Computer vision for automated seizure detection and classification: A systematic review.

Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.

Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.

Semantic Similarity Analysis Reveals Robust Gene-Disease Relationships in Developmental and Epileptic Encephalopathies.

More than 100 genetic etiologies have been identified in developmental and epileptic encephalopathies (DEEs), but correlating genetic findings with clinical features at scale has remained a hurdle because of a lack of frameworks for analyzing heterogenous clinical data. Here, we analyzed 31,742 Human Phenotype Ontology (HPO) terms in 846 individuals with existing whole-exome trio data and assessed associated clinical features and phenotypic relatedness by using HPO-based semantic similarity analysis for individuals with de novo variants in the same gene. Gene-specific phenotypic signatures included associations of SCN1A with "complex febrile seizures" (HP: 0011172; p = 2.1 × 10-5) and "focal clonic seizures" (HP: 0002266; p = 8.9 × 10-6), STXBP1 with "absent speech" (HP: 0001344; p = 1.3 × 10-11), and SLC6A1 with "EEG with generalized slow activity" (HP: 0010845; p = 0.018). Of 41 genes with de novo variants in two or more individuals, 11 genes showed significant phenotypic similarity, including SCN1A (n = 16, p < 0.0001), STXBP1 (n = 14, p = 0.0021), and KCNB1 (n = 6, p = 0.011). Including genetic and phenotypic data of control subjects increased phenotypic similarity for all genetic etiologies, whereas the probability of observing de novo variants decreased, emphasizing the conceptual differences between semantic similarity analysis and approaches based on the expected number of de novo events. We demonstrate that HPO-based phenotype analysis captures unique profiles for distinct genetic etiologies, reflecting the breadth of the phenotypic spectrum in genetic epilepsies. Semantic similarity can be used to generate statistical evidence for disease causation analogous to the traditional approach of primarily defining disease entities through similar clinical features.

Efficacy of cannabidiol in convulsive and nonconvulsive seizure types associated with treatment-resistant epilepsies in the Expanded Access Program.

The cannabidiol (CBD) Expanded Access Program (EAP), initiated in 2014, provided CBD (Epidiolex) to patients with treatment-resistant epilepsy (TRE). In the final pooled analysis of 892 patients treated through January 2019 (median exposure = 694 days), CBD treatment was associated with a 46%-66% reduction in median monthly total (convulsive plus nonconvulsive) seizure frequency. CBD was well tolerated, and adverse events were consistent with previous findings. We used pooled EAP data to investigate the effectiveness of add-on CBD therapy for individual convulsive seizure types (clonic, tonic, tonic-clonic, atonic, focal to bilateral tonic-clonic), nonconvulsive seizure types (focal with and without impaired consciousness, absence [typical and atypical], myoclonic, myoclonic absence), and epileptic spasms. CBD treatment was associated with a reduction in the frequency of convulsive seizure types (median percentage reduction = 47%-100%), and nonconvulsive seizure types and epileptic spasms (median percentage reduction = 50%-100%) across visit intervals through 144 weeks of treatment. Approximately 50% of patients had ≥50% reduction in convulsive and nonconvulsive seizure types and epileptic spasms at nearly all intervals. These results show a favorable effect of long-term CBD use in patients with TRE, who may experience various convulsive and nonconvulsive seizure types. Future controlled trials are needed to confirm these findings.

Does the type of seizure influence heart rate variability changes?

OBJECTIVE: Heart rate variability (HRV), an index of the autonomic cardiac activity, is decreased in patients with epilepsy, and a low HRV is associated with a higher risk of sudden death. Generalized tonic-clonic seizures are one of the most consistent risk factors for SUDEP, but the influence (and relative risk) of each type of seizure on cardiac function is still unknown. Our objective was to assess the impact of the type of seizure (focal to bilateral tonic-clonic seizure - FBTCS - versus non-FBTCS) on periictal HRV, in a group of patients with refractory epilepsy and both types of seizures. METHODS: We performed a 48-hour Holter recording on 121 patients consecutively admitted to our Epilepsy Monitoring Unit. We only included patients with both FBTCS and non-FBTCS on the Holter recording and selected the first seizure of each type to analyze. To evaluate HRV parameters (AVNN, SDNN, RMSSD, pNN20, LF, HF, and LF/HF), we chose 5-min epochs pre- and postictally. RESULTS: We included 14 patients, with a median age of 36 (min-max, 16-55) years and 64% were female. Thirty-six percent had cardiovascular risk factors, but no previously known cardiac disease. In the preictal period, there were no statistically significant differences in HRV parameters, between FBTCS and non-FBTCS. In the postictal period, AVNN, RMSSD, pNN20, LF, and HF were significantly lower, and LF/HF and HR were significantly higher in FBTCS. From preictal to postictal periods, FBTCS elicited a statistically significant rise in HR and LF/HF, and a statistically significant fall in AVNN, RMSSD, pNN20, and HF. Non-FBTCS only caused statistically significant changes in HR (decrease) and AVNN (increase). SIGNIFICANCE/CONCLUSION: This work emphasizes the greater effect of FBTCS in autonomic cardiac function in patients with refractory epilepsy, compared to other types of seizures, with a significant reduction in vagal tonus, which may be associated with an increased risk of SUDEP.

