Preictal dysfunctions of inhibitory interneurons paradoxically lead to their rebound hyperactivity and to low-voltage-fast onset seizures in Dravet syndrome.

Epilepsies have numerous specific mechanisms. The understanding of neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction. We analyzed EEGs from DS patients carrying a SCN1A pathogenic variant, as well as epidural electrocorticograms, hippocampal local field potentials, and hippocampal single-unit neuronal activities in Scn1a+/- and Scn1aRH/+ DS mice. Strikingly, most seizures had low-voltage-fast onset in both patients and mice, which is thought to be generated by hyperactivity of GABAergic interneurons, the opposite of the main pathological mechanism of DS. Analyzing single-unit recordings, we observed that temporal disorganization of the firing of putative interneurons in the period immediately before the seizure (preictal) precedes the increase of their activity at seizure onset, together with the entire neuronal network. Moreover, we found early signatures of the preictal period in the spectral features of hippocampal and cortical field potential of Scn1a mice and of patients' EEG, which are consistent with the dysfunctions that we observed in single neurons and that allowed seizure prediction. Therefore, the perturbed preictal activity of interneurons leads to their hyperactivity at the onset of generalized seizures, which have low-voltage-fast features that are similar to those observed in other epilepsies and are triggered by hyperactivity of GABAergic neurons. Preictal spectral features may be used as predictive seizure biomarkers.

MFCC-CNN: A patient-independent seizure prediction model.

BACKGROUND: Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications. METHODS: This paper proposes a patient-independent seizure prediction model, named MFCC-CNN, to improve the generalization ability. MFCC-CNN model introduces Mel-Frequency Cepstrum Coefficients (MFCC) features and Linear Predictive Cepstral Coefficients (LPCC) features concentrated in the low frequency region, which contains more detailed information. Convolutional neural network (CNN) is used to construct a seizure prediction model. RESULTS: Experimental results showed that the proposed model obtained accuracy of 96 % , sensitivity of 92 % , specificity of 84 % and F1-score of 85 % for 24 cases in CNHB-MIT dataset. The overall performance of MFCC-CNN model is better than the other models. CONCLUSION: MFCC-CNN model does not need to be specifically customized for different patients. As a patient-independent seizure prediction model, it has good generalization ability.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

Ablation of apparent diffusion coefficient hyperintensity clusters in mesial temporal lobe epilepsy improves seizure outcomes after laser interstitial thermal therapy.

OBJECTIVE: Laser interstitial thermal therapy (LiTT) is a minimally invasive surgical procedure for intractable mesial temporal epilepsy (mTLE). LiTT is safe and effective, but seizure outcomes are highly variable due to patient variability, suboptimal targeting, and incomplete ablation of the epileptogenic zone. Apparent diffusion coefficient (ADC) is a magnetic resonance imaging (MRI) sequence that can identify potential epileptogenic foci in the mesial temporal lobe to improve ablation and seizure outcomes. The objective of this study was to investigate whether ablation of tissue clusters with high ADC values in the mesial temporal structures is associated with seizure outcome in mTLE after LiTT. METHODS: Twenty-seven patients with mTLE who underwent LiTT at our institution were analyzed. One-year seizure outcome was categorized as complete seizure freedom (International League Against Epilepsy [ILAE] Class I) and residual seizures (ILAE Class II-VI). Volumes of hippocampus and amygdala were segmented from the preoperative T1 MRI sequence. Spatially distinct hyperintensity clusters were identified in the preoperative ADC map. Proportion of cluster volume and number ablated were associated with seizure outcomes. RESULTS: The mean age at surgery was 37.5 years and the mean follow-up duration was 1.9 years. Proportions of hippocampal cluster volume (p = .013) and number (p = .03) ablated were significantly higher in patients with seizure freedom. For amygdala clusters, the proportion of cluster number ablated was significantly associated with seizure outcome (p = .026). In the combined amygdalohippocampal complex, ablation of amygdalohippocampal clusters reliably predicted seizure outcome by their volume ablated (area under the curve [AUC] = 0.7670, p = .02). SIGNIFICANCE: Seizure outcome after LiTT in patients with mTLE was associated significantly with the extent of cluster ablation in the amygdalohippocampal complex. The results suggest that preoperative ADC analysis may help identify high-yield pathological tissue clusters that represent epileptogenic foci. ADC-based cluster analysis can potentially assist ablation targeting and improve seizure outcome after LiTT in mTLE.

Daily resting-state intracranial EEG connectivity for seizure risk forecasts.

Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.

Seizure forecasting: Where do we stand?

A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.

An epileptic seizure prediction model based on a time-wise attention simulation module and a pretrained ResNet.

