Diagnosis Of Epileptic Seizures Research Articles

Differential diagnosis of cataplexy and generalized epilepsy. Case report

Cataplexy is rare disease, the main clinical features of which are loss of muscle tone without loss of consciousness, associated with specific factors such as laughter, and less often fear, anger or crying. The differential diagnosis of epileptic seizures and cataplexy is often difficult because of similar clinical features. Establishing the diagnosis of cataplexy is important because in most cases it is associated with narcolepsy; in addition, misinterpretation of cataplexy can lead to hyperdiagnosis of epilepsy or wrong efficiency mark of antiepileptic drugs. Clinical and instrumental examination is necessary to establish the right diagnosis. Literature review and own clinical case is described.

Optimized k-nearest neighbours classifier based prediction of epileptic seizures

Epileptic seizure is an unstable condition of the brain that cause severe mental disorder and can be fatal if not properly diagnosed at an early stage. Electroencephalogram (EEG) plays a major role in early diagnosis of epileptic seizures. The volume of medical databases is enormous. Classification may become less accurate if the dataset contains redundant and irrelevant attributes. To reduce the mortality rate due to epilepsy, a decision support system that can assist medical professionals in taking immediate precautionary measures prior to reaching the critical condition is required. In this work, k-nearest neighbours (KNN) classifier algorithm is optimised using genetic algorithm for effective classification and faster prediction to meet this requirement. Genetic algorithms search for optimal solutions in complex and large environments. Results are compared with other machine learning models such as support vector machine (SVM), KNN, decision tree classifier, and random forest. With optimization using genetic algorithm KNN was able to achieve an enhancement in accuracy at lower training and testing times. It was observed that the accuracy offered by optimized KNN was 92%. Random forest classifiers showed minimum complexity and KNN algorithm provided faster performance with better accuracy.

Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches

Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

A Model for Epileptic Seizure Diagnosis Using the Combination of Ensemble Learning and Deep Learning

A Model for Epileptic Seizure Diagnosis Using the Combination of Ensemble Learning and Deep Learning

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal.

Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.

Application of recurrent analysis to classify realizations of encephalograms

The current state of science and technology is characterized by a variety of methods and approaches to solving various tasks, including in the fields of time series analysis and computer vision. This abstract explores a novel approach to the classification of time series based on the analysis of brain activity using recurrent diagrams and deep neural networks. The work begins with an overview of recent achievements in the field of time series analysis and the application of machine learning methods. The importance of time series classification in various domains, including medicine, finance, technology, and others, is em-phasized. Next, the methodology is described, in which time series are transformed into gray-scale images using recurrent diagrams. The key idea is to use recurrent diagrams to visualize the structure of time series and identify their nonlinear properties. This transformed informa-tion serves as input data for deep neural networks. An important aspect of the work is the selection of deep neural networks as classifiers for the obtained images. Specifically, residual neural networks are applied, known for their ability to effectively learn and classify large volumes of data. The structure of such networks and their advantages over other architectures are discussed. The experimental part of the work describes the use of a dataset of brain activity, which includes realizations from different states of a person, including epileptic seizures. The ob-tained visualization and classification methods are applied for binary classification of EEG realizations, where the class of epileptic seizure is compared with the rest. The main evalua-tion metrics for classification are accuracy, precision, recall, and F1-score. The experimental results demonstrate high classification accuracy even for short EEG realizations. The quality metrics of classification indicate the potential effectiveness of this method for automated di-agnosis of epileptic seizures based on the analysis of brain signals. The conclusions highlight the importance of the proposed approach and its potential usefulness in various domains where time series classification based on the analysis of brain activity and recurrent diagrams is required.

Markers that can be used in the differential diagnosis of epileptic seizure and syncope in patients with loss of consciousness

