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Epilepsy claims the lives of many people, so researchers strive to build highly accurate diagnostic models. One of the limitations of obtaining high accuracy is the scarcity of Electroencephalography (EEG) data and the fact that they are from different devices in terms of the channels number and sampling frequency. The paper proposes universal epilepsy diagnoses with high accuracy from electroencephalography signals taken from any device. The novelty of the proposal is to convert VEEG video into images, separating some parts and unifying images taken from different devices. The images were tested by dividing the video into labeled frames of different periods. By adding the spatial attention layer to the deep learning in the new model, classification accuracy increased to 99.95 %, taking five seconds/frame. The proposed has high accuracy in detecting epilepsy from any EEG without being restricted to a specific number of channels or sampling frequencies.

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BACKGROUND: Anti leucine-rich, glioma inactivated 1 (LGI1) antibody-associated autoimmune encephalitis (AE) is the second most common AE, where the trafficking and recycling of the pathogenic immunoglobulin (IgG) can be controlled by the neonatal crystallizable fragment receptor (FcRn), making the latter as a candidate therapeutic target. Efgartigimod is an antagonist of FcRn, its ability to increase the degradation of IgGs and improve the health and quality of life of patients. ADAPT trail indicated its rapid efficacy and safety on myasthenia gravis. However, there is currently no case reported using efgartigimod for the treatment of anti-LGI1-associated AE. CASE DESCRIPTION: The patient presented with five episodes of generalized tonic-clonic seizures in the past 2 weeks. The patient had no abnormal signs on magnetic resonance imaging. Electroencephalogram examinations showed an increase in bilateral symmetric or asymmetric slow activity, without any clear epileptic waves. The cerebrospinal fluid (CSF) examination results indicated a slight increase in protein (47 mg/dL). The anti-LGI1 antibody titer in serum was 1:100 and that in CSF was 1:3.2. The treatment with intravenous methylprednisolone 1000 mg once a day combined with levetiracetam tablets failed to completely control the patient's seizures. Thus, 10 mg/kg efgartigimod was administered intravenously once a week for 2 weeks. After 2 weeks of treatment, serum levels of anti-LGI1 antibody and IgG decreased and the patient's epilepsy did not recur in the next 3 months. CONCLUSIONS: This is the first case report of using efgartigimod to treat anti-LGI1-associated AE. The combination of efgartigimod and methylprednisolone resulted in favorable outcomes, indicating that this is an optional treatment plan.

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Epilepsy is one of the most common brain disorders, and seizures of epilepsy have severe adverse effects on patients. Real-time epilepsy seizure detection using electroencephalography (EEG) signals is an important research area aimed at improving the diagnosis and treatment of epilepsy. This paper proposed a real-time approach based on EEG signal for detecting epilepsy seizures using the STFT and Google-net convolutional neural network (CNN). The CHB-MIT database was used to evaluate the performance, and received the results of 97.74 % in accuracy, 98.90 % in sensitivity, 1.94 % in false positive rate. Additionally, the proposed method was implemented in a real-time manner using the sliding window technique. The processing time of the proposed method just 0.02 s for every 2-s EEG episode and achieved average 9.85- second delay in each seizure onset.

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OBJECTIVES: Status epilepticus (SE) is a serious event associated with high mortality. This study aims to validate the recently developed ADAN (Abnormal speech, ocular Deviation, Automatisms, and Number of motor epileptic seizures) scale for detecting high risk for SE. MATERIAL AND METHODS: Prospective, multicenter, observational study in adults with suspected epileptic seizures. Consecutive recruitment took place over a 27-month period in 4 hospital emergency departments (EDs). The main endpoint was the proportion of patients with criteria for SE based on the collection and analysis of clinical characteristics and the ADAN scale criteria on arrival at the ED. RESULTS: Of the 527 patients recruited, 203 (38.5%) fulfilled the criteria that predicted SE. Multiple regression analysis demonstrated that the 4 ADAN criteria were the only variables independently associated with a final diagnosis of SE (P .001). The predictive power of the scale was 90.9% (95% CI, 88.4%-93.4%) for a final SE diagnosis. We established 3 risk groups based on ADAN scores: low (score, 0-1: 8.7%), moderate (2, 46.6%), and high (> 2, 92.6%). A cut point of more than 1 had a sensitivity of 88.2% for predicting SE, specificity of 77.8%, positive predictive value of 71.3%, and negative predictive value of 91.3%. CONCLUSION: The ADAN scale is a prospectively validated, simple clinical tool for identifying patients in the ED who are at high risk for SE.