Epilepsy is a neurological disorder that affects approximately 1% of the world's populations. Epilepsy prediction has been of great interest as it can identify and warn of an upcoming seizure, and to reduce the burden of the unpredictability of seizures. In this paper, we proposed a new seizure prediction model, TASM_ResNet, based on a time-wise attention simulation module and a pre-trained ResNet, using intracranial EEG signals. The simulation module with a time-wise attention was designed to convert EEG data into image like data and extract temporal features from raw data. Pre-trained ResNet was applied to reduce the amount of training data without initial training. Moreover, since the data is extremely imbalanced, we used an improved focal loss (FL) instead of the cross-entropy loss and investigated the optimal parameters for FL. Compared with a state-of-art CNN model, our proposed model achieved a better average AUC of 0.877. Moreover, our results demonstrated that EEG signals can be migrated to the image network which was pre-trained on large data set through a simulation module.

Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.

Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for patients. Moreover, deep-learning techniques require a large dataset and a long time for training from scratch. Therefore, in this study, EEG-EEG or EEG-ECG transfer learning strategies were applied to explore their effectiveness for the training of simple cross-domain convolutional neural networks (CNNs) used in seizure prediction and sleep staging systems, respectively. The seizure model detected interictal and preictal periods, whereas the sleep staging model classified signals into five stages. The patient-specific seizure prediction model with six frozen layers achieved 100% accuracy for seven out of nine patients and required only 40 s of training time for personalization. Moreover, the cross-signal transfer learning EEG-ECG model for sleep staging achieved an accuracy approximately 2.5% higher than that of the ECG model; additionally, the training time was reduced by >50%. In summary, transfer learning from an EEG model to produce personalized models for a more convenient signal can both reduce the training time and increase the accuracy; moreover, challenges such as data insufficiency, variability, and inefficiency can be effectively overcome.

Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning.

Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. Epileptic siezure prediction systems face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model, which is able to achieve enhanced capability by utilizing a significant number of seizure patterns from globally distributed patients while maintaining data privacy. The determination of the preictal state is influenced by global and local model-assisted decision making by modeling the two-level edge layer. The Spiking Encoder (SE), integrated with the Graph Convolutional Neural Network (Spiking-GCNN), works as the local model trained using a bi-timescale approach. Each local model utilizes the aggregated seizure knowledge obtained from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. Thus, the proposed approach achieved 96.33% sensitivity and 96.14% specificity when tested on the CHB-MIT EEG dataset when modeling was performed using the bi-timescale approach and Spiking-GCNN-based epileptic pattern learning. Moreover, the adoption of federated learning greatly assists the proposed system, yielding a 96.28% higher accuracy as a result of addressing data scarcity.

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models.

OBJECTIVE: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. METHODS: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. RESULTS: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.

Seizure count forecasting to aid diagnostic testing in epilepsy.

OBJECTIVE: Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug-resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure-related morbidity. Here, we evaluated the performance of a state-space method that uses prior seizure count data to predict future counts. METHODS: A Bayesian negative-binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1-7 days ahead. RESULTS: One-day-ahead prediction of the number of electrographic seizures using a negative-binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained >50% for forecast horizons of up to 7 days. Superior performance (mean error = .99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer-valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day. SIGNIFICANCE: This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions.

Serum neuron-specific enolase: a new tool for seizure risk monitoring after status epilepticus.

BACKGROUND AND PURPOSE: There is a need for accurate biomarkers to monitor electroencephalography (EEG) activity and assess seizure risk in patients with acute brain injury. Seizure recurrence may lead to cellular alterations and subsequent neurological sequelae. Whether neuron-specific enolase (NSE) and S100-beta (S100B), brain injury biomarkers, can reflect EEG activity and help to evaluate the seizure risk was investigated. METHODS: Eleven patients, admitted to an intensive care unit for refractory status epilepticus, who underwent a minimum of 3 days of continuous EEG concomitantly with daily serum NSE and S100B assays were included. At 103 days the relationships between serum NSE and S100B levels and two EEG scores able to monitor the seizure risk were investigated. Biochemical biomarker thresholds able to predict seizure recurrence were sought. RESULTS: Only NSE levels positively correlated with EEG scores. Similar temporal dynamics were observed for the time courses of EEG scores and NSE levels. NSE levels above 17 ng/ml were associated with seizure in 71% of patients. An increase of more than 15% of NSE levels was associated with seizure recurrence in 80% of patients. CONCLUSIONS: Our study highlights the potential of NSE as a biomarker of EEG activity and to assess the risk of seizure recurrence.

Predictive value of video alone in diagnosis of epileptic vs paroxysmal nonepileptic events in children.