The ILAE classification of seizures and the epilepsies: Modification for seizures in the neonate. Position paper by the ILAE Task Force on Neonatal Seizures.

Seizures are the most common neurological emergency in the neonatal period and in contrast to those in infancy and childhood, are often provoked seizures with an acute cause and may be electrographic-only. Hence, neonatal seizures may not fit easily into classification schemes for seizures and epilepsies primarily developed for older children and adults. A Neonatal Seizures Task Force was established by the International League Against Epilepsy (ILAE) to develop a modification of the 2017 ILAE Classification of Seizures and Epilepsies, relevant to neonates. The neonatal classification framework emphasizes the role of electroencephalography (EEG) in the diagnosis of seizures in the neonate and includes a classification of seizure types relevant to this age group. The seizure type is determined by the predominant clinical feature. Many neonatal seizures are electrographic-only with no evident clinical features; therefore, these are included in the proposed classification. Clinical events without an EEG correlate are not included. Because seizures in the neonatal period have been shown to have a focal onset, a division into focal and generalized is unnecessary. Seizures can have a motor (automatisms, clonic, epileptic spasms, myoclonic, tonic), non-motor (autonomic, behavior arrest), or sequential presentation. The classification allows the user to choose the level of detail when classifying seizures in this age group.

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable.

OBJECTIVE: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics. METHODS: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort's data were provided in a different format and harmonized using the two versions of the HPO. RESULTS: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals' seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures. SIGNIFICANCE: We have generated a detailed contemporary conceptual map for harmonization of clinical seizure data, implemented in the official 2020-12-07 HPO release and freely available at hpo.jax.org. This will help to overcome the phenotypic bottleneck in genomics, facilitate reuse of valuable data, and ultimately improve diagnostics and precision treatment of the epilepsies.

Domino-like transient dynamics at seizure onset in epilepsy.

The International League Against Epilepsy (ILAE) groups seizures into "focal", "generalized" and "unknown" based on whether the seizure onset is confined to a brain region in one hemisphere, arises in several brain region simultaneously, or is not known, respectively. This separation fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset and even less so for the properties of the brain networks generating them. We consider three different patterns of domino-like seizure onset in Idiopathic Generalized Epilepsy (IGE) and present a novel approach to classification of seizures. To understand how these patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which currently is unknown. We investigate this interplay here in the framework of domino-like recruitment across a network. In particular, we use a phenomenological model of seizure onset with heterogeneous coupling and node properties, and show that in combination they generate a range of domino-like onset patterns observed in the IGE seizures. We further explore the individual contribution of heterogeneous node dynamics and coupling by interpreting in-vitro experimental data in which the speed of onset can be chemically modulated. This work contributes to a better understanding of possible drivers for the spatiotemporal patterns observed at seizure onset and may ultimately contribute to a more personalized approach to classification of seizure types in clinical practice.

Recent Advances in the Diagnosis and Treatment of Neonatal Seizures.