OBJETIVO: El estado epiléptico (EE) es una enfermedad grave con elevada mortalidad. Este estudio tiene como objetivo validar la escala ADAN, propuesta recientemente para identificar pacientes con alto riesgo de desarrollar un EE. METODO: Se realizó un estudio prospectivo, multicéntrico y observacional que incluyó a pacientes adultos con sospecha de crisis epilépticas. Se llevó a cabo un reclutamiento consecutivo durante 27 meses en los servicios de urgencias (SU) de cuatro hospitales. La variable principal fue la proporción de pacientes que cumplían criterios para EE. Se han recopilado y analizado las características clínicas y la puntuación en la escala ADAN a su llegada al SU. RESULTADOS: Se reclutaron 527 pacientes, de los cuales 203 (38,5%) cumplieron criterios de EE. En el análisis de regresión múltiple, se demostró que el habla anormal, la desviación ocular, los automatismos y el número de crisis epilépticas motoras fueron las únicas variables independientemente asociadas con un diagnóstico final de EE (p 0,001). La capacidad predictiva de la escala fue del 90,9% (intervalo de confianza del 95%, 88,4-93,4) para identificar el EE como diagnóstico final. Se establecieron tres grupos de riesgo: bajo (0 1 puntos: 8,7%), moderado (2: 46,6%) y alto (> 2: 92,6%). Una puntuación de corte > 1 punto proporcionó una sensibilidad del 88,2%, especificidad del 77,8%, valor predictivo positivo del 71,3% y valor predictivo negativo del 91,3% para predecir el EE. CONCLUSIONES: La escala ADAN es una herramienta clínica simple y validada de manera prospectiva para identificar, en los SU, a los pacientes con elevado riesgo de EE.

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Purpose/Significance The recent applications of machine learning in epilepsy seizure prediction,diagnosis prediction,seizure detection,efficacy prediction of antiepileptic drugs,and epilepsy surgery prediction are summarized and analyzed.Method/Processs Literatures are searched through PubMed to summarize the performance of each machine learning model and the challenges exist-ing in machine learning technology.Result/Conclusion Machine learning plays an important role in the diagnosis and treatment of epi-lepsy,and can provide reference for clinical doctors'diagnosis and treatment work.

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Autoimmune encephalitis (AE) is closely related to epileptic seizure, which is a common clinical manifestation or even the only symptom at acute phase of AE. Most patients do not develop seizures after treatment at acute or subacute stages, and these patients are classified as acute symptomatic seizures secondary to AE (ASSAE). Only a minority of patients will eventually develop autoimmune associated epilepsy (AAE). At present, no unified standard for clinical diagnosis is noted between ASSAE and AAE, but some differences exist in definition, pathogenesis, diagnosis and treatment. This paper summarizes the similarities and differences between ASSAE and AAE in the above aspects, aiming at providing help for clinicians to differentiate the diagnosis of the two diseases.

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PURPOSE: Currently, the diagnosis of neural antibody-mediated epilepsy/seizure (NAME/S)relies heavily on neural antibody testing, which is time-consuming, costly and introduces diagnostic delays. A statistical tool to predict the probability of a patient with NAME/S is lacking. We aimed to construct a predictive model to help clinicians expedite the diagnostic process. METHODS: We retrospectively recruited subjects (206 in the development group and 62 in the validation group) with new-onset seizures or established epilepsy suspected to have presented with antibody-mediated seizures between January 2014 and December 2019. We collected data about demographics, medical history, clinical manifestations and follow up. Binary logistic regression was used to select potential predictors for the construction of a predictive model. Five-fold cross and bootstrap validation were applied to avoid overfitting. Concordance index, calibration plots and decision curve analysis were used to assess its performance. RESULTS: The model, incorporating presence/absence of tumour, psychiatric/cognitive/emotional changes, language disturbances, sensory auras, tonic-clonic seizures, multiple seizure events, hyponatremia and MRI inflammation, was visualized as a nomogram. The crude and adjusted concordance indices were both 0.88 with a cut-off value of 0.62, sensitivity of 83.2 % and specificity of 77.4 %. The slope and intercept of the calibration curve were 0 and 1, respectively. The model also showed good performance in the validation group with a concordance index of 0.82, cut-off value of 0.33, sensitivity of 75.5 % and specificity of 73.1 %. The slope was 0.86 and the intercept was 0.039. Decision curve analysis showed that the model was useful with an optimal threshold probability of ＞4 % in both groups. CONCLUSIONS: Despite limitations such as sample volume and selection bias in subject enrolment, this model may be used to estimate the individualized probability of having NAME/S, deserving further exploration and validation.

