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 seconds after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in nondischarge epochs in patients) from 16 seconds to 10 seconds before discharges, then ramped up steeply to a significantly high level of synchrony 2 seconds before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = .004). During 6 seconds prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal, and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 minute) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events.

Increased suicidality in patients with cluster headache.

To investigate suicidality related to cluster headache and factors associated with increased suicidality in cluster headache patients. In this multicenter study, 193 cluster headache patients were recruited between September 2016 and August 2018. Patients were asked about their suicidality during and between attacks, specifically about passive suicidal ideation, active suicidal ideation, suicide plan, and suicide attempt. Univariable and multivariable logistic regression analyses were performed to evaluate the factors associated with high ictal suicidality (sum of positive response ≥ 2). Patients were followed up when they were in the between-bouts period. A total of 175 cluster headache patients in the in-bout period were included in this study. Passive suicidal ideation, active suicidal ideation, suicidal planning, and suicidal attempt were reported by 111 (64.2%), 62 (35.8%), 10 (5.8%), and four (2.3%) patients during attacks; seven (4.0%), six (3.5%), five (2.9%) and two (1.2%) patients interictally; and none (0%), one (1.9%), one (1.9%), and none (0%) among patients in the between-bouts period. Factors associated with high ictal suicidality were longer disease duration, the Headache Impact Test score, and the Patient Health Question-9 score (multivariable OR = 1.90 per 10-year increase in disease duration, 95% CI = 1.18-3.05, p = 0.008; multivariable OR = 3.19 per 10-point increase in HIT-6, 95% CI = 1.73-5.87, p < 0.001; multivariable OR = 2.11 per 10-point increase in PHQ-9, 95% CI = 1.13-3.95, p = 0.020, respectively). Cluster headache attack carries a high suicidality compared to the interictal or between-bouts state. An intensive treatment to reduce cluster headache burden may be helpful to alleviate suicide risk in cluster headache patients.

Dynamic origin of spike and wave discharges in the brain

Spike and wave discharges are the main electrographic characteristic of a number of epileptic brain disorders including childhood absence epilepsy and photosensitive epilepsy. The basic dynamic mechanism that underlies the occurrence of these abnormal electrical patterns in the brain is not well understood. The current paper aims to provide a dynamic explanation for features and generation mechanism of spike and wave discharges in the brain. The main proposition of this study is that epileptic seizures could be interpreted as a resonance phenomenon rather than a limit cycle behavior. To shows this, a revised version of Jansen-Rit neural mass model is employed. The system can switch between monostable and bistable regimes, which are considered in this paper as wake and sleep states of the brain, respectively. In particular, it is shown that, in monostable region, the model can depict the alpha rhythm and alpha rhythm suppression due to mental activity. Frequency responses of the model near the bistable regime demonstrate that high amplitude harmonic excitation may lead to spike and wave like oscillations. Based on the computational results and the concept of stochastic resonance, a model for absence epilepsy is presented which can simulate spontaneous transitions between ictal and interictal states. Finally, it is shown that spike and wave discharges during epileptic seizures can be explained as a resonance phenomenon in a nonlinear system.

Migraine classification using somatosensory evoked potentials.

