Objective.The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity.Approach.We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity.Main results.The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75,p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers.Significance.We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.
- MeSH
- dítě MeSH
- dospělí MeSH
- elektroencefalografie * metody MeSH
- epilepsie * chirurgie diagnóza patofyziologie MeSH
- klinické rozhodování * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- reprodukovatelnost výsledků MeSH
- stereotaktické techniky MeSH
- záchvaty diagnóza chirurgie patofyziologie MeSH
- Check Tag
- dítě MeSH
- 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: Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. METHODS: In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. RESULTS: The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. SIGNIFICANCE: Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.
- MeSH
- dítě MeSH
- dospělí MeSH
- elektroencefalografie * metody MeSH
- epilepsie chirurgie patofyziologie diagnóza MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- refrakterní epilepsie chirurgie patofyziologie diagnóza MeSH
- stereotaktické techniky MeSH
- výsledek terapie MeSH
- Check Tag
- dítě MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.
- MeSH
- algoritmy MeSH
- elektroencefalografie metody MeSH
- elektrokortikografie metody MeSH
- epilepsie * patofyziologie diagnóza MeSH
- hipokampus patofyziologie fyziologie MeSH
- lidé MeSH
- modely neurologické MeSH
- počítačové zpracování signálu MeSH
- výpočetní biologie metody MeSH
- záchvaty patofyziologie diagnóza MeSH
- Check Tag
- 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
OBJECTIVE: This study was undertaken to develop a standardized grading system based on expert consensus for evaluating the level of confidence in the localization of the epileptogenic zone (EZ) as reported in published studies, to harmonize and facilitate systematic reviews in the field of epilepsy surgery. METHODS: We conducted a Delphi study involving 22 experts from 18 countries, who were asked to rate their level of confidence in the localization of the EZ for various theoretical clinical scenarios, using different scales. Information provided in these scenarios included one or several of the following data: magnetic resonance imaging (MRI) findings, invasive electroencephalography summary, and postoperative seizure outcome. RESULTS: The first explorative phase showed an overall interrater agreement of .347, pointing to large heterogeneity among experts' assessments, with only 17% of the 42 proposed scenarios associated with a substantial level of agreement. A majority showed preferences for the simpler scale and single-item scenarios. The successive Delphi voting phases resulted in a majority consensus across experts, with more than two thirds of respondents agreeing on the rating of each of the tested single-item scenarios. High or very high levels of confidence were ascribed to patients with either an Engel class I or class IA postoperative seizure outcome, a well-delineated EZ according to all available invasive EEG (iEEG) data, or a well-delineated focal epileptogenic lesion on MRI. MRI signs of hippocampal sclerosis or atrophy were associated with a moderate level of confidence, whereas a low level was ascribed to other MRI findings, a poorly delineated EZ according to iEEG data, or an Engel class II-IV postoperative seizure outcome. SIGNIFICANCE: The proposed grading system, based on an expert consensus, provides a simple framework to rate the level of confidence in the EZ reported in published studies in a structured and harmonized way, offering an opportunity to facilitate and increase the quality of systematic reviews and guidelines in the field of epilepsy surgery.
OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). METHODS: We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). RESULTS: On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. CONCLUSIONS: The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. SIGNIFICANCE: Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.
- MeSH
- časové faktory MeSH
- dospělí MeSH
- elektroencefalografie * metody MeSH
- elektrokortikografie metody normy MeSH
- epilepsie * patofyziologie diagnóza MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mozek patofyziologie MeSH
- stadia spánku fyziologie MeSH
- strojové učení * 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
- MeSH
- antikonvulziva aplikace a dávkování MeSH
- cílená molekulární terapie metody trendy MeSH
- diagnostické techniky neurologické trendy MeSH
- elektrická stimulace přístrojové vybavení MeSH
- elektroencefalografie trendy MeSH
- epilepsie * diagnóza terapie MeSH
- individualizovaná medicína metody trendy MeSH
- lidé MeSH
- neurologie * trendy MeSH
- přehodnocení terapeutických indikací léčivého přípravku trendy MeSH
- transplantace trendy MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
OBJECTIVE: Developmental and epileptic encephalopathies (DEEs) are a group of severe, early-onset epilepsies characterised by refractory seizures, developmental delay, or regression and generally poor prognosis. DEE are now known to have an identifiable molecular genetic basis and are usually examined using a gene panel. However, for many patients, the genetic cause has still not been identified. The aims of this study were to identify causal variants for DEE in patients for whom the previous examination with a gene panel did not determine their genetic diagnosis. It also aims for a detailed description and broadening of the phenotypic spectrum of several rare DEEs. METHODS: In the last five years (2015-2020), 141 patients from all over the Czech Republic were referred to our department for genetic testing in association with their diagnosis of epilepsy. All patients underwent custom-designed gene panel testing prior to enrolment into the study, and their results were inconclusive. We opted for whole exome sequencing (WES) to identify the cause of their disorder. If a causal or potentially causal variant was identified, we performed a detailed clinical evaluation and phenotype-genotype correlation study to better describe the specific rare subtypes. RESULTS: Explanatory causative variants were detected in 20 patients (14%), likely pathogenic variants that explain the epilepsy in 5 patients (3.5%) and likely pathogenic variants that do not fully explain the epilepsy in 11 patients (7.5%), and variants in candidate genes in 4 patients (3%). Variants were mostly de novo 29/40 (72.5%). SIGNIFICANCE: WES enables us to identify the cause of the disease in additional patients, even after gene panel testing. It is very important to perform a WES in DEE patients as soon as possible, since it will spare the patients and their families many years of a diagnostic odyssey. In particular, patients with rare epilepsies might significantly benefit from this approach, and we propose using WES as a new standard in the diagnosis of DEE instead of targeted gene panel testing.
- MeSH
- epilepsie generalizovaná * genetika MeSH
- epilepsie * diagnóza genetika MeSH
- fenotyp MeSH
- genetické asociační studie MeSH
- genetické testování MeSH
- lidé MeSH
- sekvenování exomu MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
