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1.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4248-4251, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018934

RESUMO

Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and reducing the false-alarm rate by 34.7%. We also evaluate the battery lifetime of the wearable system including our proposed methodology and demonstrate the feasibility of using it in real time for up to 40.87 hours on a single battery charge.


Assuntos
Epilepsia , Dispositivos Eletrônicos Vestíveis , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Qualidade de Vida , Convulsões/diagnóstico
3.
Zh Nevrol Psikhiatr Im S S Korsakova ; 120(9. Vyp. 2): 68-73, 2020.
Artigo em Russo | MEDLINE | ID: mdl-33076648

RESUMO

OBJECTIVE: To summarize published data on the prevalence, characteristics and diagnostic criteria of sleep disorders in epilepsy. MATERIAL AND METHODS: A search of published articles was performed in Medline (Pubmed), Scopus, Web of Science and e-library databases. RESULTS: Epidemiologic, clinical and diagnostic aspects of excessive daytime sleepiness, obstructive sleep apnea and central apnea, restless leg syndrome and parasomnias related to slow-wave and REM-sleep in patients with epilepsy were analyzed. CONCLUSIONS: Further studies are needed to gain an insight into the complex associations of sleep disorders in epilepsy to optimize diagnostic and treatment approaches and to improve the quality of life in that patient population.


Assuntos
Epilepsia , Parassonias , Transtornos do Sono-Vigília , Epilepsia/diagnóstico , Epilepsia/epidemiologia , Humanos , Parassonias/diagnóstico , Parassonias/epidemiologia , Qualidade de Vida , Sono , Transtornos do Sono-Vigília/diagnóstico , Transtornos do Sono-Vigília/epidemiologia , Transtornos do Sono-Vigília/etiologia
4.
Zh Nevrol Psikhiatr Im S S Korsakova ; 120(8. Vyp. 2): 10-16, 2020.
Artigo em Russo | MEDLINE | ID: mdl-33016671

RESUMO

OBJECTIVE: To determine the incidence of non-convulsive status epilepticus, epileptiform activity, rhythmic and periodic patterns in patients with acute stroke. MATERIAL AND METHOD: An analysis of electroencephalography (EEG) in 86 stroke patients in the neurointensive care unit of the tertiary medical center was performed. Criteria for starting EEG recording were epileptic seizures or clinical suspicion of uncontrolled epileptic status. The ictal-interictal continuum biomarkers and the diagnostic value of EEG for prediction of survival and recovery were assessed. RESULTS: Pathological changes on EEG were recorded in 84% of patients. These patients showed the absence of the dominant occipital rhythm (66%) and hemispheric slowing (42%). Diffuse slowing below the theta range was observed in 41% of patients. EEG reactivity was absent in 20%. Sporadic epileptiform discharges were recorded in 36% of patients and rhythmic and periodic patterns in 26%. Reliable predictors of the unfavorable outcome were the absence of dominant occipital rhythm, lack of reactivity, and low amplitude of the background EEG. No association between the recording of epileptiform activity and the probability of death was shown. CONCLUSION: The most useful EEG biomarkers for predicting survival are amplitude, dominant frequency of background EEG activity and reactivity to external stimulus. Sporadic epileptiform discharges, rhythmic, and periodic patterns are not mandatory associated with a negative prognosis in stroke patients.


Assuntos
Epilepsia , Estado Epiléptico , Acidente Vascular Cerebral/diagnóstico , Eletroencefalografia , Humanos , Convulsões
5.
Yakugaku Zasshi ; 140(10): 1199-1206, 2020.
Artigo em Japonês | MEDLINE | ID: mdl-32999198