The automatic detection of migraine states using electrophysiological recordings may play a key role in migraine diagnosis and early treatment. Migraineurs are characterized by a deficit of habituation in cortical information processing, causing abnormal changes of somatosensory evoked potentials. Here, we propose a machine learning approach to utilize somatosensory evoked potential-based biomarkers for migraine classification in a noninvasive setting. Forty-two migraine patients, including 29 interictal and 13 ictal, were recruited and compared with 15 healthy volunteers of similar age and gender distribution. The right median nerve somatosensory evoked potentials were collected from all subjects. State-of-the-art machine learning algorithms including random forest, extreme gradient-boosting trees, support vector machines, K-nearest neighbors, multilayer perceptron, linear discriminant analysis, and logistic regression were used for classification and were built upon somatosensory evoked potential features in time and frequency domains. A feature selection method was employed to assess the contribution of features and compare it with previous clinical findings, and to build an optimal feature set by removing redundant features. Using a set of relevant features and different machine learning models, accuracies ranging from 51.2% to 72.4% were achieved for the healthy volunteers-ictal-interictal classification task. Following model and feature selection, we successfully separated the three groups of subjects with an accuracy of 89.7% for the healthy volunteers-ictal, 88.7% for healthy volunteers-interictal, 80.2% for ictal-interictal, and 73.3% for healthy volunteers-ictal-interictal classification tasks, respectively. Our proposed model suggests the potential use of somatosensory evoked potentials as a prominent and reliable signal in migraine classification. This non-invasive somatosensory evoked potential-based classification system offers the potential to reliably separate migraine patients in ictal and interictal states from healthy controls.

Triheptanoin alters [U-13C6]-glucose incorporation into glycolytic intermediates and increases TCA cycling by normalizing the activities of pyruvate dehydrogenase and oxoglutarate dehydrogenase in a chronic epilepsy mouse model

Triheptanoin is anticonvulsant in several seizure models. Here, we investigated changes in glucose metabolism by triheptanoin interictally in the chronic stage of the pilocarpine mouse epilepsy model. After injection of [U-13C6]-glucose (i.p.), enrichments of 13C in intermediates of glycolysis and the tricarboxylic acid (TCA) cycle were quantified in hippocampal extracts and maximal activities of enzymes in each pathway were measured. The enrichment of 13C glucose in plasma was similar across all groups. Despite this, we observed reductions in incorporation of 13C in several glycolytic intermediates compared to control mice suggesting glucose utilization may be impaired and/or glycogenolysis increased in the untreated interictal hippocampus. Triheptanoin prevented the interictal reductions of 13C incorporation in most glycolytic intermediates, suggesting it increased glucose utilization or – as an additional astrocytic fuel – it decreased glycogen breakdown. In the TCA cycle metabolites, the incorporation of 13C was reduced in the interictal state. Triheptanoin restored the correlation between 13C enrichments of pyruvate relative to most of the TCA cycle intermediates in “epileptic” mice. Triheptanoin also prevented the reductions of hippocampal pyruvate dehydrogenase and 2-oxoglutarate dehydrogenase activities. Decreased glycogen breakdown and increased glucose utilization and metabolism via the TCA cycle in epileptogenic brain areas may contribute to triheptanoin's anticonvulsant effects.

Epileptic seizure detection using cross-bispectrum of electroencephalogram signal

PurposeThe automatic detection of epileptic seizures in EEG data from extended recordings can make an important contribution to the diagnosis of epilepsy as it can efficiently reduce the workload of medical staff. MethodsThis paper describes how features based on cross-bispectrum can help with the detection of epileptic seizure activity in EEG data. Features were extracted from multi-channel intracranial EEG (iEEG) data from the Freiburg iEEG recordings of 21 patients with focal epilepsy. These features were used as a support vector machine classifier input to discriminate ictal from inter-ictal states. A post-processing method was applied to the classifier output in order to improve classification accuracy. ResultsA sensitivity of 95.8%, specificity of 96.7%, and accuracy of 96.8% were achieved. The false detection rate (FDR) was zero for 10 patients and very low for the rest. ConclusionsThe results show that the proposed method distinguishes better between ictal and inter-ictal iEEG epochs than other seizure detection methods. The proposed method has a higher accuracy index than achievable with a number of previously described approaches. Also, the method is rapid and easy and may be helpful in online epileptic seizure detection and prediction systems.

Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks

An increasing number of algorithms have been proposed for epileptic seizure prediction in recent years. But most of them are based on a partition of the electroencephalograph (EEG) signal of an epileptic patient into preictal, ictal (seizure), and interictal states. In this paper, we propose to further divide the preictal interval into multiple subintervals. Besides discriminating the seizure state from the preictal and interictal states, we also distinguish the preictal subintervals from each other. An epileptic state classification algorithm for epileptic EEG signals is then developed. The amplitude spectrums of EEG signals from 18 channels are firstly calculated and divided into 19 frequency subbands. The mean amplitude spectrum (MAS) on each of the 19 frequency subbands is then computed for each channel to form a MAS map of size 18 $$\times$$ 19. Finally, the MAS map is fed to a convolutional neural network (CNN) for feature extraction and a support vector machine (SVM) is employed for the epileptic state classification. Experiments show that, for a three-subinterval partition of the preictal state, a classification accuracy of 86.25% has been achieved on the CHB-MIT database by the MAS-based epileptic state classification algorithm using CNN and SVM.

Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis.

Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.

Epileptic Seizure Detection With Permutation Fuzzy Entropy Using Robust Machine Learning Techniques

The automatic and accurate determination of the epileptogenic area can assist doctors in presurgical evaluation by providing higher security and quality of life. Visual inspection of electroencephalogram (EEG) signals is expensive, time-consuming and prone to errors. Several numbers of automated seizure detection frameworks were proposed to replace the traditional methods and to assist neurophysiologists in identifying epileptic seizures accurately. However, these systems lagged in achieving high performance due to the anti-noise ability of feature extraction techniques, while EEG signals are highly susceptible to noise during acquisition. The present study put forwards a new entropy index Permutation Fuzzy Entropy (PFEN), which may delineate between ictal and interictal state of epileptic seizure using different machine learning classifiers. 10-fold cross-validation has been used to avoid the over-fitting of the classification model to achieve unbiased, stable, and reliable performance. The proposed index correctly distinguishes ictal and interictal states with an average accuracy of 98.72%, sensitivity of 98.82% and a specificity of 98.63%, across 21 patients with six epileptic seizure origins. The proposed system manifests the fact that lower PFEN characterizes the EEG during seizure state than in the Interictal seizure state. The study also helps us to investigate the more profound enactment of different classifiers in term of their distance metrics, learning rate, distance, weights, multiple scales, etc. rather than the conventional methods in the literature. Compared to other state of art entropy-based feature extraction methods, PFEN showed its potential to be a promising non-linear feature for achieving high accuracy and efficiency in seizure detection. It also show’s its feasibility towards the development of a real-time EEG-based brain monitoring system for epileptic seizure detection.

Visual input drives increased occipital responsiveness and harmonized oscillations in multiple cortical areas in migraineurs.

Migraineurs are hypersensitive for most sensory domains like visual, auditory or somatosensory processing even outside of attacks. This behavioral peculiarity is mirrored by findings of cortical hyper-responsivity already in the interictal state. Using repetitive visual stimulation to elicit steady state visually evoked potentials (SSVEP) in 30 interictal episodic migraineurs and 30 controls we show hyper-responsivity of the visual cortex in the migraineurs. Additionally, the occipital regions were remarkably stronger coupled to the temporal, premotor and the anterior cingulate cortex than in headache free controls. These data suggest harmonized oscillations of different cortical areas as a response to visual input which might be driven by the cuneus. Furthermore, the increased coupling is modulated by the current state of the migraine cycle as the coupling was significantly stronger in patients with longer interictal periods.

High-frequency oscillations and spikes running down after SEEG-guided thermocoagulations in the epileptogenic network of periventricular nodular heterotopia

