Accurate Diagnosis Of Epilepsy Research Articles

Analysis of the expression patterns of AVP, IGF-1, and TNF-α, APP, CD44, IFN-β IFN A β-6, α-syn, and NFL and CLU genes in generalized and focal seizures

ObjectiveThe aim of our study was to investigate the relationship between clinical indicators and gene dysregulation in different types of epilepsy, while also seeking to identify a diagnostic model capable of distinguishing between focal and generalized seizures. This highlights the critical importance of understanding clinical indicators and gene dysregulation for targeted therapeutic interventions to effectively address the specific seizure types effectively. Materials and methodsIn this study, we conducted a comprehensive analysis of the peripheral blood of epilepsy patients (n = 100) and a control group (n = 51) to determine the differential gene expression. Our analysis involved a range of statistical approaches, including correlation analysis to establish the association between clinical indicators and gene dysregulation, and principal component analysis to highlight distinct disease group from control group. Furthermore, we developed diagnostic models using logistic regression to aid in the accurate diagnosis of epilepsy. ResultsAmong several selected genes in this study such as AVP (AUC = 0.832, p < 0.0001), IGF-1 (AUC = 0.658, p = 0.0015), TNF-α (AUC = 0.8970, p < 0.0001), APP (AUC = 0.742, p < 0.0001), CD44 (AUC = 0.614, p = 0.021) and NfL (AUC = 0.937, p < 0.0001), and CLU (AUC = 0.923, p < 0.0001) have shown the outstanding discrimination. In addition to this, when all genes were included in the model, the overall diagnostic power increased significantly (AUC = 0.9968). A differential diagnostic model for focal and generalized seizures was established which discloses AUC = 0.7027, (95 % CL, 0.5765 to 0.8289, p = 0.0019). ConclusionThe conclusions drawn from these findings represented that this is the first study to highlight the distinctive gene patterns of both focal and generalized seizures, implying that peripheral blood can serve as a diagnostic source to distinguish between these seizures types, aiding in the accurate classification of epilepsy. The findings from this study indicate a promising direction for investigating more targeted pharmacological interventions directed to address the distinct needs of both focal and generalized epilepsy, which offers advancements in treatment strategies for distinctive seizure types.

Epilepsy Seizures Classification with EEG Signals: A Machine Learning Approach

Epilepsy is a neurological disorder characterized by recurrent seizures, which can significantly impact a person's life. Early and accurate diagnosis of epilepsy is crucial for effective management and treatment. The traditional methods for diagnosing epilepsy are deemed ineffective and costly. Epilepsy disease detection at an early stage is crucial. Machine learning techniques have shown promise in automating the classification of epilepsy based on various data sources, such as electroencephalogram (EEG) signals, clinical features, and imaging data. This paper presents a machine learning approach to epilepsy disease classification using EEG signal data. We have applied various machine learning models, including Random Forest, XGBoost, GradientBoost, Naive Bayes, Decision Tree, and Extra Tree, with some pre-processing and feature selection techniques. XGBoost achieved 98.93% training accuracy and 98.23% testing accuracy; Gradient Boost achieved 98.40% training and 98.20% testing accuracy; Extra Tree achieved 98.65% training and 97.85% testing accuracy; Random Forest achieved 97.42% training and 96.52% testing accuracy; Decision Tree achieved 92.6% training and 92.4% testing accuracy; Navies Bayes achieved 93.52% training and 92% testing accuracy. The XGBoost classifier achieved the highest accuracy among all other classifiers applied in the proposed research experiment.

Epileptic seizure detection on a compressed EEG signal using energy measurement

Epilepsy is a common neurological disorder affecting both children and adults. It can trigger seizures without any stimuli. An accurate diagnosis of epilepsy is essential to the treatment and management of the patient’s condition. Electroencephalography EEG is a non-invasive technique for the diagnosis of epilepsy. Data analysis and processing can be challenging due to the high dimensionality of EEG signals. In this study, the dimensionality reduction technique using compressive sensing is applied to EEG signals to optimize the process and improve the accuracy of the classification in the epileptic EEG signals. The CS method is applied to minimize processed data by reducing EEG signal dimensions, and energy is calculated on the compressed signal as a feature. The performance of the proposed method is evaluated using SVM with Linear, Quadratic, Cubic, and Radian Basis Function kernels through 5- and 10-fold cross-validation. The results showed 100% accuracy in the classification of ictal and inter-ictal EEG signals with compression ratios of 1/2, 1/4, and 1/8. The results demonstrate that CS is effective in analyzing EEG and diagnosing epilepsy. In addition, this study has significant implications for further development of dimensionality reduction techniques in various EEG-based applications, such as epilepsy classification, sleep stage analysis, and brain–computer interface.

