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.

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.

A scoping review of seizure onset pattern in SEEG and a proposal for morphological classification.

BACKGROUND: Seizure onset pattern (SOP) represents an alteration of electroencephalography (EEG) morphology at the beginning of seizure activity in epilepsy. With stereotactic electroencephalography (SEEG), a method for intracranial EEG evaluation, many morphological SOP classifications have been reported without established consensus. These inconsistent classifications with ambiguous terminology present difficulties to communication among epileptologists. METHODS: We reviewed SOP in SEEG by searching the PubMed database. Reported morphological classifications and the ambiguous terminology used were collected. After thoroughly reviewing all reports, we reconsidered the definitions of these terms and explored a more consistent and simpler morphological SOP classification. RESULTS: Of the 536 studies initially found, 14 studies were finally included after screening and excluding irrelevant studies. We reconsidered the definitions of EEG onset, period for determining type of SOP, core electrode and other terms in SEEG. We proposed a more consistent and simpler morphological SOP classification comprising five major types with two special subtypes. CONCLUSIONS: A scoping review of SOP in SEEG was performed. Our classification may be suitable for describing SOP morphology.

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.

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.

Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world's population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to "pops" in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions.

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.

Epilepsy in Early Onset Alzheimer's Disease.

BACKGROUND: Epilepsy seems to be an important comorbidity in patients with early onset Alzheimer's disease (EOAD). Currently, seizures are still underestimated in this population. However, seizures may interact with AD evolution with possible acceleration of cognitive decline. OBJECTIVE: To better define the epileptic disorders observed in patients with EOAD. METHODS: All patients diagnosed as EOAD in our hospital between 2013 and 2019 with positive CSF biomarkers for AD were selected. The usual follow-up was extended with a 3-h EEG and a consultation with an epilepsy expert. Information on epilepsy and AD were collected and analyzed. RESULTS: Among the 25 included patients, 10 (40%) were classified as epileptic. Seizure types were tonic-clonic (25%), typical temporal seizures (25%), myoclonus (25%), focal extra-temporal seizures (8%), and other seizure types (17%). AD-E patients had a significant lower MMSE (15.3±8.4 AD-E versus 22.1±5.1 AD-NE, p = 0.036) and a lower autonomy (IADL 4.1±2.7 AD-E versus 6.4±1.9 AD-NE, p = 0.046) at AD diagnosis with comparable ages between AD-E and AD-NE. Epileptic patients seemed to present a faster cognitive decline ([ΔMMSE per year 1.7±1.3 AD-E versus 0.9±1.4 AD-NE; p = 0.09). All patients with severe cognitive impairment (MMSE ≤ 10) had an epileptic comorbidity. CONCLUSION: Epilepsy is a frequent comorbidity in EOAD patients, with a percentage of 40%in our study. This comorbidity may be associated with a severe form of EOAD. The role of epilepsy in the acceleration of cognitive decline and the positive impact of antiepileptic drugs on cognition need further research.

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.

Capturing seizures in clinical trials of antiseizure medications for KCNQ2-DEE.

Literature review of patients with KCNQ2 developmental and epileptic encephalopathy (KCNQ2-DEE) reveals, based on 16 reports including 139 patients, a clinical phenotype that includes age- and disease-specific stereotyped seizures. The typical seizure type of KCNQ2-DEE, focal tonic, starts within 0-5 days of life and is readily captured by video-electroencephalography VEEG for clinical and genetic diagnosis. After initial identification, KCNQ2-DEE seizures are clinically apparent and can be clearly identified without the use of EEG or VEEG. Therefore, we propose that the 2019 recommendations from the International League against Epilepsy (ILAE), the Pediatric Epilepsy Research Consortium (PERC), for capturing and recording seizures for clinical trials (Epilepsia Open, 4, 2019, 537) are suitable for use in KCNQ2-DEE‒associated antiseizure medicine (ASM) treatment trials. The ILAE/PERC consensus guidance states that a caregiver-maintained seizure diary, completed by caregivers who are trained to recognize seizures using within-patient historical recordings, accurately captures seizures prospectively in a clinical trial. An alternative approach historically endorsed by the Food and Drug Administration (FDA) compares seizure counts captured on VEEG before and after treatment. A major advantage of the ILAE/PERC strategy is that it expands the numbers of eligible patients who meet inclusion criteria of clinical trials while maintaining accurate seizure counts (Epilepsia Open, 4, 2019, 537). Three recent phase 3 pivotal pediatric trials investigating ASMs to treat syndromic seizures in patients as young as 2 years of age (N Engl J Med, 17, 2017, 699; Lancet, 21, 2020, 2243; Lancet, 17, 2018, 1085); and ongoing phase 2 open-label pediatric clinical trial that includes pediatric epileptic syndromes as young as 1 month of age (Am J Med Genet A, 176, 2018, 773), have already used caregiver-maintained seizure diaries successfully. For determining the outcome of a KCNQ2-DEE ASM treatment trial, the use of a seizure diary to count seizures by trained observers is feasible because the seizures of KCNQ2-DEE are clinically apparent. This strategy is supported by successful precedent in clinical trials in similar age groups and has the endorsement of the international pediatric epilepsy community.