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Physiological experiments and computational models both show that the thalamic reticular nucleus (RE) participates in inducing various firing patterns of cortex. Absence seizure, featured by 2-4 Hz spike-wave discharges (SWD) oscillation, is a high incidence of disease in children. Lots of electrophysiological experiments have verified the correlation between absence seizures and RE, however, the dynamical mechanisms are not well understood. Based on previous Taylor model, we firstly study the effects of external input and self-inhibition of RE on epilepsy transition. We show that increasing external input and self-inhibition of RE can lead the system from epileptic state to normal state, and vice versa. Next, we explore two stimulus strategies added in RE and various transition behaviors can be induced, such as high saturated state to clonic. Meanwhile, as the intensity of stimulation increasing, they can not only suppress the SWD, but also produce tonic-clonic oscillation. Finally, the control of DBS on single neuron cluster and two neuron clusters are compared and we find stimulating RE and TC simultaneously is a superior mode to stimulate anyone of RE or TC. It is hoped that the results we obtained will have an enlightenment on clinical treatment.

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Deficiency of the endoplasmic reticulum transmembrane protein ARV1 leads to epileptic encephalopathy in humans and in mice. ARV1 is highly conserved, but its function in human cells is unknown. Studies of yeast arv1 null mutants indicate that it is involved in a number of biochemical processes including the synthesis of sphingolipids and glycosylphosphatidylinositol (GPI), a glycolipid anchor that is attached to the C-termini of many membrane bound proteins. GPI anchors are post-translational modifications, enabling proteins to travel from the endoplasmic reticulum (ER) through the Golgi and to attach to plasma membranes. We identified a homozygous pathogenic mutation in ARV1, p.Gly189Arg, in two brothers with infantile encephalopathy, and characterized the biochemical defect caused by this mutation. In addition to reduced expression of ARV1 transcript and protein in patients' fibroblasts, complementation tests in yeast showed that the ARV1 p.Gly189Arg mutation leads to deficient maturation of Gas1, a GPI-anchored protein, but does not affect sphingolipid synthesis. Our results suggest, that similar to mutations in other proteins in the GPI-anchoring pathway, including PIGM, PIGA, and PIGQ, ARV1 p.Gly189Arg causes a GPI anchoring defect and leads to early onset epileptic encephalopathy.

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Anterior shoulder dislocations following an epileptic event are considered rare. An extremely rare case of a 41 year old female suffering from bilateral anterior shoulder dislocation with concomitant greater tuberosities fractures after an epileptic seizure is presented. The patient presented to the out-patient orthopaedic clinic due to persistent pain and restriction of shoulders movement, 4 weeks after an epileptic seizure. Clinical examination and radiological evaluation established the diagnosis of bilateral anterior shoulder dislocation with concomitant greater tuberosities fractures. Closed reduction was performed under general anesthesia. There are 12 such cases in the literature, including the present one. Thirty percent of these cases had a delayed diagnosis. It is of paramount importance to have a high clinical suspicion for myoskeletal injuries and especially for shoulder dislocations following an epileptic episode, even in the absence of a traumatic event.

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Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.

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The definition of who has epilepsy, classification of seizure types, and types of epilepsy have all recently been revised. The classical definition of epilepsy as a person having two or more unprovoked seizures more than 24 hours apart has been expanded also to include those with one seizure and a high likelihood (more than 60%) of having another. In the new definition, epilepsy is considered to be resolved when a person is seizure-free for 10 years, the terminal 5 being off seizure medicines, or when an age-dependent syndrome has been outgrown. The new seizure type classification revises the 1981 system but maintains the primary distinction of focal- versus generalized-onset seizures. Seizures also can be of unknown onset. Focal seizures may demonstrate retention or impairment of awareness, resulting in focal-aware or focal-impaired awareness seizures. Several new focal and generalized seizure types are introduced. Classification of the epilepsies is now by grouping of seizure types, etiologies, comorbidities, and epilepsy syndromes. The goal of the new terminology is greater clarity of communication and more accurate grouping of seizure types for research. Neurodiagnostic technologists can be of great help in observing clinical and electrographic features that will define the type of seizure.

