Ictal Seizure Research Articles

Epileptic EEG classification via deep learning-based strange attractor

Electroencephalography is commonly exploited in recording brains’ electrical activities for revealing the symptoms of various neurological diseases, e.g., epileptic seizures. We hypothesize that its capability primarily comes from the information it captures from the brain, which is supposed to be a chaotic dynamical system. Therefore, seizure detection in clinical practice could benefit from strange attractor reconstruction from time series in classifying non-ictal and ictal epileptic seizures. We propose a deep learning architecture and the loss function in a latent space to implement the reconstruction. By leveraging the proposed deep model, the hidden coordinates can be derived from a low-dimensional time series or univariate time series (e.g., Electroencephalography). Furthermore, added variables can extract more features from the reconstructed dynamical system. The proposed pipeline is evaluated by using a publicly available epileptic seizure database. Experimental results demonstrated the promising outcome of the proposed approach.

Graphical Insight: Revolutionizing Seizure Detection with EEG Representation.

Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.

The suppressive effect of the specific KCC2 modulator CLP290 on seizure in mice

Epilepsy is a chronic neurological disorder characterized by episodic dysfunction of central nervous system. The most basic mechanism of epilepsy falls to the imbalance between excitation and inhibition. In adults, GABAA receptor (GABAAR) is the main inhibitory receptor to prevent neurons from developing hyperexcitability, while its inhibition relies on the low intracellular chloride anion concentration ([Cl-]i). Neuronal-specific electroneutral K+-Cl− cotransporter (KCC2) can mediate chloride efflux to lower [Cl-]i for GABAAR mediated inhibition. Our previous study has revealed that the coordinated downregulation of KCC2 and GABAAR participates in epilepsy. According to a high-throughout screen for compounds that reduce [Cl−]i, CLP290 turns out to be a specific KCC2 functional modulator. In current study, we first confirmed that CLP290 could dose-dependently suppress convulsant-induced seizures in mice in vivo as well as the epileptiform burst activities in cultured hippocampal neurons in vitro. Then, we discovered that CLP290 functioned through preventing the downregulation of the KCC2 phosphorylation at Ser940 and hence the KCC2 membrane expression during convulsant stimulation, and consequently restored the GABA inhibition. In addition, while CLP290 was given in early epileptogenesis period, it also effectively decreased the spontaneous recurrent seizures. Generally, our current results demonstrated that CLP290, as a specific KCC2 modulator by enhancing KCC2 function, not only inhibits the occurrence of the ictal seizures, but also suppresses the epileptogenic process. Therefore, we believe KCC2 may be a suitable target for future anti-epileptic drug development.

Characterizing Frontal Lobe Seizure Semiology in Children.

The objective was to analyze seizure semiology in pediatric frontal lobe epilepsy patients, considering age, to localize the seizure onset zone for surgical resection in focal epilepsy. Fifty patients were identified retrospectively, who achieved seizure freedom after frontal lobe resective surgery at Great Ormond Street Hospital. Video-electroencephalography recordings of preoperative ictal seizure semiology were analyzed, stratifying the data based on resection region (mesial or lateral frontal lobe) and age at surgery (≤4 vs >4). Pediatric frontal lobe epilepsy is characterized by frequent, short, complex seizures, similar to adult cohorts. Children with mesial onset had higher occurrence of head deviation (either direction: 55.6% vs 17.4%; p = 0.02) and contralateral head deviation (22.2% vs 0.0%; p = 0.03), ictal body-turning (55.6% vs 13.0%; p = 0.006; ipsilateral: 55.6% vs 4.3%; p = 0.0003), and complex motor signs (88.9% vs 56.5%; p = 0.037). Both age groups (≤4 and >4 years) showed hyperkinetic features (21.1% vs 32.1%), contrary to previous reports. The very young group showed more myoclonic (36.8% vs 3.6%; p = 0.005) and hypomotor features (31.6% vs 0.0%; p = 0.003), and fewer behavioral features (36.8% vs 71.4%; p = 0.03) and reduced responsiveness (31.6% vs 78.6%; p = 0.002). This study presents the most extensive semiological analysis of children with confirmed frontal lobe epilepsy. It identifies semiological features that aid in differentiating between mesial and lateral onset. Despite age-dependent differences, typical frontal lobe features, including hyperkinetic seizures, are observed even in very young children. A better understanding of pediatric seizure semiology may enhance the accuracy of onset identification, and enable earlier presurgical evaluation, improving postsurgical outcomes. ANN NEUROL 2024;95:1138-1148.

