Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.

• Klíčová slova
• Open Data Ecosystem for the Neurosciences, ensemble methods, epilepsy, intracranial EEG, refractory epilepsy, seizure prediction,
• MeSH
• algoritmy * MeSH
• crowdsourcing MeSH
• elektroencefalografie MeSH
• elektrokortikografie metody   MeSH
• epilepsie parciální diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• prediktivní hodnota testů MeSH
• refrakterní epilepsie diagnóza   MeSH
• reprodukovatelnost výsledků MeSH
• senzitivita a specificita MeSH
• spánek MeSH
• strojové učení MeSH
• studie proveditelnosti MeSH
• záchvaty diagnóza   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Accurate seizure prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead seizures per patient) from prediction-resistant patients who had the lowest seizure prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-seizure versus pre-seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant seizure prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for seizure prediction will enable further worldwide community study of the data to yield greater improvements in prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.

• MeSH
• algoritmy MeSH
• crowdsourcing metody   MeSH
• dospělí MeSH
• elektroencefalografie metody   MeSH
• epilepsie patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mozek diagnostické zobrazování   patofyziologie   MeSH
• prediktivní hodnota testů MeSH
• předpověď metody   MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• záchvaty patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH

Our goal was to identify highly accurate empirical models for the prediction of the risk of febrile seizure (FS) and FS recurrence. In a prospective, three-arm, case-control study, we enrolled 162 children (age 25.8 ± 17.1 months old, 71 females). Participants formed one case group (patients with FS) and two control groups (febrile patients without seizures and healthy controls). The impact of blood iron status, peak body temperature, and participants' demographics on FS risk and recurrence was investigated with univariate and multivariate statistics. Serum iron concentration, iron saturation, and unsaturated iron-binding capacity differed between the three investigated groups (pFWE < 0.05). These serum analytes were key variables in the design of novel multivariate linear mixture models. The models classified FS risk with higher accuracy than univariate approaches. The designed bi-linear classifier achieved a sensitivity/specificity of 82%/89% and was closest to the gold-standard classifier. A multivariate model assessing FS recurrence provided a difference (pFWE < 0.05) with a separating sensitivity/specificity of 72%/69%. Iron deficiency, height percentile, and age were significant FS risk factors. In addition, height percentile and hemoglobin concentration were linked to FS recurrence. Novel multivariate models utilizing blood iron status and demographic variables predicted FS risk and recurrence among infants and young children with fever.

• MeSH
• deficit železa * MeSH
• febrilní křeče * diagnóza   etiologie   MeSH
• horečka komplikace   MeSH
• kojenec MeSH
• lidé MeSH
• předškolní dítě MeSH
• studie případů a kontrol MeSH
• železo MeSH
• Check Tag
• kojenec MeSH
• lidé MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• železo MeSH

OBJECTIVE: Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. METHODS: This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0-2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location-specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. RESULTS: For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature. SIGNIFICANCE: Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

• Klíčová slova
• circadian, forecasting, seizure, sleep, weather,
• MeSH
• Bayesova věta MeSH
• časové faktory * MeSH
• dospělí MeSH
• elektrokortikografie MeSH
• epilepsie patofyziologie   MeSH
• hodnocení rizik MeSH
• lidé středního věku MeSH
• lidé MeSH
• počasí * MeSH
• rizikové faktory MeSH
• spánek * MeSH
• záchvaty epidemiologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy Assist Device) in a pseudo-prospective mode using iEEG from four canines with naturally occurring epilepsy. APPROACH: The system was trained and tested on 75 seizures collected over 1608 d utilizing a genetic algorithm to optimize forecasting hyper-parameters (prediction horizon (PH), median filter window length, and probability threshold) for each subject-specific seizure forecasting model. The trained CNN models were deployed on a hand-held tablet computer and tested on testing iEEG datasets from four canines. The results from the iEEG testing datasets were compared with Monte Carlo simulations using a Poisson random predictor with equal time in warning to evaluate seizure forecasting performance. MAIN RESULTS: The results show the CNN models forecasted seizures at rates significantly above chance in all four dogs (p < 0.01, with mean 0.79 sensitivity and 18% time in warning). The deep learning method presented here surpassed the performance of previously reported methods using computationally expensive features with standard machine learning methods like logistic regression and support vector machine classifiers. SIGNIFICANCE: Our findings principally support the feasibility of deploying trained CNN models on a hand-held computational device (Mayo Epilepsy Assist Device) that analyzes streaming iEEG data for real-time seizure forecasting.