Seizures are the most common neurological emergency in the neonates, and this age group has the highest incidence of seizures compared with any other period of life. The author provides a narrative review of recent advances in the genetics of neonatal epilepsies, new neonatal seizure classification system, diagnostics, and treatment of neonatal seizures based on a comprehensive literature review (MEDLINE using PubMED and OvidSP vendors with appropriate keywords to incorporate recent evidence), personal practice, and experience. Knowledge regarding various systemic and postzygotic genetic mutations responsible for neonatal epilepsy has been exploded in recent times, as well as better delineation of clinical phenotypes associated with rare neonatal epilepsies. An International League Against Epilepsy task force on neonatal seizure has proposed a new neonatal seizure classification system and also evaluated the specificity of semiological features related to particular etiology. Although continuous video electroencephalogram (EEG) is the gold standard for monitoring neonatal seizures, amplitude-integrated EEGs have gained significant popularity in resource-limited settings. There is tremendous progress in the automated seizure detection algorithm, including the availability of a fully convolutional neural network using artificial machine learning (deep learning). There is a substantial need for ongoing research and clinical trials to understand optimal medication selection (first line, second line, and third line) for neonatal seizures, treatment duration of antiepileptic drugs after cessation of seizures, and strategies to improve neuromorbidities such as cerebral palsy, epilepsy, and developmental impairments. Although in recent times, levetiracetam use has been significantly increased for neonatal seizures, a multicenter, randomized, blinded, controlled phase IIb trial confirmed the superiority of phenobarbital over levetiracetam in the acute suppression of neonatal seizures. While there is no single best choice available for the management of neonatal seizures, institutional guidelines should be formed based on a consensus of local experts to mitigate wide variability in the treatment and to facilitate early diagnosis and treatment.

Semiologic subgroups of insulo-opercular seizures based on connectional architecture atlas.

OBJECTIVE: Insulo-opercular seizures are characterized by diverse semiology, related to the insula's multiple functional roles and extensive connectivity. We aimed to identify semiologic subgroups and correlate these with insulo-opercular subregions based on connectional architecture. METHODS: We retrospectively collected a large series of 37 patients with insulo-opercular seizures explored by stereoelectroencephalography (SEEG) from three epilepsy centers. A new human brain atlas (Brainnetome Atlas, BNA) based on both anatomic and functional connections was employed to segment insulo-opercular cortex. Semiology and SEEG changes were carefully reviewed and quantified. Principal component analysis and cluster analysis were used to correlate semiologic characteristics with insulo-opercular subregions. RESULTS: Four main semiologic subgroups were identified, organized along an anteroventral to posterodorsal axis based on BNA. Group 1 was characterized by epigastric sensation and/or integrated gestural motor behaviors with or without feelings of fear or rage, involving the anteroventral insular regions and mesial temporal lobes. Group 2 was characterized by auditory sensations and symmetric proximal/axial tonic signs involving the posteroventral temporal operculum. The characteristics of group 3 were orofacial and laryngeal signs, involving the intermediate insulo-opercular regions. The features of group 4 were somatosensory signs followed by nonintegrated gestural motor behaviors and/or asymmetric tonic signs involving the posterodorsal insulo-opercular regions with propagation to the mesial frontal lobes. Thus anteroventral seizure organizations predominantly showed limbic system semiology, whereas more posterodorsal regions were associated with semiology involving mainly the sensorimotor system. Subjective symptoms proved to be particularly discriminating factors. SIGNIFICANCE: Insulo-opercular seizures can be categorized in terms of clinical semiology and correlate with connectional architecture subregions along an anteroventral-posterodorsal axis in line with the cytoarchitectonic gradient rather than the gyral anatomy of the insula cortex. This provides new insights into facilitating differential diagnosis and presurgical localization but also highlights the importance of considering connectional architecture in determining neural correlates of complex semiologic patterns.

Epilepsy-Definition, Classification, Pathophysiology, and Epidemiology.

Seizures affect the lives of 10% of the global population and result in epilepsy in 1 to 2% of people around the world. Current knowledge about etiology, diagnosis, and treatments for epilepsy is constantly evolving. As more is learned, appropriate and updated definitions and classification systems for seizures and epilepsy are of the utmost importance. Without proper definitions and classification, many individuals will be improperly diagnosed and incorrectly treated. It is also essential for research purposes to have proper definitions, so that appropriate populations can be identified and studied. Imprecise definitions, failure to use accepted terminology, or inappropriate use of terminology hamper our ability to study and advance the field of epilepsy. This article begins by discussing the pathophysiology and epidemiology of epilepsy, and then covers the accepted contemporary definitions and classifications of seizures and epilepsies.

Evaluation of interobserver variability in application of the new neonatal seizure classification proposed by the ILAE Task Force.