The efficacy of lamotrigine after failure of the first administration of valproate in treating epilepsy: a systematic review and meta-analysis.

Epilepsy is a long-term recurrent chronic brain disease that can cause significant emotional burden to the patient and their family, as well as huge economic costs to society. Timely and accurate diagnosis of epilepsy, together with early and standardized treatments can effectively control seizures and restore the patient's quality of life and reduce the economic burden. This meta-analysis examined the efficacy of lamotrigine administration in patients with epilepsy. A literature search was performed in the PubMed, Embase, and OVID-Medline databases to identify articles related to epilepsy and lamotrigine that were published from the establishment of the database to April 2021. The keywords used for the literature search included "epilepsy", "sodium valproate", "lamotrigine", "effectiveness", and "therapeutic effect". It uses Cochrane review manual 5.3 to evaluate the quality of the included literature and review manager 5.3 software for meta-analysis. A total of 9 studies involving 1,864 patients with epilepsy were included in this meta-analysis. The results revealed that, after treatment failure with the first drug of valproic acid, the total effective rate of lamotrigine treatment had an odds ratio (OR) of 2.21 with a 95% confidence interval (CI) of 1.15 to 4.27 (Z=2.37; P=0.02). The total adverse reaction rate (OR =0.70; 95% CI: 0.55 to 0.88; Z=2.98; P=0.003) and the improvement rate of epilepsy associated with lamotrigine treatment (OR =4.22; 95% CI: 1.00 to 17.84; Z=1.96; P=0.05) were all significantly higher than that of other drug treatments. A total of 9 articles were included in this meta-analysis to examine the efficacy of lamotrigine in the treatment of epilepsy. The clinical efficacy of lamotrigine addition therapy was found to be superior to lamotrigine replacement therapy, and the incidence of adverse reactions was lower than that of lamotrigine replacement therapy. However, due to the low methodological quality of the included literatures, this conclusion should be further verified using large sample and high-quality randomized double-blinded experiments.

Epilepsy Detection Using Artificial Neural Networks

Epilepsy is a neurological disorder, where there is a cluster of brain cells that behave in a hyperexcitable manner, the individual can promote injuries, trauma or, in more severe cases, sudden death. Electroencephalogram (EEG) is the most used way to detect epileptic seizures. Therefore, more simplified methods of analysis of the EEG can help in the diagnosis and treatment of these individuals more quickly. In this study, we extracted pertinent EEG characteristics to assess the epileptic seizure period. We use Perceptron Multilayer artificial neural networks to classify the period of the crisis, obtaining a more efficient diagnosis. The multilayer neural network obtained an accuracy of 98%. Thus, the strategy of extracting characteristics and the architecture of the assigned network were sufficient for a rapid and accurate diagnosis of epilepsy.

Antiepileptic Drug Therapy in Patients with Drug-Resistant Epilepsy.

Antiepileptic drug (AED) therapy starts with an accurate diagnosis of epilepsy and is followed by sequential drug trials. Seizure freedom is largely achieved by the first two drug trials; thus, epilepsy that cannot be controlled after appropriately conducted trials of the first two drugs is defined as drug-resistant epilepsy (DRE). It is still unclear which mode of pharmacotherapy, among monotherapy and polytherapy, shows better outcomes in cases of DRE. However, in a recent large hospital cohort study over past two decades, combination therapy was associated with a progressive increase in seizure-free rate than monotherapy in DRE. The benefits of polytherapy in the management of DRE might be related to the recent introduction of many new AEDs with different and novel mechanisms of action and better pharmacokinetic and tolerability profiles. These new AEDs were introduced to the market after they have proven their superiority over placebos in randomized controlled trials (RCTs) on add-on therapy in patients with DRE. Therefore, polytherapy including these new AEDs in the regimen is the approved mode of treatment for cases of DRE; this has prompted physicians to try various combinations of polytherapy to optimize the clinical outcomes. In addition, the significant discrepancies in AED responder rates between RCTs and real-world practice may support the importance of judicious use of new drugs in polytherapy by experienced epileptologists. Most experts now agree to the concept of “rational polytherapy” consisting of mechanistic combinations of AEDs exerting synergistic interactions and to the importance of continuing trials of different rational polytherapy regimens to improve the outcome of the core population of epilepsy patients in the long term.

P03-F Early care protocol for patients with a first epileptic seizure in the Complejo Hospitalario Toledo

Rationale The early and accurate diagnosis of epilepsy is essential. After a first seizure, it is important to detect which patients are at special risk of having a second crisis. An expert assessment and an early EEG improves the diagnostic yield, increases the therapeutic compliance and reduces the adverse effects due to unnecessary treatments. Methods The epilepsy Team of the Hospital Virgen de La Salud of Toledo, has implemented an early first seizure assessment consisting of three main steps: 1. Detection of the first unprovoked crisis in the Emergency Department, filling in a specific form and assessment and registration by the on-call Neurologist. 2. Case analysis, each morning, by the Epilepsy Team and contact with the Clinical Neurophysiologists to perform early EEG within 24–48 h. 3. Early First Seizure clinic within 1 week by the Epileptologists. Results After six months, 60 patients have been included, with a diagnosis of epilepsy in 27. 17 were focal epilepsies and 10 generalized epilepsies. EEG was performed in all patients within 24–72 h and EEG after sleep deprivation in 63%. All patients have had a neuroimaging test, 100% CT and 60% MRI. Conclusions In our experience, the EEG wtihin the first 48 h after a first seizure is essential for the epilepsy diagnosis.

Evaluation of serum matrix metalloproteinase-9 as a potential biomarker for diagnosis of epilepsy in children

Introduction Epilepsy is a common serious neurological disorder. Medical histories and electroencephalogram are not always sufficient for epilepsy diagnosis; therefore, exploring the novel methods for the accurate diagnosis of epilepsy is of great importance. Matrix metalloproteinase (MMP) are vital drivers of synaptic remodeling in health and disease. It plays a well described role in neuroinflammation; it is suggested that at early stages of epileptogenesis, inhibition of MMP may help ameliorate cell death, aberrant network rewiring, and neuroinflammation and prevent development of epilepsy. Aim MMPs especially MMP-9 and MMP-2 are sensitive to seizures; thus, MMP-9 may be a potential biomarker for epilepsy diagnosis, we assessed MMP-9 levels in the context of its possible use as a biomarker of the activity and diagnosis of epilepsy and evaluation of the nutritional state by anthropometric measurement. Patients and methods Thirty epileptic patients aged from 4 to 17 years, selected randomly from the inpatient and outpatient neurological unit of pediatric department in Alzhraa University Hospital between 2018 and 2019 were subjected to: etailed medical history, general and neurological examination, routine laboratory studies, electroencephalogram, anthropometric measurement, and serum MMP-9 level measurement. Thirty age-matched and sex-matched healthy controls were selected and their serum MMP-9 and anthropometric measurements were obtained. Results Serum MMP-9 levels were statistically significantly high in patients with idiopathic epilepsy compared with the control group. Sensitivity of MMP-9 as biomarkers was 62.07% with 96.67% specificity. The cutoff point of MMP-9 more than 4. Conclusion MMP-9 levels increased in patients with epilepsy. This tool may be a helpful diagnostic biomarker for epilepsy with good sensitivity and specificity.

Epileptic Seizure Detection Based on EEG Signals and CNN.

Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. While electroencephalography (EEG) plays important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, an expert is needed to analyze all EEG recordings to detect epileptic activity. This method is obviously time-consuming and tedious, and a timely and accurate diagnosis of epilepsy is essential to initiate antiepileptic drug therapy and subsequently reduce the risk of future seizures and seizure-related complications. In this study, a convolutional neural network (CNN) based on raw EEG signals instead of manual feature extraction was used to distinguish ictal, preictal, and interictal segments for epileptic seizure detection. We compared the performances of time and frequency domain signals in the detection of epileptic signals based on the intracranial Freiburg and scalp CHB-MIT databases to explore the potential of these parameters. Three types of experiments involving two binary classification problems (interictal vs. preictal and interictal vs. ictal) and one three-class problem (interictal vs. preictal vs. ictal) were conducted to explore the feasibility of this method. Using frequency domain signals in the Freiburg database, average accuracies of 96.7, 95.4, and 92.3% were obtained for the three experiments, while the average accuracies for detection in the CHB-MIT database were 95.6, 97.5, and 93% in the three experiments. Using time domain signals in the Freiburg database, the average accuracies were 91.1, 83.8, and 85.1% in the three experiments, while the signal detection accuracies in the CHB-MIT database were only 59.5, 62.3, and 47.9% in the three experiments. Based on these results, the three cases are effectively detected using frequency domain signals. However, the effective identification of the three cases using time domain signals as input samples is achieved for only some patients. Overall, the classification accuracies of frequency domain signals are significantly increased compared to time domain signals. In addition, frequency domain signals have greater potential than time domain signals for CNN applications.

A novel robust diagnostic model to detect seizures in electroencephalography

Identifying seizure patterns in complex electroencephalography (EEG) through visual inspection is often challenging, time-consuming and prone to errors. These problems have motivated the development of various automated seizure detection systems that can aid neurophysiologists in accurate diagnosis of epilepsy. The present study is focused on the development of a robust automated system for classification against low levels of supervised training. EEG data from two different repositories are considered for analysis and validation of the proposed system. The signals are decomposed into time-frequency sub-bands till sixth level using dual-tree complex wavelet transform (DTCWT). All details and last approximation coefficients are used to calculate features viz. energy, standard deviation, root-mean-square, Shannon entropy, mean values and maximum peaks. These feature sets are passed through a general regression neural network (GRNN) for classification with K-fold cross-validation scheme under varying train-to-test ratios. The current model yields ceiling level classification performance (accuracy, sensitivity & specificity) in all combinations of datasets (ictal vs non-ictal) in less than 0.028s. The proposed scheme will not only maximize hit-rate and correct rejection rate but also will aid neurophysiologists in the fast and accurate diagnosis of seizure onset.

Detection and Cortical Localization of Ictal Signatures using Electroencephalogram Signals

Event Abstract Back to Event Detection and Cortical Localization of Ictal Signatures using Electroencephalogram Signals PIYUSH SWAMI1, 2*, Bijaya K. Panigrahi3, Sneh Anand1, 2, Manvir Bhatia4, Manjari Tripathi5 and Tapan K. Gandhi3 1 Indian Institute of Technology Delhi, Centre for Biomedical Engineering, India 2 All India Institute of Medical Sciences, Biomedical Engineering Unit, India 3 Indian Institute of Technology Delhi, Department of Electrical Engineering, India 4 Saket City Hospital, Department of Neurology, India 5 All India Institute of Medical Sciences, Department of Neurology, India Epilepsy is a common brain disorder which affects nearly 65 million people in the world. Electroencephalography is the most important technique for the diagnosis of epilepsy till date. The ictal signatures present in electroencephalogram (EEG) signals are usually detected by visual examination by the neuro-clinician or neuro-physiologist. This is an extremely tedious and error prone work since, the EEG signals are contaminated with various artifacts. Further, locating the epicenter of seizures is even more challenging due to the non-stationary nature of EEG. Hence, application of expert models for detection and source localization of ictal signatures is extremely important. In this work, we have proposed a novel methodology for detecting ictal signatures in EEG using complex wavelet transform and fuzzy-Sugeno classifier. The minimum reduction and maximum relevance technique was applied for feature selection. The results showed ceiling level of accuracy and coherent statistical performances. The classified ictal periods were used for the performing independent component analysis and subsequently cortical source localization over the Montreal Neurological Institute head model. The outcomes stamp successful seizure detection and visualization of the ictal events over the human cortex. This development assists in fast and accurate diagnosis of epilepsy and can be of utmost use in developing and under-developed countries where the patients to neurologists’ ratio is phenomenally higher. Additionally, the cortical location of the epileptic seizures helps in predicting the progression of pharmacological treatment and possible surgical outcomes. Figure 1

Principles of antiepileptic drug treatment of epilepsy.

For the successful treatment of epilepsy, accurate diagnosis of epilepsy and epileptic seizures, and proper selection of antiepileptic drugs (AED) according to the classification of epileptic syndromes are fundamentally important. Efficacy of AED treatment, however, depends not only on its pharmacological action but also on its efficient use, namely a rationally thinking tailor-made treatment considering the characteristics of each patients, i.e. individual differences in pharmacokinetics, factors influencing AED concentrations, AED interactions, and comprehensively their life style and psychosocial factors.