Stereotactic Electroencephalogram Recordings in Temporal Lobectomy Patients Demonstrates the Predictive Value of Interictal Cross-Frequency Correlations: A Retrospective Study.

Positive correlations between low- and high-frequency spectra from stereotactic electroencephalogram (SEEG) recordings have been implicated in pathological brain activity interictally and have been used for ictal detection in both focal and network models. We evaluated SEEG signals in patients who ultimately underwent temporal lobectomy to evaluate their utility in seizure localization and prediction of seizure freedom post-resection. We retrospectively analyzed cross-frequency correlations between beta and high gamma (HG) interictal SEEG signals from 22 patients. We compared signals based on temporal versus extra-temporal locations, seizure-free (SF) versus non-seizure-free (NSF) outcomes, and mesial (M) versus mesial temporal-plus (M+) onset. Positive cross-correlations were increased in temporal areas. NSF patients showed a higher proportion of positive electrodes in temporal areas. SF patients had a greater proportion of significant channels in mesial versus lateral temporal areas. HG/Beta correlations in mesial versus lateral temporal areas predicted seizure freedom better than ictal SEEG seizure onset localization to M or M+ locations. We present preliminary data that local HG/Beta correlations may predict epilepsy focus and surgical outcome and may have utility as adjunct methods to conventional SEEG analysis. Further studies are needed to determine strategies for prospective studies and clinical use.

The Postictal Phase in Canine Idiopathic Epilepsy: Semiology, Management, and Impact on the Quality of Life from the Owners' Perspective.

Dogs with idiopathic epilepsy experience not only the preictal and ictal seizure phases but also the postictal phase. To date, research has primarily focused on the preictal and ictal semiology and therapeutic control of ictal events. Research into the postictal phase's pathophysiology, as a therapeutic target and how it impacts the quality of life, is sparse across different species. Interestingly, even if anecdotally, owners report the postictal period being an impactful negative factor on their quality of life as well as their dog's quality of life. We aimed to assess the semiology and the impact of postictal signs on the quality of life of owners and dogs. This observational study was carried out using surveys of owners of dogs with seizure disorders. The questionnaire was filled out by 432 dog owners, 292 of whom provided complete responses that could be analysed. More than nine out of ten owners (97%) reported the presence of various postictal clinical signs. The dog's and the owner's quality of life was mainly affected by specific postictal signs, i.e., disorientation (dog: 31%; owner: 20%), compulsive walking (dog: 17%; owner: 22%), ataxia (dog: 12%; owner: 6%), and blindness (dog: 17%; owner: 10%). Nearly 61% of the owners felt that the severity of postictal signs was moderate or severe. Rescue antiseizure medications did not have an effect on controlling the postictal signs based on 71% of the responders. In contrast, 77% of the respondents reported that other measures such as rest, physical closeness, and a quiet and dark environment had a positive impact on the postictal phase. Overall, this survey shows that specific postictal signs are common and have a notable impact on the perceived quality of life of both dogs and their owners. According to the respondents, antiseizure medication might have no influence on the postictal phase in most cases, in contrast to other nonpharmacological measures. Further research on the management of the postictal phase is vital for improving the quality of life of dogs with seizure disorders and their owners.

Controlling neocortical epileptic seizures using forced temporal spike-time stimulation: an in silico computational study.

Epileptic seizure is typically characterized by highly synchronized episodes of neural activity. Existing stimulation therapies focus purely on suppressing the pathologically synchronized neuronal firing patterns during the ictal (seizure) period. While these strategies are effective in suppressing seizures when they occur, they fail to prevent the re-emergence of seizures once the stimulation is turned off. Previously, we developed a novel neurostimulation motif, which we refer to as "Forced Temporal Spike-Time Stimulation" (FTSTS) that has shown remarkable promise in long-lasting desynchronization of excessively synchronized neuronal firing patterns by harnessing synaptic plasticity. In this paper, we build upon this prior work by optimizing the parameters of the FTSTS protocol in order to efficiently desynchronize the pathologically synchronous neuronal firing patterns that occur during epileptic seizures using a recently published computational model of neocortical-onset seizures. We show that the FTSTS protocol applied during the ictal period can modify the excitatory-to-inhibitory synaptic weight in order to effectively desynchronize the pathological neuronal firing patterns even after the ictal period. Our investigation opens the door to a possible new neurostimulation therapy for epilepsy.

Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM

In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)2PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating features in the WST domain, the BW(2D)2PCA was proposed based on original two-directional two-dimensional principal component analysis, by considering both the contribution of eigenvalue and the variation of two adjacent eigenvalues. Totally fifteen classification tasks of classifying normal vs interictal vs ictal EEGs, non-seizure vs seizure EEGs and normal vs congestive heart failure (CHF) ECGs were investigated. Applying patient non-specific strategy, the proposed scheme reported ACCs of no less than 99.300 ± 0.121 % for all the thirteen classification cases of Bonn dataset in classifying normal vs interictal vs ictal EEGs, MCC of 90.947 ± 0.128 % in distinguishing non-seizure vs seizure EEGs of CHB-MIT dataset, and MCC of 99.994 ± 0.001 % in identifying normal vs CHF ECGs of BBIH dataset. Experimental results indicate BW(2D)2PCA based framework outperforms (2D)2PCA based scheme, the high-performance results manifest the effectiveness of the proposed framework and our proposal is superior to most existing approaches.

A novel moving window-based power spectrum features for single-channel EEG classification using machine learning

Electroencephalogram (EEG) signal classification is a crucial and very difficult task. Meanwhile, extracting features that are representative and able to discriminate different types of EEG signals is a complex task. Such features are usually fed to machine learning algorithms to classify the EEG signals based on the extracted features. This paper proposed a highly accurate and real-time features extraction method that can be used to help physicians in detecting different types of seizures and states in EEG signals characterized by a set of features extracted from the power spectrum of the EEG window. This is achieved by applying the following four steps. First, the EEG signals dataset contains different classes of EEG signals: Normal Eye Closed, Normal Eye Opened, Focal Seizure, Non-Focal Seizure, and Ictal Seizure activities. Second, each EEG signal has a length of 4097 samples sampled with a sampling frequency of 173.6 Hz which resulted in 23.6 seconds in length, this signal will be truncated into windows (Sub-signals) with a length of 349 samples (Approximately 2 seconds) with a total number of 12 windows for each signal. Afterward, the Fourier Transform (FT) based power spectrum will be computed for each window, then a set of different features are extracted from each window's FT power spectrum, and these features are classified using different Machine Learning (ML) algorithms. The results showed that the proposed methodology yields around 98% accuracy for the five different classification scenarios using different ML algorithms. The suggested method is hence robust, fast, real-time, accurate, and simple.

Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research.

Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.

Comprehensive Performance Analysis of Classifiers in Diagnosis of Epilepsy

Epilepsy becomes one of the most frequently arising brain disorder, and it is marked by the unexpected occurrence of frequent seizures. In this study, the University of the Boon Database with ictal seizure disorder diagnosis of the epilepsy is classified by making use of the expectation maximization features as dimensionality reduction technique followed by the nonlinear model, namely, Gaussian mixture model, logistic regression, firefly algorithm, and hybrid model such as cuckoo search with Gaussian mixture model and firefly algorithm with the Gaussian mixture model which are the classifiers used for the diagnosis of epilepsy from the electroencephalogram signals. The performance of the classifiers is analyzed based on performance index, sensitivity, specificity, accuracy, mean square error, good detection rate, and error rate. The most promising outcome in this work indicates expectation maximization features are applied as the dimensionality reduction technique and the hybrid model Cuckoo search with the Gaussian mixture model outperforms with classification accuracy of 92.19%, performance index of 81.43%, good detection rate of 83.48%, and with low error rate of 15.62%, among other classifiers.

Predictors of surgical outcome in focal cortical dysplasia and its subtypes.

The authors analyzed predictors of surgical outcome in patients with focal cortical dysplasia (FCD) and its ILAE (International League Against Epilepsy) subtypes after noninvasive multimodal evaluation and calculated time to first seizure. Data of 355 patients with refractory epilepsy, confirmed FCD pathology, and 2-13 years of postsurgical follow-up were analyzed to determine the predictive roles of clinical, EEG, imaging, and surgical factors that influence seizure freedom. The mean ± SD age at surgery was 20.26 ± 12.18 years. In total, 142 (40.0%) patients had daily seizures and 90 (25.3%) had multiple seizure types. MRI showed clear-cut FCD in 289 (81.4%) patients. Pathology suggested type I FCD in 27.3% of patients, type II in 28.4%, and type III in 42.8% of patients. At latest follow-up, 72.1% of patients were seizure free and 11.8% were seizure free and not receiving antiepileptic drugs. Among the subtypes, 88.8% of patients with type III, 69.3% with type II, and 50.5% with type I FCD were seizure free. Multiple seizure types, acute postoperative seizures (APOS), and type I FCD were predictors of persistent seizures, whereas type III FCD was the strongest predictor of seizure freedom. Type I FCD was associated with daily seizures, frontal and multilobar distribution, subtle findings on MRI, incomplete resection, and persistent seizures. Type II and III FCD were associated with clear-cut lesion on MRI, regional interictal and ictal EEG onset pattern, focal pattern on ictal SPECT, complete resection, and seizure freedom. Type III FCD was associated with temporal location, whereas type I and II FCD were associated with extratemporal location. Nearly 80% of patients with persistent seizures, mostly those with type I FCD, had their first seizure within 6 months postsurgery. Long-term seizure freedom after surgery can be achieved in more than two-thirds of patients with FCD after noninvasive multimodal evaluation. Multiple seizure types, type I FCD, and APOS were predictors of persistent seizures. Seizures recurred in about 80% of patients within 6 months postsurgery.