ObjectiveEpilepsy associated with periventricular nodular heterotopia (PNH) is characterized by complex relationships between the heterotopic and the normotopic cortex during the interictal state and at seizure onset. High-frequency oscillations (HFO) have been proposed as a marker of epileptogenicity that might reflect disease activity. The effects of thermocoagulations on epileptogenicity in this context remain unknown. We aimed to investigate the interictal HFO- and spike profiles of different cortical structures before and after two consecutive SEEG-guided thermocoagulations, in correlation with seizure outcome, in a patient with PNH-related drug-resistant epilepsy. MethodsThe epileptogenic zone (EZ) was defined by SEEG analysis based on the Epileptogenicity Index. Interictal spikes, ripples (80–250 Hz) and fast ripples (FR, 250-330 Hz) were analyzed within the heterotopia, the temporal neocortex and the hippocampus. ResultsThe SEEG recordings revealed a distributed EZ involving the heterotopia and the posterior temporal neocortex. Both structures were targeted by thermocoagulations. Background spikes, ripples and FR-rates were significantly higher in PNH compared to the normotopic cortex. A drastic reduction of spikes (by over 80%) and absence of FR were demonstrated both in the PNH and in the neocortex during the second SEEG exploration 6 months after the first thermocoagulation, whereas no significant difference was observed in the posterior hippocampus. Ripples were significantly reduced by the first and suppressed by the second thermocoagulation within the three structures. Seizures relapsed after two months but decreased in frequency after the first thermocoagulation. Sustained seizure-freedom was achieved only after the second procedure. ConclusionsOur data demonstrate the running down of interictal HFO and spikes within the epileptogenic network following thermocoagulations of heterotopic and normotopic sites involved at seizure onset. This dynamics was in good correlation with significantly improved seizure control. SignificanceCombination of ictal and different interictal markers of epileptogenicity, including HFO and spike analysis, is important to get the full picture of the epileptogenic zone and could help to evaluate the disease activity.

Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction

The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%.

A Patient-Specific Approach for Short-Term Epileptic Seizures Prediction Through the Analysis of EEG Synchronization.

This paper proposes a patient-specific approach for short-term prediction (i.e., within few minutes) of epileptic seizures. We use noninvasive EEG data, since the aim is exploring the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. Our approach is based on finding synchronization patterns in the EEG that allow to distinguish in real time preictal from interictal states. In practice, we develop easily computable functions over a graph model to capture the variations in the synchronization, and employ a classifier for identifying the preictal state. We compare two state-of-the-art classification algorithms and a simple and computationally inexpensive threshold-based classifier developed ad hoc. Results on publicly available scalp EEG database and on scalp data of the patients of the Unit of Neurology and Neurophysiology at University of Siena show that this simple and computationally viable processing is able to highlight the changes in synchronization when a seizure is approaching. The proposed approach, characterized by low computational requirements and by the use of noninvasive techniques, is a step toward the development of portable and wearable devices for real-life use.

Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies

Drug-refractory focal epilepsies are network diseases associated with functional connectivity alterations both during ictal and interictal periods. A large majority of studies on the interictal/resting state have focused on functional MRI-based functional connectivity. Few studies have used electrophysiology, despite its high temporal capacities. In particular, stereotactic-EEG is highly suitable to study functional connectivity because it permits direct intracranial electrophysiological recordings with relative large-scale sampling. Most previous studies in stereotactic-EEG have been directed towards temporal lobe epilepsy, which does not represent the whole spectrum of drug-refractory epilepsies. The present study aims at filling this gap, investigating interictal functional connectivity alterations behind cortical epileptic organization and its association with post-surgical prognosis. To this purpose, we studied a large cohort of 59 patients with malformation of cortical development explored by stereotactic-EEG with a wide spatial sampling (76 distinct brain areas were recorded, median of 13.2 per patient). We computed functional connectivity using non-linear correlation. We focused on three zones defined by stereotactic-EEG ictal activity: the epileptogenic zone, the propagation zone and the non-involved zone. First, we compared within-zone and between-zones functional connectivity. Second, we analysed the directionality of functional connectivity between these zones. Third, we measured the associations between functional connectivity measures and clinical variables, especially post-surgical prognosis. Our study confirms that functional connectivity differs according to the zone under investigation. We found: (i) a gradual decrease of the within-zone functional connectivity with higher values for epileptogenic zone and propagation zone, and lower for non-involved zones; (ii) preferential coupling between structures of the epileptogenic zone; (iii) preferential coupling between epileptogenic zone and propagation zone; and (iv) poorer post-surgical outcome in patients with higher functional connectivity of non-involved zone (within- non-involved zone, between non-involved zone and propagation zone functional connectivity). Our work suggests that, even during the interictal state, functional connectivity is reinforced within epileptic cortices (epileptogenic zone and propagation zone) with a gradual organization. Moreover, larger functional connectivity alterations, suggesting more diffuse disease, are associated with poorer post-surgical prognosis. This is consistent with computational studies suggesting that connectivity is crucial in order to model the spatiotemporal dynamics of seizures.10.1093/brain/awy214_video1awy214media15833456182001.