Ambulatory EEG to Classify the Epilepsy Syndrome.

SUMMARY: Recording of interictal epileptiform discharges to classify the epilepsy syndrome is one of the most common indications for ambulatory EEG. Ambulatory EEG has superior sampling compared with standard EEG recordings and advantages in terms of cost-effectiveness and convenience compared with a prolonged inpatient EEG study. Ambulatory EEG allows for EEG recording in all sleep stages and transitional states, which can be very helpful in capturing interictal epileptiform discharges. In the absence of interictal epileptiform discharges or in patients with atypical events, the characterization of an epilepsy syndrome may require recording of the habitual events. Diagnostic ambulatory EEG can be a useful alternative to inpatient video-EEG monitoring in a selected number of patients with frequent events who do not require medication taper or seizure testing for surgical localization.

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.

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.

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.

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.

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.

How to diagnose and classify idiopathic (genetic) generalized epilepsies.

Idiopathic or genetic generalized epilepsies (IGE) constitute an electroclinically well-defined group that accounts for almost one third of all people with epilepsy. They consist of four well-established syndromes and some other rarer phenotypes. The main four IGEs are juvenile myoclonic epilepsy, childhood absence epilepsy, juvenile absence epilepsy and IGE with generalized tonic-clonic seizures alone. There are three main seizure types in IGE, namely generalized tonic-clonic seizures, typical absences and myoclonic seizures, occurring either alone or in any combination. Diagnosing IGEs requires a multidimensional approach. The diagnostic process begins with a thorough medical history with a specific focus on seizure types, age at onset, timing and triggers. Comorbidities and family history should be questioned comprehensively. The EEG can provide valuable information for the diagnosis, including specific IGE syndromes, and therefore contribute to their optimal pharmacological treatment and management.

An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning.

Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients' health, cognition, etc. In the current condition, EEG plays a vital role in the diagnosis, judgment, and qualitative location of epilepsy among the clinical diagnosis of various epileptic seizures and is an indispensable means of detection. The study of the EEG signals of patients with epilepsy can provide a strong basis and useful information for in-depth understanding of its pathogenesis. Although, intelligent classification technologies based on machine learning have been widely used to the classification of epilepsy EEG signals and show the effectiveness. In fact, it is difficult to ensure that there is always enough EEG data available for training the model in real life, which will affect the performance of the algorithms. In view of this, to reduce the impact of insufficient data on the detection performance of the algorithms, a novel discriminate least squares regression- (DLSR-) based inductive transfer learning method was introduced which is on the basis of DLSR and the inductive transfer learning. And, it is applied to promote the adaptability and accuracy of the epilepsy EEG signal recognition. The proposed method inherits the advantages of DLSR; it can be more suitable for classification scenarios by expanding the interval between different classes. Meanwhile, it can simultaneously use the data of the target domain and the knowledge of the source domain, which is helpful for getting better performance. The results show that the improved method has more advantages in EEG signal recognition comparing to several other representative methods.

Absence-to-bilateral-tonic-clonic seizure: A generalized seizure type.

OBJECTIVE: To test the hypothesis that absence seizures can evolve to generalized tonic-clonic seizures, we documented electroclinical features of this novel seizure type. METHODS: In 4 large video-EEG databases, we identified recordings of seizures starting with impaired awareness that, without returning to baseline interictal state, evolved to generalized tonic-clonic seizures. We extracted the detailed semiologic and electrographic characteristics of these seizures, and we documented the clinical background, diagnoses, and therapeutic responses in these patients. RESULTS: We identified 12 seizures from 12 patients. All seizures started with a period of impaired awareness and bursts of generalized spike or polyspike and slow-wave discharges, the hallmark of absence seizures. Without returning to baseline, the nonmotor (absence) phase was followed by tonic-clonic convulsions. We called this novel generalized seizure type absence-to-bilateral-tonic-clonic seizure. Most patients had idiopathic generalized epilepsies, although with a high incidence of unusual features and poor therapeutic response. CONCLUSIONS: Absence-to-bilateral-tonic-clonic seizures are a novel generalized seizure type. Clinicians should be aware of this seizure for correctly diagnosing patients. This novel seizure type may further elucidate generalized ictogenesis.

An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features.

The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals. In order to improve the effect of automatic detection, this study proposes a new classification method based on unsupervised multiview clustering results. In addition, considering the high-dimensional characteristics of the original data samples, a deep convolutional neural network (DCNN) is introduced to extract the sample features to obtain deep features. The deep feature reduces the sample dimension and increases the sample separability. The main steps of our proposed novel EEG detection method contain the following three steps: first, a multiview FCM clustering algorithm is introduced, and the training samples are used to train the center and weight of each view. Then, the class center and weight of each view obtained by training are used to calculate the view-weighted membership value of the new prediction sample. Finally, the classification label of the new prediction sample is obtained. Experimental results show that the proposed method can effectively detect seizures.

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.