OBJECTIVE: Clinical identification of neonatal seizures (NS) remains challenging. The International League Against Epilepsy (ILAE) Task Force on Neonatal Seizures has proposed a new classification of NS, based on the 2017 ILAE seizure classification. One of the key points of this proposed NS classification is that seizure types should be determined by the "predominant" clinical feature. However, when the definition of "predominant" is uncertain, interobserver variability may arise. METHODS: We asked 49 health professionals to classify 21 NS video-electroencephalogram (EEG) recordings using the proposed 9 seizure types. RESULTS: The degree of agreement among participants was low, and agreement was weak among experts in neonatal neurology. Among experts, the rate of agreement was <50% for 2 NS. This disagreement was related to differences in the interpretation of "predominant features." Although interobserver variability was present among users of the new NS classification, the reproducibility of the NS classification was satisfactory. CONCLUSION: Education designed to foster consistent application of the standards for NS will be important for reducing interobserver variability and expanding the use of the new NS classification.

Clinical characteristics of video-EEG patients: Limited utility of prolonging VEEG study duration beyond 5 days for spell classification.

OBJECTIVE: The objective of the study was to identify the probability of establishing a diagnosis based on the duration of video-electroencephalogram (VEEG) monitoring. Additional aims were to determine whether there is a relationship between clinical characteristics of epilepsy monitoring unit (EMU) patients and VEEG results. METHODS: We studied EMU length of stay and assessed the utility of prolonging studies in patients who had not yet received a diagnosis. Clinical characteristics in 212 consecutive patients admitted for scalp VEEG monitoring were recorded. We collected data including reason for admission, frequency of seizures/spells, gender, age, age at seizure onset, handedness, family history, history of neurologic disease, current and past antiepileptic drugs (AEDs), and prior work-up. Subjects were categorized into five diagnostic groups: epileptic seizures (Epi), nonepileptic events (NEE), mixed epileptic and nonepileptic events (Mixed), nonepileptic events from a physiologic cause (NEEP), and nondiagnostic study without results recorded (ND). RESULTS: The most diagnoses were made during the first day of admission (45%), and by day 3, 82 patients remained without a diagnosis. On day 3, 25 of these patients (33%) received a diagnosis, on day 4, seven (22%) additional patients received a diagnosis, on day 5, 5 patients (35%) received a diagnosis, and by day 6, only one additional patient (11%) was given a diagnosis. Significant differences were found between diagnostic groups for admission reason, duration of EMU stay, age at seizure onset, duration of epilepsy, seizure frequency, and number of current and previously tried AEDs. CONCLUSIONS: Our findings show that the majority of patients are diagnosed in the first 2 days of admission, and we found a limited benefit of prolonging nonsurgical inpatient VEEG studies beyond 5 days for spell/seizure classification. Additionally, patient demographics were significantly different for patients depending on VEEG diagnosis, which can help predict the utility of completing VEEG studies in individual patients.

An investigation into the preferred terminology for functional seizures.

There is considerable debate in the literature regarding what to call functional seizures, with terms such as pseudoseizures, nonepileptic attack disorder (NEAD), and dissociative seizures being used. Provision of an accurate diagnosis and coherent explanation is a vital first step in the management of functional seizures and can result in cessation or reduced frequency for some individuals. This study investigated preferences for and offensiveness of terms used to describe functional seizures, and expectations for recovery with psychological treatment. A sample of 87 healthy adults completed an online survey, in which eight different diagnostic terms were ranked in order of preference (1 - most preferred, 8 - least preferred): functional nonepileptic attacks (FNEA), dissociative seizures, functional seizures, psychogenic seizures, NEAD, pseudoseizures, conversion disorder, and hysteria. Replicating Stone and colleagues protocol, each term was investigated for five connotations. Offense scores were calculated from the number of participants who selected 'yes' to at least one of the negative connotations ('Putting it on', 'Mad', and 'Imagining Symptoms'). Expectations about the possibility of recovering through medical or psychological treatment were also recorded. Functional nonepileptic attack was ranked the highest preferred term with dissociative seizures and functional seizures closely following. Nonepileptic attack disorder was the least offensive term, with FNEA and functional seizures joint second. Unsurprisingly, the three least preferred terms were also the most offensive: pseudoseizures, conversion disorder, and hysteria. Expectations of nonrecovery from psychological treatment were lowest for terms implicating a psychological cause: pseudoseizures, dissociative seizures, psychogenic seizures, and hysteria. The results suggest that either the terms FNEA or functional seizures should be adopted by healthcare professionals and patients, as they are the most preferred, least offensive, and expectations for nonrecovery with psychological treatment were moderate compared with the other terms. Limitations and areas for future research are discussed.