RESUMO

Potential risks to the fetus or infant should be considered prior to medication during pregnancy and lactation. It is essential to evaluate the exposure levels of drugs and their related factors in addition to toxicological effects. Epilepsy is one of the most common neurological complications in pregnancy; some women continue to use antiepileptic drugs (AEDs) to control seizures. Benzodiazepines (BZDs) are widely prescribed for several women who experience symptoms such as anxiety and insomnia during the postpartum period. In this review, we describe the 1) transport mechanisms of AEDs across the placenta and the effects of these drugs on placental transporters, and 2) the transfer of BZDs into breast milk. Our findings indicated that carrier systems were involved in the uptake of gabapentin (GBP) and lamotrigine (LTG) in placental trophoblast cell lines. SLC7A5 was the main contributor to GBP transport in placental cells. LTG was transported by a carrier that was sensitive to chloroquine, imipramine, quinidine, and verapamil. Short-term exposure to 16 AEDs had no effect on folic acid uptake in placental cells. However, long-term exposure to valproic acid (VPA) affected the expression of folate carriers (FOLR1, SLC46A1). Furthermore, VPA administration changed the expression levels of various transporters in rat placenta, suggesting that sensitivity to VPA differed across gestational stages. Lastly, we developed a method for quantifying eight BZDs in human breast milk and plasma using LC/MS/MS, and successfully applied it to quantify alprazolam in breast milk and plasma donated by a lactating woman.


Assuntos
Anticonvulsivantes/metabolismo , Benzodiazepinas/metabolismo , Transporte Biológico/genética , Aleitamento Materno , Gabapentina/metabolismo , Lactação/metabolismo , Lamotrigina/metabolismo , Transportador 1 de Aminoácidos Neutros Grandes/fisiologia , Troca Materno-Fetal , Leite Humano/metabolismo , Placenta/metabolismo , Ácido Valproico/metabolismo , Anticonvulsivantes/efeitos adversos , Benzodiazepinas/efeitos adversos , Benzodiazepinas/uso terapêutico , Linhagem Celular , Epilepsia/tratamento farmacológico , Feminino , Receptor 1 de Folato/genética , Receptor 1 de Folato/metabolismo , Gabapentina/efeitos adversos , Expressão Gênica/efeitos dos fármacos , Humanos , Lamotrigina/efeitos adversos , Gravidez , Complicações na Gravidez/tratamento farmacológico , Transportador de Folato Acoplado a Próton/genética , Transportador de Folato Acoplado a Próton/metabolismo , Ácido Valproico/efeitos adversos
6.
Yakugaku Zasshi ; 140(10): 1207-1212, 2020.
Artigo em Japonês | MEDLINE | ID: mdl-32999199

RESUMO

T-type calcium channels are low-threshold voltage-gated calcium channel and characterized by unique electrophysiological properties such as fast inactivation and slow deactivation kinetics. All subtypes of T-type calcium channel (Cav3.1, 3.2 and 3.3) are widely expressed in the central nerve system, and they have an important role in homeostasis of sleep, pain response, and development of epilepsy. Recently, several reports suggest that T-type calcium channels may mediate neuronal plasticity in the mouse brain. We succeeded to develop T-type calcium channel enhancer ethyl 8'-methyl-2',4-dioxo-2-(piperidin-1-yl)-2'H-spiro[cyclopentane-1,3'-imidazo[1,2-a]pyridine]-2-ene-3-carboxylate (SAK3) which enhances Cav3.1 and 3.3 currents in each-channel expressed neuro2A cells. SAK3 can promote acetylcholine (ACh) release in the mouse hippocampus via enhancing T-type calcium channel. In this review, we have introduced the role of T-type calcium channel, especially Cav3.1 channel in the mouse hippocampus based on our previous data using SAK3 and Cav3.1 knockout mice.


Assuntos
Canais de Cálcio Tipo T/efeitos dos fármacos , Canais de Cálcio Tipo T/fisiologia , Imidazóis/farmacologia , Neurônios/fisiologia , Compostos de Espiro/farmacologia , Acetilcolina/metabolismo , Animais , Encéfalo/fisiologia , Canais de Cálcio Tipo T/genética , Canais de Cálcio Tipo T/metabolismo , Células Cultivadas , Sistema Nervoso Central/metabolismo , Fenômenos Eletrofisiológicos , Epilepsia/etiologia , Expressão Gênica/efeitos dos fármacos , Hipocampo/metabolismo , Homeostase , Camundongos , Plasticidade Neuronal , Dor/etiologia , Ratos , Sono/fisiologia
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2524-2527, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018520

RESUMO

Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy patients. However, localizing the SOZ can be a time consuming and tedious process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during passive patient monitoring. Single pulse electrical stimulation (SPES) is currently performed on patients undergoing invasive EEG monitoring for the main purposes of mapping functional brain networks such as language and motor networks. We hypothesize that evoked responses from SPES can also be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. To test our hypothesis, we construct patient specific single-input multi-output transfer function models from the evoked responses recorded from five epilepsy patients that underwent SPES evaluation and iEEG monitoring. Our preliminary results suggest that the stimulation electrodes that produced the highest gain transfer functions, as measured by the ${\mathcal{H}_\infty }$ norm, correspond to those electrodes clinically defined in the SOZ in successfully treated patients.Clinical Relevance- This study creates an innovative tool that allows clinicians to identify the seizure onset zone in medically refractory epilepsy patients using quantitative metrics thereby increasing surgical success outcomes, mitigating patient risks, and decreasing costs.