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Wearable pressure sensors have attracted increasing attention for biomechanical monitoring due to their portability and flexibility. Although great advances have been made, there are no facile methods to produce sensors with good performance. Here, we present a simple method for manufacturing flexible and self-powered piezoelectric sensors based on LiNbO3 (LN) particles. The LN particles are dispersed in polypropylene (PP) doped with multiwalled carbon nanotubes (MWCNTs) by hot pressing (200 °C) to form a flexible LN/MWCNT/PP piezoelectric composite film (PCF) sensor. This cost-effective sensor has high sensitivity (8 Pa), fast response time (ca. 40 ms), and long-term stability (>3000 cycles). Measurements of pressure changes from peripheral arteries demonstrate the applicability of the LN/MWCNT/PP PCF sensor to biomechanical monitoring as well as its potential for biomechanics-related clinical diagnosis and forecasting.

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Mood disorders associated with epilepsy are very common and overrepresented compared with other chronic medical conditions. Depression is a particularly common and worrisome comorbidity, especially because suicidality seems to be increased significantly in the context of epilepsy. Although psychosocial stressors commonly are associated, intrinsic characteristics of seizure disorders may contribute to the expression of depressive symptoms. Depression and epilepsy may exacerbate each other. Epilepsy with seizure foci in the temporal lobe may represent a higher risk of developing depression, especially if the seizures do not generalize. Treatment of depression is multifaceted and includes psychotherapy and sophisticated regimens of anticonvulsants. Most antidepressants may be used safely and effectively in the context of depression, although high-quality evidence is lacking. Ultimately, treatment of comorbid mood disorder has important implications for outcome and quality of life, perhaps even more than treatment of epilepsy itself.

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BACKGROUND AND OBJECTIVE: Epileptic seizure detection is traditionally performed by expert clinicians based on visual observation of EEG signals. This process is time-consuming, burdensome, reliant on expensive human resources, and subject to error and bias. In epilepsy research, on the other hand, manual detection is unsuitable for handling large data-sets. A computerized seizure identification scheme can eradicate the aforementioned problems, aid clinicians, and benefit epilepsy research. METHODS: In this work, a new automated epilepsy diagnosis scheme based on Tunable-Q factor wavelet transform (TQWT) and bootstrap aggregating (Bagging) using Electroencephalogram (EEG) signals is proposed. Until now, this is the first time spectral features in the TQWT domain in conjunction with Bagging are employed for epilepsy seizure identification to the best of the authors' knowledge. At first, we decompose the EEG signal segments into sub-bands using TQWT. We then extract various spectral features from the TQWT sub-bands. The suitability of spectral features in the TQWT domain is established through statistical measures and graphical analyses. Afterwards, Bagging is employed for epileptic seizure classification. The efficacy of Bagging in the proposed detection scheme is also studied in this research. The effects of various TQWT and Bagging parameters are investigated. The optimal choices of these parameters are also determined. The performance of the proposed scheme is studied using a publicly available benchmark EEG database for various classification cases that include inter-ictal (seizure-free interval), ictal (seizure) and healthy; seizure and non-seizure; ictal and inter-ictal; and seizure and healthy. RESULTS: In comparison with the state-of-the-art algorithms, the performance of the proposed method is superior in terms of sensitivity, specificity, and accuracy. CONCLUSION: The seizure detection method proposed herein therefore can alleviate the burden of medical professionals of analyzing a large bulk of data by visual inspection, speed-up epilepsy diagnosis and benefit epilepsy research.

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This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use.

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Peri-ictal water drinking (PIWD) has been reported as the action of drinking during or within two minutes of an electroclinical seizure. It is considered a peri-ictal vegetative symptom, evident both during childhood and adulthood epilepsy. The aim of this paper was to describe the clinical and electroencephalographic features of two new adult subjects suffering from symptomatic temporal lobe epilepsy with episodes of PIWD recorded by VIDEO-EEG and to review literature data in order to better define this peculiar event during seizures, a rare and probably underestimated semiological sign. To date, 51 cases with focal epilepsy and seizures associated with PIWD have been reported. All patients presented with temporal lobe epilepsy. All cases but one had symptomatic epilepsy. Most of the patients had an involvement of the right hemisphere. Water drinking was reported as an ictal sign in the majority of patients, and less frequently was reported as postictal. We believe that PIWD might be considered a rare automatic behaviour, like other automatisms. Automatisms are more frequently described in patients with temporal lobe epilepsy. PIWD was reported also to have lateralizing significance in the non-dominant temporal lobe, however, because of its rarity, this finding remains unclear.