Terminology for psychogenic nonepileptic seizures: Making the case for "functional seizures".

PURPOSE: The purpose of the study was to review the literature on the terminologies for psychogenic nonepileptic seizures (PNES) and make a proposal on the terminology of this condition. This proposal reflects the authors' own opinions. METHODS: We systematically searched MEDLINE (accessed from PubMed) and EMBASE from inception to October 10, 2019 for articles written in English with a main focus on PNES (with or without discussion of other functional neurological disorders) and which either proposed or discussed the accuracy or appropriateness of PNES terminologies. RESULTS: The search strategy reported above yielded 757 articles; 30 articles were eventually included, which were generally of low quality. "Functional seizures" (FS) appeared to be an acceptable terminology to name this condition from the perspective of patients. In addition, FS is a term that is relatively popular with clinicians. CONCLUSION: From the available evidence, FS meets more of the criteria proposed for an acceptable label than other popular terms in the field. While the term FS is neutral with regard to etiology and pathology (particularly regarding whether psychological or not), other terms such as "dissociative", "conversion", or "psychogenic" seizures are not. In addition, FS can potentially facilitate multidisciplinary (physical and psychological) management more than other terms. Adopting a universally accepted terminology to describe this disorder could standardize our approach to the illness and facilitate communication between healthcare professionals, patients, their families, carers, and the wider public.

Critique of the 2017 epileptic seizure and epilepsy classifications.

This article critiques the International League Against Epilepsy (ILAE) 2015-2017 classifications of epilepsy, epileptic seizures, and status epilepticus. It points out the following shortcomings of the ILAE classifications: (1) they mix semiological terms with epileptogenic zone terminology; (2) simple and widely accepted terminology has been replaced by complex terminology containing less information; (3) seizure evolution cannot be described in any detail; (4) in the four-level epilepsy classification, level two (epilepsy category) overlaps almost 100% with diagnostic level one (seizure type); and (5) the design of different classifications with distinct frameworks for newborns, adults, and patients in status epilepticus is confusing. The authors stress the importance of validating the new ILAE classifications and feel that the decision of Epilepsia to accept only manuscripts that use the ILAE classifications is premature and regrettable.

Interictal coupling of HFOs and slow oscillations predicts the seizure-onset pattern in mesiotemporal lobe epilepsy.

OBJECTIVE: Low-voltage fast activity (LVF) and low-frequency high-amplitude periodic spiking (PS) are the two most common seizure-onset patterns in mesiotemporal lobe epilepsy, with different underlying mechanisms, pathology, and postsurgical outcome. The present work aims to investigate whether specific coupling patterns of high-frequency oscillations (HFOs >80 Hz) and low-frequency waves in the interictal period may distinguish these two patterns, and also seizure-onset zone (SOZ) from non-SOZ as a secondary aim. METHODS: We used intracranial electroencephalography (iEEG) data (during non-rapid eye movement [NREM] sleep) of 18 patients with either LVF or PS seizure-onset patterns. We investigated the interaction between HFOs (ripples: 80-250 Hz and fast ripples: >250 Hz) and slow oscillations (slow-delta, delta, and theta waves). We compared classic features (amplitude, duration, frequency, and power) and phase of coupling between HFOs and slower oscillations inside and outside the SOZ. We then used these features to classify HFOs and subsequently patients into LVF and PS groups. RESULTS: Ripples in the LVF group had significantly longer duration, lower frequency, and higher amplitude than in the PS group. The phase of slow oscillations at which HFOs occur is different between the LVF and PS HFOs (LVF, mostly at the peak or the transition of peak to trough; PS, mostly during the transition of trough to peak). HFOs associated with theta waves best discriminate seizure-onset patterns. The coupling phase improves the classification of HFOs and patients to either LVF or PS groups, and also the classification of HFOs in SOZ and non-SOZ. SIGNIFICANCE: The phase of coupling of HFOs and low-frequency waves may help to not only identify the SOZ, but also to classify patients with different types of seizure-onset patterns. It likely reflects that different disease processes are involved in these patterns during the interictal period.

Comparing two classification schemes for seizures and epilepsy in rural China.