Assuntos
Epilepsia Resistente a Medicamentos , Epilepsia , Epilepsia Resistente a Medicamentos/terapia , Estimulação Elétrica , Eletrocorticografia , Epilepsia/terapia , Humanos , Convulsões/terapia
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2687-2690, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018560

RESUMO

The purpose of this study is to analyse the ictal variations in peripheral blood flow using photoplethysmogram (PPG) and single lead Electrocardiogram (ECG) signals. 11 subjects with 56 partial seizures were recorded with the PPG sensor worn on their left ankles. 6 different features from PPG pulse morphology related to hemodynamics were derived. The seizures were divided into two groups based on the side of the seizure activity. The investigation of ictal variations in features did not show any significant difference between the seizures' lateralizations. The analysis of latencies of ictal changes in the PPG features revealed the PPG pulse amplitude precede the variations in other PPG features including ictal heart rate variability. In addition, analysis of the effect of seizure lengths on ictal variations showed the seizures' lengths have no significant effect on the feature variation rates.Clinical relevance- Analysis of the extracted PPG features and their timing suggest an increase in vascular resistance due to increase in sympathetic tone which occurs prior to the ictal tachycardia. These variations is independent of the seizures' lengths and lateralizations.


Assuntos
Epilepsia , Fotopletismografia , Epilepsia/diagnóstico , Frequência Cardíaca , Humanos , Convulsões/diagnóstico , Resistência Vascular
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2833-2836, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018596

RESUMO

Seizure types and characteristics may vary with time in a patient with distinct mechanisms underlying the propagation of ictal activity. Similarly, we found that both focal and generalized seizures coexist in some pilocarpine-induced chronic temporal lobe epilepsy (TLE) rats. In different seizure patterns, mapping complex networks and analyzing epileptic characteristics involved in seizure propagation are likely to reflect seizure propagation mechanisms, and indicate the establishment of stimulation strategy for epilepsy treatment, especially on the selection of stimulation targets. In our study, we used Granger causality method to track the time-variant epileptic brain functional connectivity in focal and generalized seizures from multi-site local field potentials (LFPs). Results showed that these two major types of seizures had different propagation patterns during ictal period. When comparing them, generalized seizures involved in a network with more complex relationships and spread to more extensive brain regions than in local seizures at mid-ictal stage. Moreover, we observed that focal seizures had a focused causal hub with strong interactions, while generalized seizures had relative distributed causal hubs to drive the development of seizure during seizure-onset stage. These findings suggest that stimulation strategy might need to be adapted to different seizure types thus allowing for retuning abnormal epileptic brain network and obtaining better treatment effect on seizure suppression.


Assuntos
Epilepsia do Lobo Temporal , Epilepsia , Animais , Encéfalo , Humanos , Pilocarpina , Ratos , Convulsões
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3384-3387, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018730

RESUMO

Vagus nerve stimulation (VNS) is a neurostimulation therapy for epilepsy and severe depression and has been recently shown to be effective for other conditions. Despite its demonstrated safety and efficacy, long-term and off-target effects of VNS remain to be fully determined. One of the complications reported in epilepsy is stimulation-induced sleep abnormalities. As epilepsy itself can impact sleep quality, contribution of VNS alone in such off-target effects remain mainly unknown. In this study, we analyzed data from long-term VNS experiments in rats to characterize effects of VNS on circadian rhythms derived from heart rate and heart rate variability (HRV). We have also explored possible sex differences in long-term effects of VNS on intrinsic biological rhythms. Compared with control animals, significant VNS-induced changes in circadian rhythms were observed particularly in female rats over 24h and 6h light cycles (1PM-7PM). These findings enhance our understanding of VNS contribution and biological sex role on sleep difficulties reported by using VNS therapy.