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PURPOSE: The significance of periodic EEG patterns in patients with impaired consciousness is controversial. We aimed to determine if treating these patterns influences clinical outcome. METHOD: We studied all patients who had periodic discharges on their EEG recordings from January 2007 to December 2009. Patients with clinical seizures within the preceding 24h, or with unequivocal electrographical seizure activity were excluded. Logistic regression was performed to analyze for factors associated with (a) mortality (b) functional status (c) resolution of EEG pattern. RESULTS: Of the 4246 patients who had EEG, 111 (2.6%) had periodic EEG patterns. 64 met inclusion criteria. In adjusted analysis, higher mortality was associated with acute symptomatic etiology (OR 17.74, 95% CI 1.61-196.07, p=0.019), and presence of clinical seizures (OR 4.73, 95% CI 1.10-20.34, p=0.037). For each unit decrement of GCS, the odds of inpatient mortality and a poorer functional state on discharge increased by 23% (95% CI 7-37%, p=0.009) and 33% (95% CI 9-51%, p=0.011) respectively. Administration of abortive therapy was an independent risk factor for poorer functional status on discharge (adjusted OR 41.39, 95% CI 2.88-594.42, p=0.006), while patients with history of pre-existing cerebral disease appeared more likely to return to baseline functional status on discharge (unadjusted OR 5.00, 95% CI 1.40-17.86, p=0.013). CONCLUSION: Treatment of periodic EEG patterns does not independently improve clinical outcome of patients with impaired conscious levels. Occurrence of seizures remote to the time of EEG and lower GCS scores independently predict poor prognoses.

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Temporal seizures due to hippocampal origins are very common among epileptic patients. Presented is a novel seizure prediction approach employing correlation and chaos theories. The early identification of seizure signature allows for various preventive measures to be undertaken. Electro-encephalography signals are spectrally broken down into the following sub-bands: delta; theta; alpha; beta; and gamma. The proposed approach consists of observing a high correlation level between any pair of electrodes for the lower frequencies and a decrease in the Lyapunov index (chaos or entropy) for the higher frequencies. Power spectral density and statistical analysis tools were used to determine threshold levels for the lower frequencies. After studying all five sub-bands, the analysis has revealed that the seizure signature can be extracted from the delta band and the high frequencies. High frequencies are defined as both the gamma band and the ripples occurring within the 60-120 Hz sub-band. To validate the proposed approach, six patients from both sexes and various age groups with temporal epilepsies originating from the hippocampal area were studied using the Freiburg database. An average seizure prediction of 30 min, an anticipation accuracy of 72%, and a false-positive rate of 0% were accomplished throughout 200 h of recording time.

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The monitoring of epileptic seizures is mainly done by means of electroencephalogram (EEG) monitoring. Although this method is accurate, it is not comfortable for the patient as the EEG-electrodes have to be attached to the scalp which hampers the patient's movement. This makes long-term home monitoring not feasible. In this paper, the aim is to propose a seizure detection system based on accelerometry for the detection of epileptic seizure. The used sensors are wireless, which can improve quality of life for the patients. In this system, three 2D accelerometer sensors are positioned on the right arm, left arm, and left thigh of an epileptic patient. Datasets from three patients suffering from severe epilepsy are used in this paper for the development of an automatic detection algorithm. This monitoring system is based on Wireless Sensor Networks and can determine the location of the patient when a seizure is detected and then send an alarm to hospital staff or the patient's relatives. Our wireless sensor nodes are MICAz Motes developed by Crossbow Technology. The proposed system can be used for patients living in a clinical environment or at their home, where they do only their daily routines. The analysis of the recorded data is done by an Artificial Neural Network and K Nearest-Neighbor to recognize seizure movements from normal movements. The results show that K Nearest Neighbor performs better than Artificial Neural Network for detecting these seizures. The results also show that if at least 50% of the signal consists of seizure samples, we can detect the seizure accurately. In addition, there is no need for training the algorithm for each new patient.