Epileptic Seizure Detection Using Brain-Rhythmic Recurrence Biomarkers and ONASNet-Based Transfer Learning.

The electroencephalogram (EEG) tool has great potential for real-time monitoring of abnormal brain activities, such as preictal and ictal seizures. Developing an EEG-based detection system for patients with epilepsy is vital for clinical management and targeted therapy. This paper proposes a single-channel seizure detection system using brain-rhythmic recurrence biomarkers (BRRM) and an optimized model (ONASNet). BRRM is a direct mapping of the recurrence morphology of brain rhythms in phase space; it reflects the nonlinear dynamics of original EEG signals. The architecture of ONASNet is determined through a modified neural network searching strategy. Then, we exploited transfer learning to apply ONASNet to our EEG data. The combination of BRRM and ONASNet leverages the multiple channels of a neural network to extract features from different brain rhythms simultaneously. We evaluated the efficiency of BRRM-ONASNet on the real EEG recordings derived from Bonn University. In the experiments, different transfer-learning models (TLMs) are respectively constructed using ONASNet and seven well-known neural network structures (VGG16/VGG19/ResNet50/InceptionV3/DenseNet121/Xception/NASNet). Moreover, we compared those TLMs by model size, computing complexity, learning capability, and prediction latency. ONASNet outperforms other structures by strong learning capability, high stability, small model size, short latency, and less requirement of computing resources. Comparing BRRM-ONASNet with other existing methods, our work performs better than others with 100% accuracy under the identical dataset and same detection task. Contributions: The proposed method in this study, analyzing nonlinear features from phase-space representations using a deep neural network, provides new insights for EEG decoding. The successful application of this method in epileptic-seizure detection contributes to computationally medical assistance for epilepsy.

Tertiary wavelet model based automatic epilepsy classification system

PurposeThis work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.Design/methodology/approachIn this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.FindingsFor the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.Originality/valueEpilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.

Substantial outcome improvement using a refined pilocarpine mouse model of temporal lobe epilepsy

Systemic pilocarpine treatment is one of the most reliable means of inducing temporal lobe epilepsy (TLE). However, the traditional pilocarpine injection protocol using mice was associated with a high death rate, possibly because of cardiorespiratory collapse following status epilepticus (SE). To prevent this, we developed a modified procedure of pilocarpine SE induction, which included a single injection of a moderate dose of caffeine during the induction phase. That new protocol was based on the use of young male mice as well as on a refined Racine's scale. Using that protocol, we report a substantially increased survival rate, thus enabling the generation of a large cohort of mice that exhibited cardinal histological (e.g., mossy fiber sprouting) and electrophysiological (e.g., chronic interictal events and ictal seizures) characteristics associated with TLE. In conclusion, our refined caffeine- and pilocarpine-based protocol substantially improves the outcome of the reliable pilocarpine mouse model of TLE.

Automated detection of abnormalities from an EEG recording of epilepsy patients with a compact convolutional neural network

Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires expertise and experience to identify abnormalities. It is thus crucial to develop automated models for the detection of abnormalities in EEGs related to epilepsy. This paper describes the development of a novel class of compact convolutional neural networks (CNNs) for detecting abnormal patterns and electrodes in EEGs for epilepsy. The designed model is inspired by a CNN developed for brain–computer interfacing called multichannel EEGNet (mEEGNet). Unlike the EEGNet, the proposed model, mEEGNet, has the same number of electrode inputs and outputs to detect abnormal patterns. The mEEGNet was evaluated with a clinical dataset consisting of 29 cases of juvenile and childhood absence epilepsy labeled by a clinical expert. The labels were given to paroxysmal discharges visually observed in both ictal (seizure) and interictal (nonseizure) durations. Results showed that the mEEGNet detected abnormalities with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs. Moreover, the number of parameters is much smaller than other CNN models. To our knowledge, the dataset of absence epilepsy validated with machine learning through this research is the largest in the literature.