Assuntos
Epilepsia , Estimulação do Nervo Vago , Animais , Ritmo Circadiano , Epilepsia/terapia , Feminino , Frequência Cardíaca , Masculino , Ratos
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3460-3464, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018748

RESUMO

The absence of epileptiform activity in a scalp electroencephalogram (EEG) recorded from a potential epilepsy patient can cause delays in clinical care delivery. Here we present a machine-learning-based approach to find evidence for epilepsy in scalp EEGs that do not contain any epileptiform activity, according to expert visual review (i.e., "normal" EEGs). We found that deviations in the EEG features representing brain health, such as the alpha rhythm, can indicate the potential for epilepsy and help lateralize seizure focus, even when commonly recognized epileptiform features are absent. Hence, we developed a machine-learning-based approach that utilizes alpha-rhythm-related features to classify 1) whether an EEG was recorded from an epilepsy patient, and 2) if so, the seizure-generating side of the patient's brain. We evaluated our approach using "normal" scalp EEGs of 48 patients with drug-resistant focal epilepsy and 144 healthy individuals, and a naive Bayes classifier achieved area under ROC curve (AUC) values of 0.81 and 0.72 for the two classification tasks, respectively. These findings suggest that our methodology is useful in the absence of interictal epileptiform activity and can enhance the probability of diagnosing epilepsy at the earliest possible time.


Assuntos
Epilepsia , Teorema de Bayes , Encéfalo , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Convulsões/diagnóstico
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3695-3698, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018803

RESUMO

Epilepsy affects over 50 million people worldwide and 30% of patients' seizures are medically refractory. The process of localizing and removing the epileptogenic zone is error-prone and ill-posed in part because we do not understand how epilepsy manifests. It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. If the fragility of the cortical network could be computed over a period in which seizure genesis occurs, then it might elucidate network mechanisms correlated to the epileptogenic zone. In this study, we used local field potentials (LFP) from neocortex by implementing an acute model of epilepsy in mice. These recordings were used to develop a dynamical network model that quantifies the fragility of the nodes from LFP epochs of baseline activity, preictal and ictal states. Fragility was quantified by the generation of a linear time-varying model to which we then applied a perturbation to determine the sensitivity of nodes in the network. Spatiotemporal fragility maps showed clear quantifiable changes in the epileptogenic network's properties throughout different states of seizure genesis. We quantified this difference over a baseline, preictal and ictal periods to show that network fragility is modulated in the manifestation of epilepsy.


Assuntos
Epilepsia , Neocórtex , Animais , Humanos , Camundongos , Convulsões
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3703-3706, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018805

RESUMO

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we present a new approach based on convolutional neural network (CNN) to detect IEDs from EEGs automatically. The input to CNN is a combination of raw EEG and frequency sub-bands, namely delta, theta, alpha and, beta arranged as a vector for one-dimensional (1D) CNN or matrix for two-dimensional (2D) CNN. The proposed method is evaluated on 554 scalp EEGs. The database consists of 18,164 IEDs marked by two neurologists. Five-fold cross-validation was performed to assess the IED detectors. The resulting 1D CNN based IED detector with multiple sub-bands achieved a false positive rate per minute of 0.23 and a precision of 0.79 at 90% sensitivity. Further, the proposed system is evaluated on datasets from three other clinics, and the features extracted from CNN outputs could significantly discriminate (p-values <; 0.05) the EEGs with and without IEDs. We have proposed an optimized method with better performance than the literature that could aid clinicians to diagnose epilepsy expeditiously, and thereby devise proper treatment.


Assuntos
Aprendizado Profundo , Epilepsia , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Redes Neurais de Computação , Couro Cabeludo
14.
Artigo em Russo | MEDLINE | ID: mdl-33081460

RESUMO

The authors present a detailed review of current advances in the field of genetics of epilepsy. Separately, new views on the etiology and pathogenesis of genetic epileptic encephalopathies, focal epilepsy and idiopathic generalized epilepsies are examined. The authors emphasize the importance of genetic discoveries for the clinical practice, including the prospects in the development of patients' personalized treatment. A comparative analysis of the value of various methods of genetic research in the diagnosis of epilepsy, methods of integrating molecular genetic analyses into everyday practical medicine is presented.