Automatic Seizure Detection Based on Nonlinear Dynamical Analysis of EEG Signals and Mutual Information.

Introduction:In this paper, nonlinear dynamical analysis based on Recurrence Quantification Analysis (RQA) is employed to characterize the nonlinear EEG dynamics. RQA can provide useful quantitative information on the regular, chaotic, or stochastic property of the underlying dynamics.Methods:We use the RQA-based measures as the quantitative features of the nonlinear EEG dynamics. Mutual Information (MI) was used to find the most relevant feature subset out of RQA-based features. The selected features were fed into an artificial neural network for grouping of EEG recordings to detect ictal, interictal, and healthy states. The performance of the proposed procedure was evaluated using a database for different classification cases.Results:The combination of five selected features based on MI achieved 100% accuracy, which demonstrates the superiority of the proposed method.Conclusion:The results showed that the nonlinear dynamical analysis based on Rcurrence Quantification Analysis (RQA) can be employed as a suitable approach for characterizing the nonlinear EEG dynamics and detecting the seizure.

EEG based epileptiform pattern recognition inside and outside the seizure states

Abstract Automatic diagnosis of epilepsy by computers based on Electroencephalography (EEG) analysis is a beneficial practice which increases recognition rate, speeds up diagnosis and saves physicians from long hours of EEG inspection. Most studies on this subject report results on detecting seizures; but seizures, which appear during the ictal states are very rare to catch. Therefore, an efficient algorithm must be able to detect epilepsy during nonseizure periods, or in other words interictal states as well. We present a novel algorithm to detect epileptiform patterns during both states: ictal and interictal. For this purpose, we use wavelet packet analysis (WPA) rather than traditional time and frequency domain methods. WPA, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high frequency resolution than Wavelet analysis. WPA subimages are analyzed further to obtain feature vectors of Log Energy Entropy, Norm Entropy and Energy. These features are fed into a classifier, multilayer perceptron (MLP). We test our method on a well-known and widely studied database which includes healthy, ictal and interictal EEG recordings. Normal vs. Ictal and Interictal vs. Ictal zone classifications are realized at 100% accuracy by using any of the three features. Nonseizure vs. Seizure state classification is realized with 100% accuracy using Norm entropy. Normal vs. Interictal zone classification is achieved with 100% accuracy using Log Energy Entropy. Normal vs. Interictal vs. Ictal Zone classifications are considered as two-step binary classifications of Nonseizure vs. Seizure state followed by Normal vs. Interictal zone both of which are realized at 100% accuracy. Therefore, jointly use of Log Energy Entropy and Norm entropy is able to realize all possible classifications with 100% accuracy. We make the most comprehensive comparison of results belonging to all possible classification categories reported on the same datasets. The proposed method outperforms all the other results in all possible classifications by achieving 100% accuracy in all possible classifications. The method presented is the only single method that achieves this. Overall, the method provides us with a promising tool for the detection of epileptiform patterns during and outside the seizure states.

PHENOMENOLOGY AND THERAPY OF AFFECTIVE DISORDERS IN EPILEPSIA

The paper proposes a typology of affective disorders in epilepsy, including preictal, ictal, interictal and postictal states. It also discusses various variants of affective states (depressive, dysphoric, manic) in epilepsy. Mild, moderate and severe depression has been identified in patients with epilepsy depending on the degree of severity of depressive symptoms. The paper describes the clinical impressions of patients with epilepsy which showed the efficacy of anticonvulsants in affective disorders.