OBJECTIVE: Previous studies examined the use of video-based diagnosis and the predictive value of videos for differentiation of epileptic seizures (ES) from paroxysmal nonepileptic events (PNEE) in the adult population. However, there are no such published studies strictly on the pediatric population. Using video-EEG diagnosis as a gold standard, we aimed to determine the diagnostic predictive value of videos of habitual events with or without additional clinical data in differentiating the PNEE from ES in children. METHODS: Consecutive admissions to our epilepsy monitoring unit between June 2020 and December 2020 were analyzed for events of interest. Four child neurologists blinded to the patient's diagnosis formulated a diagnostic impression based upon the review of the video alone and again after having access to basic clinical information, in addition to the video. Features of the video which helped to make a diagnosis were identified by the reviewers as a part of a survey. RESULTS: A total of 54 patients were included (ES n = 24, PNEE n = 30). Diagnostic accuracy was calculated for each reviewer and combined across all the ratings. Diagnostic accuracy by video alone was 74.5% (sensitivity 80.8%, specificity 66.7%). Providing reviewers with basic clinical information in addition to the videos significantly improved diagnostic accuracy compared to viewing the videos alone. Inter-rater reliability between four reviewers based on the video alone showed moderate agreement (κ = 0.51) and unchanged when additional clinical data were presented (κ = 0.51). The ES group was significantly more likely to demonstrate changes in facial expression, generalized stiffening, repetitive eye blinks, and eye deviation when compared with the PNEE group, which was more likely to display bilateral myoclonic jerking. CONCLUSIONS: Video review of habitual events by Child Neurologists may be helpful in reliably distinguishing ES from PNEE in children, even without included clinical information.

T-wave heterogeneity crescendo in the surface EKG is superior to heart rate acceleration for seizure prediction.

We examined whether T-wave heterogeneity (TWH) on the surface electrocardiographic (EKG) could predict epileptic seizure onset. Patients with electroencephalography-confirmed generalized tonic-clonic seizures (GTCS) (n = 6) exhibited abnormal elevations in TWH (>80 µV) at baseline (105 ±â€¯20.4 µV), which increased from 30 min prior to seizure without heart rate increases > 2 beats/min until 10 min pre-seizure. Specifically, TWH on 3-lead surface EKG patch recordings increased from 1-hour baseline to 30 min (<0.05), 20 min (p < 0.002), 10 min (p = 0.01), and 1 min (p = 0.01) before seizure onset. At 10 min following GTCS, TWH returned to 110 ±â€¯20.3 µV, similar to baseline (p = 0.54). This pre-ictal TWH warning pattern was not present in patients with psychogenic nonepileptic seizures (PNES) (n = 3), as TWH did not increase until PNES and returned to baseline within 10 min after PNES. Acute elevations in TWH may predict impending GTCS and may discriminate patients with GTCS from those with behaviorally similar PNES.

An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT.

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients' quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively.

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals.

Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients' health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals.

Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique's accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.

Serum Proteins Associated with Blood-Brain Barrier as Potential Biomarkers for Seizure Prediction.

As 30% of epileptic patients remain drug-resistant, seizure prediction is vital. Induction of epileptic seizure is a complex process that can depend on factors such as intrinsic neuronal excitability, changes in extracellular ion concentration, glial cell activity, presence of inflammation and activation of the blood−brain barrier (BBB). In this study, we aimed to assess if levels of serum proteins associated with BBB can predict seizures. Serum levels of MMP-9, MMP-2, TIMP-1, TIMP-2, S100B, CCL-2, ICAM-1, P-selectin, and TSP-2 were examined in a group of 49 patients with epilepsy who were seizure-free for a minimum of seven days and measured by ELISA. The examination was repeated after 12 months. An extensive medical history was taken, and patients were subjected to a follow-up, including a detailed history of seizures. Serum levels of MMP-2, MMP-9, TIMP-1, CCL-2, and P-selectin differed between the two time points (p < 0.0001, p < 0.0001, p < 0.0001, p < 0.0001, p = 0.0035, respectively). General linear model analyses determined the predictors of seizures. Levels of MMP-2, MMP-9, and CCL-2 were found to influence seizure count in 1, 3, 6, and 12 months of observation. Serum levels of MMP-2, MMP-9, and CCL-2 may be considered potential biomarkers for seizure prediction and may indicate BBB activation.

Increased branched-chain amino acids at baseline and hours before a spontaneous seizure in the human epileptic brain.

The objective of this study was to monitor the extracellular brain chemistry dynamics at baseline and in relation to spontaneous seizures in human patients with refractory epilepsy. Thirty patients with drug-resistant focal epilepsy underwent intracranial electroencephalography and concurrent brain microdialysis for up to 8 continuous days. Extracellular brain glutamate, glutamine, and the branched-chain amino acids (BCAAs) valine, leucine, and isoleucine were quantified in the dialysis samples by liquid chromatography-tandem mass spectrometry. Extracellular BCAAs and glutamate were chronically elevated at baseline by approximately 1.5-3-fold in brain regions of seizure onset and propagation versus regions not involved by seizures. Moreover, isoleucine increased significantly above baseline as early as 3 h before a spontaneous seizure. BCAAs play important roles in glutamatergic neurotransmission, mitochondrial function, neurodegeneration, and mammalian target of rapamycin signaling. Because all of these processes have been implicated in epilepsy, the results suggest a novel role of BCAAs in the pathogenesis of spontaneous seizures.

Machine learning and wearable devices of the future.

Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.