• MeSH
• deep learning * MeSH
• elektrokortikografie přístrojové vybavení   metody   MeSH
• epilepsie diagnóza   patofyziologie   MeSH
• implantované elektrody * MeSH
• předpověď MeSH
• psi MeSH
• záchvaty diagnóza   patofyziologie   MeSH
• zvířata MeSH
• Check Tag
• psi MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

IMPORTANCE: Stereoelectroencephalography (SEEG) has become the criterion standard in case of inconclusive noninvasive presurgical epilepsy workup. However, up to 40% of patients are subsequently not offered surgery because the seizure-onset zone is less focal than expected or cannot be identified. OBJECTIVE: To predict focality of the seizure-onset zone in SEEG, the 5-point 5-SENSE score was developed and validated. DESIGN, SETTING, AND PARTICIPANTS: This was a monocentric cohort study for score development followed by multicenter validation with patient selection intervals between February 2002 to October 2018 and May 2002 to December 2019. The minimum follow-up period was 1 year. Patients with drug-resistant epilepsy undergoing SEEG at the Montreal Neurological Institute were analyzed to identify a focal seizure-onset zone. Selection criteria were 2 or more seizures in electroencephalography and availability of complete neuropsychological and neuroimaging data sets. For validation, patients from 9 epilepsy centers meeting these criteria were included. Analysis took place between May and July 2021. MAIN OUTCOMES AND MEASURES: Based on SEEG, patients were grouped as focal and nonfocal seizure-onset zone. Demographic, clinical, electroencephalography, neuroimaging, and neuropsychology data were analyzed, and a multiple logistic regression model for developing a score to predict SEEG focality was created and validated in an independent sample. RESULTS: A total of 128 patients (57 women [44.5%]; median [range] age, 31 [13-58] years) were analyzed for score development and 207 patients (97 women [46.9%]; median [range] age, 32 [16-70] years) were analyzed for validation. The score comprised the following 5 predictive variables: focal lesion on structural magnetic resonance imaging, absence of bilateral independent spikes in scalp electroencephalography, localizing neuropsychological deficit, strongly localizing semiology, and regional ictal scalp electroencephalography onset. The 5-SENSE score had an optimal mean (SD) probability cutoff for identifying a focal seizure-onset zone of 37.6 (3.5). Area under the curve, specificity, and sensitivity were 0.83, 76.3% (95% CI, 66.7-85.8), and 83.3% (95% CI, 72.30-94.1), respectively. Validation showed 76.0% (95% CI, 67.5-84.0) specificity and 52.3% (95% CI, 43.0-61.5) sensitivity. CONCLUSIONS AND RELEVANCE: High specificity in score development and validation confirms that the 5-SENSE score predicts patients where SEEG is unlikely to identify a focal seizure-onset zone. It is a simple and useful tool for assisting clinicians to reduce unnecessary invasive diagnostic burden on patients and overutilization of limited health care resources.

• MeSH
• elektroencefalografie * MeSH
• epilepsie diagnóza   chirurgie   MeSH
• kohortové studie MeSH
• lidé MeSH
• předoperační péče MeSH
• průzkumy a dotazníky normy   MeSH
• záchvaty diagnóza   chirurgie   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• validační studie MeSH

OBJECTIVE: We performed a retrospective study to determine the different types of seizure onset patterns (SOP) in invasive EEG (IEEG) in patients with temporal lobe epilepsy (TLE). METHODS: We analyzed a group of 51 patients (158 seizures) with TLE who underwent IEEG. We analyzed the dominant frequency during the first 3s after the onset of ictal activity. The cut-off value for distinguishing between fast and slow frequencies was 8 Hz. We defined three types of SOPs: (1) fast ictal activity (FIA) - frequency ≥8 Hz; (2) slow ictal activity (SIA) - frequency <8 Hz; and (3) attenuation of background activity (AT) - no clear-cut rhythmic activity during the first 3s associated with changes of IEEG signal (increase of frequency, decrease of amplitude). We tried to find the relationship between different SOP types and surgery outcome, histopathological findings, and SOZ localization. RESULTS: The most frequent SOP was FIA, which was present in 67% of patients. More patients with FIA were classified postoperatively as Engel I than those with SIA and AT (85% vs. 31% vs. 0) (P < 0.001). There were no statistically significant differences in the type of SOP, in the histopathological findings, or in the SOZ localization. CONCLUSION: In patients with refractory TLE, seizure onset frequencies ≥8 Hz during the first 3s of ictal activity are associated with a better surgical outcome than frequencies <8 Hz. SIGNIFICANCE: Our study suggests that very early seizure onset frequencies in IEEG in patients with TLE could be the independent predictive factor for their outcome, regardless of the localization and etiology.