BACKGROUND AND PURPOSE: The International League against Epilepsy (ILAE) updated the classifications of seizures and epilepsies in 2017. The 2017 classifications were compared with the 1980s classifications in rural China. METHODS: People with epilepsy receiving treatment under the National Epilepsy Control Programme were recruited from rural areas in China. Their seizures and epileptic syndrome were classified using the 1980s ILAE classification system and then re-classified according to the 2017 system. Differences in seizure, epilepsy and aetiology classifications were identified. RESULTS: A total of 597 individuals (58% males, aged 6-78 years) were included. Amongst them 535 (90%) had a single seizure type, 57 (9.55%) had two types and five (0.84%) had three. There was complete agreement between the 1981 and 2017 classifications for the 525 individuals with focal seizures. Seizures originally classified as generalized in 10 of 65 individuals were re-classified as unknown in the 2017 classification. Compared to the 1980s classifications, the proportion of individuals with unknown seizures and unknown epilepsy increased from 1.2% (7/597) to 2.8% (17/597, P = 0.002), and unknown aetiology increased from 32% (189/597: 182 cryptogenic and seven unclassified) to 39% (230/597; P < 0.001) in the 2017 classifications. CONCLUSIONS: The 1980s and 2017 classifications had 100% agreement in classifying focal seizures and epilepsy in rural China. A small but significant proportion of generalized seizures and epilepsy and aetiologies classified in the old classifications were re-classified to unknown in the new classifications. These results highlight the need for improvement in clinical evaluation of people with epilepsy in resource-poor settings.

Semiological classification of psychogenic nonepileptic seizures: A systematic review and a new proposal.

PURPOSE: A semiological classification of psychogenic nonepileptic seizures (PNES) may help address proper diagnosis and management and also provide standardization for future studies. The aim of the current paper was to systematically review the literature on the proposed classification systems for the semiology of PNES and to provide a new proposal based on the best available evidence. METHODS: I searched the electronic database PubMed on May 16, 2019 for articles that included the following search terms: "psychogenic" AND "semiology" or "classification" and also "nonepileptic" AND "semiology" or "classification" and also "pseudoseizure" AND "semiology" or "classification" since 1940. I applied the same methodology using the electronic database Scopus, though I limited the search to the title, abstract, and keywords. RESULTS: I could identify 15 classification systems through this search strategy using the electronic database PubMed. Searching the Scopus did not yield any additional relevant papers. CONCLUSION: I proposed a new semiological classification system for PNES based on this systematic review. This includes three major classes of motor seizures, nonmotor seizures, and mixed semiology. A universally accepted and appropriate semiological classification system for PNES may lead to better standardization of future studies and may also help in better understanding of the pathophysiological basis of this condition.

Implementation of the new ILAE classification of epilepsies into clinical practice - A cohort study.

PURPOSE: Appropriate management of patients with epilepsy requires precise classification of their disease. Implementation of the recent International League Against Epilepsy (ILAE) classification of seizures and epilepsies may affect data on the relative proportions of specific types of seizures or epilepsies and should be tested in everyday practice. The aim of the study was to determine the prevalence of specific epilepsy types, syndromes, and etiologies, as defined by the new ILAE classification, in a large cohort of adult patients with epilepsy. MATERIAL AND METHODS: The single-center cohort study involved consecutive adult patients with epilepsy seen at the university epilepsy clinic. Information about medical history, neurological examination, neuroimaging, electroencephalography (EEG), genetic tests, epilepsy treatment, and other investigations was collected from medical records and prospectively updated if necessary. Epilepsy types and etiology, as well as epileptic syndromes, were classified according to the new ILAE classifications. RESULTS: We studied 653 patients (mean age: 37.2 years, 59.9% were women). Epilepsy was classified as focal in 458 cases (70.2%), generalized in 155 subjects (23.7%), or as combined focal and generalized in 11 patients (1.7%). The epilepsy type was labeled as unknown in 29 (4.4%) patients. A definite cause of epilepsy was identified in 59.4% of the cases, with a structural etiology (n = 179, 27.4%) and genetic or presumed genetic etiology (n = 169, 25.9%) being the most common. In 167 (25.5%) patients, specific epilepsy syndromes, mostly genetic generalized epilepsy syndromes, were diagnosed. CONCLUSION: The use of the recent ILAE classification of seizures and epilepsies in the cohort of patients with epilepsy seen in single epilepsy center enabled unequivocal characterization of epilepsy type in >95% of patients. A definite etiology of epilepsy could be established in about 60% of patients.