Amaç: Bilinç kaybı, senkop ve epileptik nöbetlerin ortak belirtisidir. Çalışmamızda acil servise bilinç kaybı ile başvuran hastalarda senkop ve epileptik nöbet ayırıcı tanısı için kullanılabilecek klinik ve biyokimyasal biyobelirteçleri belirlemek amaçlanmıştır.
 Gereç ve Yöntemler: Hastanemiz acil servisine üç ay boyunca bilinç kaybı şikâyeti ile başvuran hastaların dosyaları ve laboratuvar incelemeleri (laktat, hemoglobin, nötrofil, lenfosit) sonuçları tarandı, demografik özellikleri ve eşlik eden hastalıkları kaydedildi. Elde edilen veriler istatistiksel analizinde SofaStat (Auckland, New Zealand) programı kullanıldı.
 Bulgular: Epileptik nöbet nedeniyle başvuran hastalar, senkop nedeniyle başvuranlara kıyasla daha genç hastalardı ancak cinsiyet dağılımı birbirine benzerdi. Senkop ile başvuranlarda diyabet ve hipertansiyon öyküsü daha fazlaydı. İlk kez başvuranlara kıyasla daha önce benzer nöbet yaşayanların epilepsi olma olasılığı daha fazlaydı. Epileptik nöbet ile başvuranlarda, epizot sonrasında konfüzyon öyküsü senkop ile başvuranlardan daha fazlaydı. Epileptik nöbet nedeniyle başvuranların serum laktat düzeyi ortancası (3,3 U/L), senkop nedeniyle başvuranlara (2,3 U/L) kıyasla anlamlı düzeyde daha yüksekti (p=0,002). Serum laktat değerleri analiz edildiğinde, epilepsi ve senkop hastalarını ayırt etmek için en iyi eşik değer 2,5 U/L idi. Bu eşik değerin tanısal doğruluk oranı % 60,5 iken, sensitivitesi %60,2 ve spesifisitesi %62,3 idi. Kadınların hemoglobin ve hematokrit değerlerinin ortancası erkeklere kıyasla anlamlı düzeyde daha düşüktü. 
 Sonuç: Çalışmamızın sonuçları, serum laktat düzeylerinin epileptik nöbet nedeniyle başvuran hastaları senkop nedeniyle başvuranlardan ayırt etmede önemli bir biyobelirteç olduğunu göstermektedir. Serum laktat değeri özellikle hasta ve hasta yakınlarının epizodu tarifleyemediği durumlarda değerli bilgiler sunabilir.

EEG Epileptic Data Classification Using the Schrodinger Operator's Spectrum.

Epilepsy is a common brain disorder characterized by recurrent, unprovoked seizures which affects over 65 million people. Visual inspection of Electroencephalograms (EEG) is common for diagnosis; however, it requires time and expertise. Therefore, an accurate computer-aided epileptic seizure diagnosis system would be valuable. A new research tendency when tackling epileptic seizure detection tends towards minimizing human manual intervention by designing frameworks with autonomous feature engineering. In this optic, this paper proposes a new approach for EEG epileptic data classification. Features derived from the Semi-Classical Signal Analysis (SCSA) method, a quantum-inspired signal processing method well-suited for the characterization of pulse-shaped physiological signals, are proposed. In addition nonlinear dynamical features that proved efficient in characterizing nonlinear dynamics of neural activity have been extracted. Moreover, hyperparameters' optimization, correlation analysis and feature selection have been performed. The selected features are fed into five different machine learning classifiers. The performance of the proposed approach has been analyzed using Bonn university database. The results show that all classifiers yield a performance accuracy of 93% and above.Clinical relevance- The paper contributes to the design of methods and algorithms to build reliable software solutions to assist medical experts and reduce epilepsy disease's diagnosis time and errors.

Automated diagnosis of epileptic seizures using EEG image representations and deep learning

BackgroundThe identification of seizure and its complex waveforms in electroencephalography (EEG) through manual examination is time consuming, tedious, and susceptible to human mistakes. These issues have prompted the design of an automated seizure detection system that can assist the neurophysiologists by providing a fast and accurate analysis. MethodsExisting automated seizure detection systems are either machine learning based or deep learning based. Machine learning based algorithms employ handcrafted features with sophisticated feature selection approaches. As a result of which their performance varies with the choice of the feature extraction and selection techniques employed. On the other hand, deep learning-based methods automatically deduce the best subset of features required for the categorization task but they are computationally expensive and lacks generalization on clinical EEG datasets. To address the above stated limitations and motivated by the advantage of continuous wavelet transform's (CWT) in elucidating the non-stationary nature of the EEG signals in a better way, we propose an approach based on EEG image representations (constructed via applying WT at different scale and time intervals) and transfer learning for seizure detection. Firstly, the pre-trained model is fine-tuned on the EEG image representations and thereafter features are extracted from the trained model by performing activations on different layers of the network. Subsequently, the features are passed through a Support Vector Machine (SVM) for categorization using a 10-fold data partitioning scheme. Results and comparison with existing methodsThe proposed mechanism results in a ceiling level of classification performance (accuracy=99.50/98.67, sensitivity=100/100 & specificity=99/96) for both the standard and the clinical dataset that are better than the existing state-of-the art works. ConclusionThe rapid advancement in the field of deep learning has created a paradigm shift in automated diagnosis of epilepsy. The proposed tool has effectually marked the relevant EEG segments for the clinician to review thereby reducing the time burden in scanning the long duration EEG records.

Latency of epileptic and psychogenic nonepileptic seizures.