• MeSH
• dospělí MeSH
• elektroencefalografie * MeSH
• epilepsie temporálního laloku patofyziologie   chirurgie   MeSH
• fixace tkání MeSH
• funkční lateralita fyziologie   MeSH
• implantované elektrody MeSH
• interpretace statistických dat MeSH
• jednofotonová emisní výpočetní tomografie MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mladiství MeSH
• mladý dospělý MeSH
• neurochirurgické výkony MeSH
• počítačové zpracování obrazu MeSH
• pozitronová emisní tomografie MeSH
• výsledek terapie MeSH
• záchvaty patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

OBJECTIVE: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. METHODS: We propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. RESULTS: In this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p<0.001, n=6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs. CONCLUSION: Hierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis. SIGNIFICANCE: The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management.

• Klíčová slova
• Hierarchical clustering, Machine learning, Seizure forecasting,
• Publikační typ
• časopisecké články MeSH

Recent findings suggest that neural complexity reflecting a number of independent processes in the brain may characterize typical changes during epileptic seizures and may enable to describe preictal dynamics. With respect to previously reported findings suggesting specific changes in neural complexity during preictal period, we have used measure of pointwise correlation dimension (PD2) as a sensitive indicator of nonstationary changes in complexity of the electroencephalogram (EEG) signal. Although this measure of complexity in epileptic patients was previously reported by Feucht et al (Applications of correlation dimension and pointwise dimension for non-linear topographical analysis of focal onset seizures. Med Biol Comput. 1999;37:208-217), it was not used to study changes in preictal dynamics. With this aim to study preictal changes of EEG complexity, we have examined signals from 11 multicontact depth (intracerebral) EEG electrodes located in 108 cortical and subcortical brain sites, and from 3 scalp EEG electrodes in a patient with intractable epilepsy, who underwent preoperative evaluation before epilepsy surgery. From those 108 EEG contacts, records related to 44 electrode contacts implanted into lesional structures and white matter were not included into the experimental analysis.The results show that in comparison to interictal period (at about 8-6 minutes before seizure onset), there was a statistically significant decrease in PD2 complexity in the preictal period at about 2 minutes before seizure onset in all 64 intracranial channels localized in various brain sites that were included into the analysis and in 3 scalp EEG channels as well. Presented results suggest that using PD2 in EEG analysis may have significant implications for research of preictal dynamics and prediction of epileptic seizures.

• MeSH
• elektroencefalografie * metody   MeSH
• epilepsie patofyziologie   MeSH
• lidé MeSH
• mladý dospělý MeSH
• Check Tag
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• kazuistiky MeSH
• práce podpořená grantem MeSH

OBJECTIVE: To compare persons with epilepsy (PWE) to those with psychogenic non-epileptic seizures (PNES) on measures of depression, anxiety, and alexithymia subscales (i.e., difficulty identifying emotions, difficulty describing emotions, and external-oriented thinking). MATERIAL AND METHODS: In this retrospective study, 235 epilepsy patients and 90 patients with PNES were evaluated between 2012 and 2020 at the Northeast Regional Epilepsy Group. These patients had completed the Toronto Alexithymia Scale (TAS-20), The Center for Epidemiologic Studies - Depression Scale (CES-D) and The State-Trait Anxiety Inventory (STAI). Background information was collected regarding work/student/disability status at the time of the evaluation history of psychiatric diagnosis; psychological trauma; and involvement in psychotherapy either at the time of the evaluation or prior. RESULTS: Significant differences between PWEs and those with PNES were found not only in historical data (e.g., Psychiatric History, History of Trauma, and History of Therapy) (p < .001) but also on measures of Depression (p = .002) and Anxiety (p < .001). ANOVA analysis also revealed significant differences in the distribution of the TAS-Total score, TAS-Describing emotions, and TAS-Identifying emotions. Using logistic regression (stepwise model) the optimal set of predictors for a differential diagnosis of epilepsy and PNES was combination of TAS-Identifying emotions score, history of psychological trauma, and history of therapy. The accuracy of the prediction was determined to be 80.2 %. CONCLUSIONS: Although higher alexithymia rates are present in PNES and PWEs, clinicians may find a combination of TAS-Identifying Emotion score, history of trauma, and history of psychotherapy useful in supporting a differential diagnosis. Also, a subgroup may exist among those with PNES with high levels of alexithymia, depression, and anxiety that may require a different treatment approach focused on addressing difficulties in identifying and describing their emotions and their other symptomatology.

• Klíčová slova
• Alexithymia, Anxiety, Depression, Epileptic seizures, Functional dissociative seizures, Functional neurological disorders,
• MeSH
• afekt fyziologie   MeSH
• afektivní symptomy * diagnóza   etiologie   MeSH
• deprese diagnóza   etiologie   MeSH
• dospělí MeSH
• epilepsie * psychologie   komplikace   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• psychiatrické posuzovací škály MeSH
• retrospektivní studie MeSH
• úzkost * diagnóza   etiologie   MeSH
• záchvaty * psychologie   diagnóza   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH