Clinical Electroencephalogram Research Articles

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifying long multivariate time series, optimal prediction models and feature extraction methods for EEG classification remain elusive. Our study addressed the problem of EEG classification under the framework of brain age prediction, applying a deep learning model on EEG time series. We hypothesized that decomposing EEG signals into oscillatory modes would yield more accurate age predictions than using raw or canonically frequency-filtered EEG. Specifically, we employed multivariate intrinsic mode functions (MIMFs), an empirical mode decomposition (EMD) variant based on multivariate iterative filtering (MIF), with a convolutional neural network (CNN) model. Testing a large dataset of routine clinical EEG scans (n = 6540) from patients aged 1 to 103 years, we found that an ad-hoc CNN model without fine-tuning could reasonably predict brain age from EEGs. Crucially, MIMF decomposition significantly improved performance compared to canonical brain rhythms (from delta to lower gamma oscillations). Our approach achieved a mean absolute error (MAE) of 13.76 ± 0.33 and a correlation coefficient of 0.64 ± 0.01 in brain age prediction over the entire lifespan. Our findings indicate that CNN models applied to EEGs, preserving their original temporal structure, remains a promising framework for EEG classification, wherein the adaptive signal decompositions such as the MIF can enhance CNN models' performance in this task.

Automated diagnosis of EEG abnormalities with different classification techniques.

Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs) are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high variance in epileptic seizures among patients, and limited clinical data constraints. To overcome these challenges, this paper presents two approaches for EEG signal classification. One of these approaches depends on Machine Learning (ML) tools. The used features are different types of entropy, higher-order statistics, and sub-band energies in the Hilbert Marginal Spectrum (HMS) domain. The classification is performed using Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN) classifiers. Both seizure detection and prediction scenarios are considered. The second approach depends on spectrograms of EEG signal segments and a Convolutional Neural Network (CNN)-based residual learning model. We use 10000 spectrogram images for each class. In this approach, it is possible to perform both seizure detection and prediction in addition to a 3-state classification scenario. Both approaches are evaluated on the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) dataset, which contains 24 EEG recordings for 6 males and 18 females. The results obtained for the HMS-based model showed an accuracy of 100%. The CNN-based model achieved accuracies of 97.66%, 95.59%, and 94.51% for Seizure (S) versus Pre-Seizure (PS), Non-Seizure (NS) versus S, and NS versus S versus PS classes, respectively. These results demonstrate that the proposed approaches can be effectively used for seizure detection and prediction. They outperform the state-of-the-art techniques for automatic seizure detection and prediction. Block diagram of proposed epileptic seizure detection method using HMS with different classifiers.

Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.

Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793-0.852 and accuracies of 0.786-0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.

Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study.

This study was undertaken to develop a multimodal machine learning (ML) approach for predicting incident depression in adults with epilepsy. We randomly selected 200 patients from the Calgary Comprehensive Epilepsy Program registry and linked their registry-based clinical data to their first-available clinical electroencephalogram (EEG) and magnetic resonance imaging (MRI) study. We excluded patients with a clinical or Neurological Disorders Depression Inventory for Epilepsy (NDDI-E)-based diagnosis of major depression at baseline. The NDDI-E was used to detect incident depression over a median of 2.4 years of follow-up (interquartile range [IQR] = 1.5-3.3 years). A ReliefF algorithm was applied to clinical as well as quantitative EEG and MRI parameters for feature selection. Six ML algorithms were trained and tested using stratified threefold cross-validation. Multiple metrics were used to assess model performances. Of 200 patients, 150 had EEG and MRI data of sufficient quality for ML, of whom 59 were excluded due to prevalent depression. Therefore, 91 patients (41 women) were included, with a median age of 29 (IQR = 22-44) years. A total of 42 features were selected by ReliefF, none of which was a quantitative MRI or EEG variable. All models had a sensitivity > 80%, and five of six had an F1 score ≥ .72. A multilayer perceptron model had the highest F1 score (median = .74, IQR = .71-.78) and sensitivity (84.3%). Median area under the receiver operating characteristic curve and normalized Matthews correlation coefficient were .70 (IQR = .64-.78) and .57 (IQR = .50-.65), respectively. Multimodal ML using baseline features can predict incident depression in this population. Our pilot models demonstrated high accuracy for depression prediction. However, overall performance and calibration can be improved. This model has promise for identifying those at risk for incident depression during follow-up, although efforts to refine it in larger populations along with external validation are required.

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence

Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed. To develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG-Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse. In this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded. Diagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients' habitual clinical episodes obtained during video-EEG recording. The characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models (P < .001) and similar to human experts. In this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.

Concurrent recordings of slow DC-potentials and epileptiform discharges: Novel EEG amplifier and signal processing techniques

Ionic currents within the brain generate voltage oscillations. These bioelectrical activities include ultra-low frequency electroencephalograms (DC-EEG, frequency less than 0.1 Hz) and conventional clinical electroencephalograms (AC-EEG, 0.5–70 Hz). Although AC-EEG is commonly used for diagnosing epilepsy, recent studies indicate that DC-EEG is an essential frequency component of EEG and can provide valuable information for analyzing epileptiform discharges. During conventional EEG recordings, DC-EEG is censored by applying high-pass filtering to i) obliterate slow-wave artifacts, ii) eliminate the bioelectrodes' half-cell potential asymmetrical changes in ultralow-low frequency, and iii) prevent instrument saturation. Spreading depression (SD), which is the most prolonged fluctuation in DC-EEG, may be associated with epileptiform discharges. However, recording of SD signals from the scalp's surface can be challenging due to the filtering effect and non-neuronal slow shift potentials. In this study, we describe a novel technique to extend the frequency bandwidth of surface EEG to record SD signals. The method includes novel instrumentation, appropriate bioelectrodes, and efficient signal-processing techniques. To evaluate the accuracy of our approach, we performed a simultaneous surface recording of DC- and AC-EEG from epileptic patients during long-term video EEG monitoring, which provide a promising tool for diagnosis of epilepsy. Data Availability StatementThe data presented in this study are available on request.

Byte-Pair Encoding for classifying routine clinical electroencephalograms in adults over the lifespan.

Routine clinical EEG is a standard test used for the neurological evaluation of patients. A trained specialist interprets EEG recordings and classifies them into clinical categories. Given time demands and high inter-reader variability, there is an opportunity to facilitate the evaluation process by providing decision support tools that can classify EEG recordings automatically. Classifying clinical EEG is associated with several challenges: classification models are expected to be interpretable; EEGs vary in duration and EEGs are recorded by multiple technicians operating various devices. Our study aimed to test and validate a framework for EEG classification which satisfies these requirements by transforming EEG into unstructured text. We considered a highly heterogeneous and extensive sample of routine clinical EEGs (n = 5785), with a wide range of participants aged between 15 and 99 years. EEG scans were recorded at a public hospital, according to 10/20 electrode positioning with 20 electrodes. The proposed framework was based on symbolizing EEG signals and adapting a previously proposed method from natural language processing (NLP) to break symbols into words. Specifically, we symbolized the multichannel EEG time series and applied a byte-pair encoding (BPE) algorithm to extract a dictionary of the most frequent patterns (tokens) reflecting the variability of EEG waveforms. To demonstrate the performance of our framework, we used newly-reconstructed EEG features to predict patients' biological age with a Random Forest regression model. This age prediction model achieved a mean absolute error of 15.7 years. We also correlated tokens' occurrence frequencies with age. The highest correlations between the frequencies of tokens and age were observed at frontal and occipital EEG channels. Our findings demonstrated the feasibility of applying an NLP-based approach to classifying routine clinical EEG. Notably, the proposed algorithm could be instrumental in classifying clinical EEG with minimal preprocessing and identifying clinically-relevant short events, such as epileptic spikes.

Textural feature of EEG signals as a new biomarker of reward processing in Parkinson’s disease detection

BackgroundParkinson’s disease (PD) detection holds great potential for providing effective treatments, slowing the disease process, and improving the quality of patient’s life, but the development of a clinical accurate, generalized, robust and cost-effective method is a challenge. MethodIn this paper, a novel PD detection method based on textural features of clinical electroencephalogram (EEG) signals has been proposed. In contrast to most existing methods, which do not consider reward positivity (RP)-relevant features for automatic PD detection, this method has focused on providing a novel EEG marker of RP using an enhanced time–frequency representation, texture descriptors based on Gray Level Co-occurrence Matrix, local binary pattern, and sparse coding classifier. ResultsThe proposed method has been evaluated using EEG signals recorded during a reinforcement-learning task from 28 patients with PD and 28 sex- and age matched healthy controls. Results have demonstrated that the proposed architecture reaches a high detection with an average accuracy rate of 100%, presenting better performance and outperforming previous techniques. Conclusionsit can provide a new solution to detect RP changes in PD and can offer obvious stability advantages on several clinical and technical variables (medication states, type of textural descriptors, reduced channels), suggesting a generalizable detection system.

Clinical features, electroencephalogram, and biomarkers in pediatric sepsis-associated encephalopathy

To date, no specific diagnostic criteria for sepsis-associated encephalopathy (SAE) have been established. We studied 33 pediatric patients with sepsis prospectively and evaluated the level of consciousness, the presence of delirium, electroencephalographic (EEG) findings, and plasma levels of neuron-specific enolase and S100-calcium-binding protein-B. A presumptive diagnosis of SAE was primarily considered in the presence of a decreased level of consciousness and/or delirium (clinical criteria), but specific EEG abnormalities were also considered (EEG criteria). The time course of the biomarkers was compared between groups with and without clinical or EEG criteria. The Functional Status Scale (FSS) was assessed at admission, discharge, and 3–6 months post-discharge. Clinical criteria were identified in 75.8% of patients, EEG criteria in 26.9%, both in 23.1%, and none in 23.1%. Biomarkers did not differ between groups. Three patients had an abnormal FSS at discharge, but no one on follow-up. A definitive diagnostic pattern for SAE remained unclear. Clinical criteria should be the basis for diagnosis, but sedation may be a significant confounder, also affecting EEG interpretation. The role of biomarkers requires a better definition. The diagnosis of SAE in pediatric patients remains a major challenge. New consensual diagnostic definitions and mainly prognostic studies are needed.

Case report of anti-NMDA receptor encephalitis in a 24-year-old female: an uncommon presentation

BackgroundAnti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is a form of autoimmune encephalitis. Due to the variability of the initial symptoms, anti-NMDAR encephalitis is not only underdiagnosed but also can be misdiagnosed as viral encephalitis or other pathologies. The origin of this disease is often paraneoplastic. Anti-NMDAR encephalitis preferentially affects children and young adults, and it has a male/female ratio of 1/4. In case of clinical suspicion, electroencephalogram and brain magnetic resonance imaging are useful, but lumbar puncture for cerebrospinal fluid analysis is used to confirm the diagnosis. Treatment for this disease includes immunosuppression and tumour resection when indicated.Case presentationWe report the case of a 24-year-old female admitted to the emergency room following the onset of acute confusion. Due to the rapid deterioration of consciousness, swallowing disorders, respiratory failure and severe bradycardia the patient was intubated. On day 23 after presentation, brain magnetic resonance suggested autoimmune limbic encephalitis. Cerebrospinal fluid results were positive for anti-NMDA antibodies. After IV methylprednisolone and plasmapheresis and a second line therapy with corticosteroid and mycophenolic acid, the patient’s clinical condition gradually improved.ConclusionsAnti-NMDAR encephalitis typically occurs in young patients with no history of acute psychiatric symptoms. The possibility of this pathology should be taken into account before diagnosing a patient with a psychiatric illness.

Neurological management and work-up of neurotoxicity associated with CAR T cell therapy

IntroductionTreatment with CD19 chimeric antigen receptor (CAR) T cells is an innovative therapeutic approach for patients with relapsed/refractory diffuse large B cell lymphoma (r/rDLBCL) and B-lineage acute lymphoblastic leukemia (r/rALL). However, convincing therapeutic response rates can be accompanied by cytokine release syndrome (CRS) and severe neurotoxicity termed immune effector cell-associated neurotoxicity syndrome (ICANS).MethodsSingle center, prospective observational study of fifteen consecutive r/r DLBCL patients treated with Tisagenlecleucel within 1 year at Hannover Medical School. Extensive neurological work-up prior to CAR T cell infusion included clinical examination, cognitive testing (Montreal-Cognitive-Assessment), brain MRI, electroencephalogram, electroneurography, and analysis of cerebrospinal fluid. After CAR T cell infusion, patients were neurologically examined for 10 consecutive days. Afterwards, all patients were assessed at least once a week.ResultsICANS occurred in 4/15 patients (27%) within 6 days (4–6 days) after CAR T cell infusion. Patients with ICANS grade 2 (n = 3) exhibited similar neurological symptoms including apraxia, expressive aphasia, disorientation, and hallucinations, while brain MRI was inconspicuous in either case. Treatment with dexamethasone rapidly resolved the clinical symptoms in all three patients. Regarding baseline parameters prior to CAR T cell treatment, patients with and without ICANS did not differ.ConclusionsIn our cohort, ICANS occurred in only every fourth patient and rather low grade neurotoxicity was found during daily examination. Our results demonstrate that a structured neurological baseline examination and close monitoring are helpful to detect CAR T cell related neurotoxicity already at an early stage and to potentially prevent higher grade neurotoxicity.

Temporal Onset Focal Seizures Induced by Intermittent Photic Stimulation.

The study aimed to review the clinical, radiological, and pathological findings and electroencephalogram (EEG) of pediatric epilepsy patients with temporal onset focal seizures induced by intermittent photic stimulation (IPS). Four patients with temporal onset photosensitivity focal seizures were analyzed. Three (75%) of the four patients were female. The average age of seizure-onset was 4.4 years. The interictal EEG showed both generalized and focal spike and waves in one patient and focal or multifocal spike and waves alone in three patients. Photoparoxysmal response (PPR) was evoked in all patients and showed generalized discharges (patients 2–4), both generalized and posterior discharges (patient 1). Both generalized and focal discharges could coexist in interictal discharges and PPR. The sensitive frequencies of PPR and photoconvulsive response (PCR) were 12–30 and 10–16 Hz, respectively, which were close to the occipital rhythm. In all patients, the onset of PCR was recorded, namely, the left anterior and mesial temporal lobe (TL), the left posterior TL, and the whole left TL, which showed two forms: the seizure of two patients was the onset of slow waves in the temporal area without spreading generalized discharges (patients 1 and 4), and the other one was fast rhythmic activities in the temporal area, spreading to the occipital area or gradually evolving into the generalized discharges (patients 2 and 3). During follow-up, except for patient 3 who had occasional seizures, the seizures of the remaining patients were under control. Temporal onset focal seizures could be induced by IPS. Temporal onset photosensitivity seizures were mostly easy to control with antiseizure drugs.

Epilepsy EEG Classification Based on Convolution Support Vector Machine

Clinical Electroencephalogram (EEG) data is of great significance to realize automatable detection, recognition and diagnosis to reduce the valuable diagnosis time. To make a classification of epilepsy, we constructed convolution support vector machine (CSVM) by integrating the advantages of convolutional neural networks (CNN) and support vector machine (SVM). To distinguish the focal and non-focal epilepsy EEG signals, we firstly reduced the dimensionality of EEG signals by using principal component analysis (PCA). After that, we classified the epilepsy EEG signals by the CSVM. The accuracy, sensitivity and specificity of our method reach up to 99.56%, 99.72% and 99.52% respectively, which are competitive than the widely acceptable algorithms. The proposed automatic end to end epilepsy EEG signals classification algorithm provides a better reference for clinical epilepsy diagnosis.

Seizure localization using EEG analytical signals

ObjectiveLocalization of epileptic seizures, usually characterized by abnormal hypersynchronous wave patterns from the cortex, remains elusive. We present a novel, robust method for automatic localization of seizures on the scalp from clinical electroencephalogram (EEG) data. MethodsSeizure patient EEG data was decomposed via the Hilbert Transform and processed through the following methodology: sorting the analytic amplitude (AA) in the time instance, locating the maximum amplitude within the vector of channels, cross-correlating amplitude values in the time index with the channel vector. The channel with highest AA value in time was located. ResultsOur approach provides an automated way to isolate the epi-genesis of seizure events with 93.3% precision and 100% sensitivity. The method differentiates seizure-related neural activity from other common EEG noise artifacts (e.g., blinks, myogenic noise). ConclusionsWe evaluated performance characteristics of our source location methodology utilizing both phase and energy of EEG signals from patients who exhibited seizure events. Feasibility of the new algorithm is demonstrated and confirmed. SignificanceThe proposed method contributes to high-performance scalp localization for seizure events that is more straightforward and less computationally intensive than other methods (e.g., inverse source modeling). Ultimately, it may aid clinicians in providing improved patient diagnosis.

Convolutional neural network for detection and classification of seizures in clinical data.

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data. Graphical abstract.

Ictal Inhibition: Sync Globally, Slack Locally.

Role of Paroxysmal Depolarization in Focal Seizure Activity Tryba AK, Merricks EM, Lee S, et al. J Neuroph. 2019;122(5):1861-1873. doi.org/10.1152/jn.00392.2019We analyze the role of inhibition in sustaining focal epileptic seizure activity. We review ongoing seizure activity at the mesoscopic scale that can be observed with microelectrode arrays as well as at the macroscale of standard clinical electroencephalogram. We provide clinical, experimental, and modeling data to support the hypothesis that paroxysmal depolarization (PD) is a critical component of the ictal machinery. We present dual-patch recordings in cortical cultures showing reduced synaptic transmission associated with presynaptic occurrence of PD, and we find that the PD threshold is cell size related. We further find evidence that optically evoked PD activity in parvalbumin neurons can promote propagation of neuronal excitation in neocortical networks in vitro. Spike sorting results from microelectrode array measurements around ictal wave propagation in human focal seizures demonstrate a strong increase in putative inhibitory firing with an approaching excitatory wave, followed by a sudden reduction of firing at passage. At the macroscopic level, we summarize evidence that this excitatory ictal wave activity is strongly correlated with oscillatory activity across a centimeter-sized cortical network. We summarize Wilson–Cowan-type modeling showing how inhibitory function is crucial for this behavior. Our findings motivated us to develop a network motif of neurons in silico, governed by a reduced version of the Hodgkin–Huxley formalism, to show how feedforward, feedback, PD, and local failure of inhibition contribute to observed dynamics across network scales. The presented multidisciplinary evidence suggests that the PD not only is a cellular marker or epiphenomenon but actively contributes to seizure activity. NEW & NOTEWORTHY: We present mechanisms of ongoing focal seizures across meso- and macroscales of microelectrode array and standard clinical recordings, respectively. We find modeling, experimental, and clinical evidence for a dual role of inhibition across these scales: local failure of inhibition allows propagation of a mesoscopic ictal wave, whereas inhibition elsewhere remains intact and sustains macroscopic oscillatory activity. We present evidence for PD as a mechanism behind this dual role of inhibition in shaping ictal activity.

Epileptic seizure detection using EEG signals and extreme gradient boosting.

The problem of automated seizure detection is treated using clinical electroencephalograms (EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus (TUSZ). Performances on this complex data set are still not encountering expectations. The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances. Two methods are explored: a standard partitioning on a recent and larger version of the TUSZ, and a leave-one-out approach used to increase the amount of data for the training set. XGBoost, a fast implementation of the gradient boosting classifier, is the ideal algorithm for these tasks. The performances obtained are in the range of what is reported until now in the literature with deep learning models. We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types. We show that generalized seizures tend to be far better predicted than focal ones. We also notice that some EEG channels and features are more important than others to distinguish seizure from background.

Early clinical and EEG findings associated with the outcome in childhood absence epilepsy

ObjectivesThe objective of this study was to investigate several clinical electroencephalogram (EEG) findings possibly predicting the early response to antiepileptic drugs (AEDs) and the late outcome in children with clinical EEG features fitting the syndromic diagnosis of childhood absence epilepsy (CAE). MethodsIn 117 untreated patients with typical absences, we analyzed clinical EEG features, and resting EEG activity using partial directed coherence to calculate out- and inflow of cortical oscillations in different regions of interest. ResultsAbsences began before 4 years in 12.0%, at 4–9.5 years in 71.8%, and at 10–13 years in 16.2% of the cases. Valproate was started in 91 patients and ethosuximide in 27. With one of AEDs, 77.8% reached seizure control, while the remaining patients needed to switch to the alternative AED. Only 5.9% patients remained drug-resistant. Absences with simple automatisms were the only feature associated with a lack of response to the first AED. Connectivity analysis of resting EEGs showed increased frontal outflow in patients compared with controls, which was significantly greater in the nonresponders to the first AED than in responders.Among the 91 patients followed for 61.2 ± 31.7 months, 14.2% relapsed after a seizure-free period, without differences between the responders to the first or second AED. ConclusionsThe assessment of electroclinical features provided only minimal prognostic indices. The enhanced outflow of frontal oscillations suggests a circuitry dysfunction significantly greater in the nonresponder to the early treatment.Seizure relapses were rare and comparable in patients who reached seizure freedom with first or second AED, indicating that the resistance to one AED does not influence the outcome.

Clinical treatment of epilepsy caused by AIDS-related encephalopathy

Objective To explore the clinical characteristics, diagnostic method of epilepsy caused by acquired immunodeficiency syndrome(AIDS)-related encephalopathy and the value of surgical treatment. Methods A retrospective analysis was conducted on 12 patients with epilepsy caused by AIDS-related encephalopathy admitted to Department of Neurosurgery, Beijing Ditan Hospital, Capital Medical University from June 2012 to January 2018. Of the 12 patients, 4 had intracranial lymphoma, 4 had intracranial tuberculoma, 2 had cryptococcal meningitis with intracranial hypertension and 2 had Toxoplasma encephalopathy. Three of them had intracranial multiple occupancies. Ten patients underwent pathological excision and 2 of them with hydrocephalus underwent ventriculo-peritoneal shunt simultaneously. Twelve patients received regular drug therapy and highly active antiretroviral therapy treatment before and after operation. Results The symptoms of epilepsy were significantly improved after 6 months to 2 years of follow-up except for 1 case who died of brain edema on the 6th day after operation. Among them, Engel grade Ⅰ was achieved in 7 cases, Engel grade Ⅱ in 3, Engel grade Ⅲ in 1. Conclusions The seizure types and pathogenic factors of AIDS-related epilepsy patients are complex and diverse. Comprehensive treatment should be carried out according to clinical manifestations, laboratory examinations, imaging characteristics, electroencephalogram and medical treatment. Under the condition of full preoperative evaluation and mastery of indications, surgery could effectively control AIDS-related epilepsy. Key words: Acquired immunodeficiency syndrome; Epilepsy; Neurosurgical procedures; Treatment outcome; Clinical features

NEUROPSYCHOLOGICAL STATUS OF PATIENTS WITH EPILEPSY AND CLINICAL MANIFESTATIONS OF CRANIOCEREBRAL DISPROPORATION ON THE BACKGROUND OF MODIFIED THERAPY

In the course of the study, 124 case records of patients with epileptic seizures (cryptogenic epilepsy) were analyzed. Diagnosis was established based on a typical clinical presentation, anamnesis, electroencephalogram (EEG) dynamics, and/or video EEG monitoring in the 10-hour format. Group I comprised 36 patients with normal liquor-cranial indexes ranging from 1.6 to 3.6 with an average value of 2.1±0.2; these patients received standard multicomponent therapy during the hospitalization and at the outpatient-polyclinic stage. Group II comprised 42 patients who had liquor-cranial indexes below the lower limit of normal ranging from 0.8 to 1.2 with an average value of 1.0±0.1; these patients also received therapy for primary diagnosis according to standard therapy scheme, which was identical to that in group I. Patients of group III (main group, n=46) had liquor-cranial indexes below physiological norm (from 0.8 to 1.2 with an average value of 0.9±0.1) and received modified therapy. Two-year follow up study showed that more severe cognitive deficit developed in patients with low cerebrovascular indexes receiving standard therapy compared to patients with low cerebrovascular indexes who received modified therapy. The absence of significant differences between patients of group I and III suggest that timely correction of increased intracranial pressure allowed to prevent significant aggravation of pathopsychological characteristics, loss of short-term and long-term memory, and overall cognitive function decline.