Due to their semiological similarities, psychogenic nonepileptic seizures (PNESs) can occasionally hardly be differentiated from epileptic seizures (ESs), and long-term video-electroencephalographic monitoring (VEM) is needed for the differential diagnosis. To investigate the time of the first clinical event and its distribution on the days of VEM in ES and PNES patients. In total, a consecutive series of 48 PNES and 51 ES patients matched for gender and age were retrospectively and consecutively evaluated. The time distribution of the seizures during the day was noted. Seizure latency was determined as the time in hours from the start of the video-electroencephalographic recording to the first clinical event. The seizure latency was significantly shorter in PNES patients compared to ES patients (p < 0.001). Seventy-two percent of PNES patients and 49.1% of ES patients had their first seizure in the 24 hours of video-EEG recording (p = 0.023). Recording longer than 48 hours was required for 12.5% of PNES patients and 37.3% of ES patients (p = 0.006). While ESs were almost evenly distributed throughout the day, most PNESs occurred during the evening hours (p = 0.011). We observed that the PNESs appeared earlier than the ESs in the VEM and were concentrated during daylight hours. Although not strictly reliable, seizure latency can contribute to the differential diagnosis of ES and PNES.

Use of video alone for differentiation of epileptic seizures from non-epileptic spells: A systematic review and meta-analysis

IntroductionReview of videos (without electroencephalography) to differentiate epileptic seizures (ES) from non-epileptic spells (NES) may be helpful where epilepsy monitoring is not feasible. Previous studies of video-based diagnosis have suffered from variable accuracy, sensitivity, and specificity. MethodsWe systematically reviewed relevant literature in PubMed, Embase, and Web of Science from inception to September 2022, identifying articles that reported on the video-based diagnosis of ES and NES. In primary analysis, for each study, the most expert group was chosen when different groups of reviewers classified the videos (e.g., epilepsy specialists and general neurologists). In secondary analysis, we compared the diagnostic accuracy of different expertise levels (e.g., epileptologists, general neurologists, residents, medical students). Meta-analysis was performed to obtain pooled estimates of reliability measures. ResultsFrom 5245 articles identified, 13 met the inclusion criteria, with cumulative data from 683 patients (696 videos) reviewed by 95 independent reviewers in primary analysis. Video alone had a strong ability to differentiate ES from NES as evidenced by the following metrics- area under the curve- 0.9 (considered “outstanding”), sensitivity- 82.2% (95% Confidence Interval [C.I], 80.2%-84.0%), specificity- 84.7% (C.I., 82.8%-86.5%), and diagnostic odds ratio- 24.7 (C.I., 11.5–52.9). The secondary analysis showed reviewer-dependent accuracy with epileptologists showing the highest accuracy (DOR 81.2, C.I., 90.0%-94.6%). ConclusionsVideo alone has reliable diagnostic performance for differentiating ES from NES. Meta-analysis limitations include inter-study heterogeneity including variable video quality and reviewer expertise. Combined video-EEG remains the gold standard for the diagnosis of epilepsy and NES.

Classification Model for Epileptic Seizure Using Simple Postictal Laboratory Indices.

Distinguishing syncope from epileptic seizures in patients with sudden loss of consciousness is important. Various blood tests have been used to indicate epileptic seizures in patients with impaired consciousness. This retrospective study aimed to predict the diagnosis of epilepsy in patients with transient loss of consciousness using the initial blood test results. A seizure classification model was constructed using logistic regression, and predictors were selected from a cohort of 260 patients using domain knowledge and statistical methods. The study defined the diagnosis of seizures and syncope based on the consistency of the diagnosis made by an emergency medicine specialist at the first visit to the emergency room and the diagnosis made by an epileptologist or cardiologist at the first outpatient visit using the International Classification of Diseases 10th revision (ICD-10) code. Univariate analysis showed higher levels of white blood cells, red blood cells, hemoglobin, hematocrit, delta neutrophil index, creatinine kinase, and ammonia levels in the seizure group. The ammonia level had the highest correlation with the diagnosis of epileptic seizures in the prediction model. Therefore, it is recommended to be included in the first examination at the emergency room.

Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges.

Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians' ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.

Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure.

Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder-Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our approach can contribute to the accurate and optimized detection of seizures while scaling the design rules and increasing performance without changing the network's depth.

Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis

Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis

Forensic Identification of a Traffic Accident Caused by Epilepsy: A Case Report

Epileptic seizures have typical characteristics including the sudden arrest of movement and being transient and self-limiting, which will lead to corresponding restrictions on daily related behaviors and career choices of those with epilepsy, such as whether a driving license is revoked or its issuance is limited. In China, the patient of epilepsy is absolutely cannot drive a vehicle, this also is a content that of Road Traffic Safety Law of our country. In many U.S. states, a person must be seize-free for six months to a year to get a driver's license. In the UK, patients need to be symptom-free for at least a year before they can apply for a driving licence. Japan for epilepsy patients to apply for a driver's license requires drug control without disease for two years. So, the definition of epilepsy is crucial for drivers. In order to find out the root cause of the accident, this paper is committed to determining whether the driver had seizures that led to a severe traffic accident. The concept, diagnostic criteria, diagnostic elements, clinical classification and imaging results associated with epilepsy can be used to make a preliminary diagnosis of epileptic seizures. The onset of the disease plays a decisive role in judging the nature of traffic accidents.

Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate.

Basic Principles of Electroencephalogram

Electroencephalogram(EEG)signals are mainly generated by postsynaptic potentials(EPSP)in the apical dendrites of pyramidal cells in the cortex. The mechanism of generation of interictal epileptiform discharges(IED)is paroxysmal depolarization shift(PDS). IEDs are negative towards the cortical surface and positive towards the white matter. To interpret EEG, readers should know normal EEG waveforms of wakefulness and sleep, normal variants such as wicket spikes or small sharp spikes, and artifacts such as eye movements or electromyography. Overinterpretation of EEG is a major cause of misdiagnosis; therefore, the accurate identification of IED is essential. The International Federation of Clinical Neurophysiology has proposed a criteria for the identification of IED. Long-term video-EEG is useful for correct diagnosis and localization of the epileptogenic zone in the setting of presurgical evaluation of intractable epilepsy and is also useful for the differential diagnosis of epileptic seizures and non-epileptic events. Clinical history and symptomatology are of utmost importance for correct diagnosis. Diagnosis should not be made using only EEG findings.

Effective Epileptic Seizure Detection Using Enhanced Salp Swarm Algorithm-based Long Short-Term Memory Network

In this manuscript, a deep learning method is implemented for automatic detection of abnormal and normal Electroencephalogram (EEG) signals, especially for early diagnosis of Epileptic Seizure (ES) disease. Firstly, 8th order Butter Worth Filter (BWF) is applied for removing artifacts from Bern Barcelona (BB), Bonn University (BU), and Temple University Hospital (TUH) datasets. Next, the swarm decomposition method is employed for signal decomposition where it provides a significant frequency localization by separating the essential signal components from the denoised EEG signals. Then, the semantic feature extraction is performed for extracting feature values from the decomposed signals. The feature extraction includes the benefits like reduction in overfitting risk, accuracy improvement, and speed up the training process. Further, the extracted multidimensional feature values are reduced by proposing enhanced Salp Swarm Algorithm (SSA), where it selects optimum feature values for classification. The enhanced SSA uses Whale Optimization Algorithm (WOA) for identifying the potential feature values from the multi-dimensional feature space. Lastly, the selected features are fed to the Long Short Term Memory (LSTM) network for classifying abnormality and normality of seizures. The experimental results show that the enhanced SSA based LSTM network classifies abnormality and normality of seizure with classification accuracy of 97.84%, 99.32%, and 99.20% on the BU, BB, and TUH datasets.

Staring spells in children with autism spectrum disorder: A clinical dilemma.

It is a common occurrence for children with autism spectrum disorder to be diagnosed with staring spells. Staring spells are defined as periods of time when children "space out" and are subcategorized as either "absence seizures" (brain activity resembling a seizure but with no physical seizure symptoms) or "non-epileptic spells" (inattentiveness or daydreaming). Due to the subtle characteristics of staring spells, they are usually diagnosed via long-term video electroencephalogram. The child is monitored for 3-5 days with an electroencephalogram which records brain waves. An electroencephalogram may be difficult to perform in children with autism spectrum disorder due to behavior, cognitive, or sensory concerns. Therefore, we wanted to investigate other clinical characteristics that may help us differentiate between epileptic seizures versus non-epileptic spells in children with autism spectrum disorder presenting with staring spells. We reviewed 140 charts retrospectively from the years of 2010-2021. We abstracted demographic and clinical information from the electronic medical record system and reviewed electroencephalogram videos to group the 140 children into epileptic seizure diagnosis group versus non-epileptic spell group. Of the 140 children in this study, 22 were diagnosed with epileptic seizures and the remaining were diagnosed with non-epileptic spells. We found that the two groups differed in certain clinical characteristics such as how long the staring spells lasted, how many staring spells the child had in 1 week, and whether they responded to verbal commands. We believe that clinical features may be helpful in differentiating epileptic seizures from non-epileptic spells in children with autism spectrum disorder.