Analysis of Brief Potentially Ictal Rhythmic Discharges (BIRDs) and seizures in Epilepsy Patients with Responsive Neurostimulation undergoing Scalp Electroencephalography (2982)

Analysis of Brief Potentially Ictal Rhythmic Discharges (BIRDs) and seizures in Epilepsy Patients with Responsive Neurostimulation undergoing Scalp Electroencephalography (2982)

COMPARISON OF VARIOUS EEG ELECTRODE PLACEMENT SYSTEMS TO DETECT EPILEPTIFORM ABNORMALITIES IN INFANTS

Background: Technical difficulties in placement of whole 10-20 electrode system is not uncommon in neonates and infants. Apart from the full channel many centers uses the modified and amplitude integrated EEG montages to identify seizures. Objective: Efficacy of standard, modified and amplitude integrated EEG electrode placement in infants to detect epileptiform abnormalities. Methods: All routine EEGs from June 2015 to April 2018 were taken. Age ≤ 2years at the time of recoding was the inclusion criteria. Digital EEG was performed according to standard 10-20 electrode placement system in all patients. Abnormal EEGs were reanalyzed in different montages like A) 21 electrodes- full channel, B) 10 electrodes- modified long distance and C)4 electrodes- (centro/parietal) respectively. Inter ictal epileptiform discharges (IEDs), seizures, periodic complexes and non-specific dysfunctions were tabulated in all patients. Full channel montages are considered bench mark for the analysis Results: A total 129 EEGs analyzed. A) Full channel standard 21 electrodes montages could identify IEDs in 35, non-specific electrophysiological dysfunction in 9 and burst-suppression pattern in 2 EEGs. B) Modified electrode placement consists of 10 electrodes could identify IEDs only in 26 EEGs and non-specific electro physiological dysfunction in 6 EEGs. A total of 28 nonconvulsive seizures (NCS) recorded in 6 patients; 20 numbers of NCS (71.4%) seen in modified electrode placement (B) and only 16 (57.1%) seen in centro/parietal electrode placement (C).. Conclusion: Standard EEG electrode placement has higher yield in detecting epileptiform abnormalities.

A retrospective, observational study of perampanel in refractory and super-refractory status epilepticus

IntroductionThe outcomes of administration of Perampanel (PER) which is a β-amino-3-(5-methyl-3-oxo-1,2-oxazol-4-yl)propionic acid (AMPA) receptor antagonist for the treatment of refractory status epilepticus (RSE) and Super-refractory (SRSE) were previously reported in small cohort studies and case reports. We report efficacy and side effect results of an observational cohort of 75 patients treated with PER for RSE and SRSE. MethodsThis was a single-center, retrospective, observational study of patients with RSE admitted to the neurocritical care unit between April 2017 and September 2019 who received treatment with PER. The primary outcome was the occurrence of a definite response to PER, which was defined as clear resolution of the ictal pattern and/or seizures within 72 h of delivery of PER which was the last administered antiseizure medication (ASM). Secondary outcomes included the percentage of patients other response types (partial responder or non-responder), as well as the rate of adverse effects. ResultsA total 75 patients were included in our analysis. PER was initiated as the median sixth ASM at a median initial dose of 12 mg. For the primary outcome, 31 (41.3%; 95% confidence interval 31.0%–53.0%) patients were classified as a definite responder. Seven patients (9.3%) experienced an adverse effect that was attributed to PER, with the most common being sedation in four patients. ConclusionsIn our retrospective cohort of RSE, we observed a definite response rate of 41.3% within 72 h of PER initiation. PER was well tolerated with few documented adverse effects. Further prospective studies are needed to confirm the role of PER in treating patients with RSE.

Epileptic Seizures Detection Using DCT-II and KNN Classifier in Long-Term EEG Signals

Epilepsy is one of the most common diseases of the nervous system around the world, affecting all age groups and causing seizures leading to loss of control for a period of time. This study presents a seizure detection algorithm that uses Discrete Cosine Transformation (DCT) type II to transform the signal into frequency-domain and extracts energy features from 16 sub-bands. Also, an automatic channel selection method is proposed to select the best subset among 23 channels based on the maximum variance. Data are segmented into frames of one Second length without overlapping between successive frames. K-Nearest Neighbour (KNN) model is used to detect those frames either to ictal (seizure) or interictal (non-seizure) based on Euclidean distance. The experimental results are tested on 21 patients included in the CHB-MIT dataset. The average F1-score was found to be 93.12, whereas the False-Positive Rate (FPR) average was determined to be 0.07.