Assuntos
Epilepsias Parciais , Epilepsia Generalizada , Epilepsia , Epilepsia/genética , Humanos
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5416-5419, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019205

RESUMO

Epileptic Seizure (Epilepsy) is a neurological disorder that occurs due to abnormal brain activities. Epilepsy affects patients' health and lead to life-threatening situations. Early prediction of epilepsy is highly effective to avoid seizures. Machine Learning algorithms have been used to classify epilepsy from Electroencephalograms (EEG) data. These algorithms exhibited reduced performance when classes are imbalanced. This work presents an integrated machine learning approach for epilepsy detection, which can effectively learn from imbalanced data. This approach utilizes Principal Component Analysis (PCA) at the first stage to extract both high- and low- variant Principal Components (PCs), which are empirically customized for imbalanced data classification. Conventionally, PCA is used for dimension reduction of a dataset leveraging PCs with high variances. In this paper, we propose a model to show that PCs associated with low variances can capture the implicit pattern of minor class of a dataset. The selected PCs are then fed into different machine learning classifiers to predict seizures. We performed experiments on the Epileptic Seizure Recognition dataset to evaluate our model. The experimental results show the robustness and effectiveness of the proposed model.


Assuntos
Epilepsia , Convulsões , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Aprendizado de Máquina , Análise de Componente Principal , Convulsões/diagnóstico
16.
Artigo em Inglês | MEDLINE | ID: mdl-33017927

RESUMO

Vagal Nerve Stimulation (VNS) is an option in the treatment of drug-resistant epilepsy. However, approximately a quarter of VNS subjects does not respond to the therapy. In this retrospective study, we introduce heart-rate features to distinguish VNS responders and non-responders. Standard pre-implantation measurements of 66 patients were segmented in relation to specific stimuli (open/close eyes, photic stimulation, hyperventilation, and rests between). Median interbeat intervals were found for each segment and normalized (NMRR). Five NMRRs were significant; the strongest feature achieved significance with p=0.013 and AUC=0.66. Low mutual correlation and independence on EEG signals mean that presented features could be considered as an addition for models predicting VNS response using EEG.


Assuntos
Epilepsia , Estimulação do Nervo Vago , Eletroencefalografia , Epilepsia/terapia , Frequência Cardíaca , Humanos , Estudos Retrospectivos
17.
Artigo em Inglês | MEDLINE | ID: mdl-33017928

RESUMO

Transient electrophysiological anomalies in the human brain have been associated with neurological disorders such as epilepsy, may signal impending adverse events (e.g, seizurse), or may reflect the effects of a stressor, such as insufficient sleep. These, typically brief, high-frequency and heterogeneous signal anomalies remain poorly understood, particularly at long time scales, and their morphology and variability have not been systematically characterized. In continuous neural recordings, their inherent sparsity, short duration and low amplitude makes their detection and classification difficult. In turn, this limits their evaluation as potential biomarkers of abnormal neurodynamic processes (e.g., ictogenesis) and predictors of impending adverse events. A novel algorithm is presented that leverages the inherent sparsity of high-frequency abnormalities in neural signals recorded at the scalp and uses spectral clustering to classify them in very high-dimensional signals spanning several days. It is shown that estimated clusters vary dynamically with time and their distribution changes substantially both as a function of time and space.


Assuntos
Encéfalo , Epilepsia , Algoritmos , Análise por Conglomerados , Fenômenos Eletrofisiológicos , Epilepsia/diagnóstico , Humanos
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 112-115, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017943

RESUMO

Epilepsy is a neurological disorder which causes seizures in over 65 million people worldwide. Recently developed implantable therapeutic devices aim to prevent symptoms by applying acute electrical stimulation to the seizure-generating brain region in response to activity detected by on-device machine learning hardware. Many training algorithms require an equal number of examples for each target class (e.g. normal activity and seizures), and performance can suffer if this condition is not satisfied. In the case of epilepsy, poor performance can cause seizures to be missed, or stimulation to be applied erroneously. As there is an abundance of normal (interictal) data in clinical EEG recordings, but seizures are rare events (less than 1% of the dataset), the data available for training is severely imbalanced. There are several conventional pre-processing methods used to address imbalanced class learning, such as down-sampling of the majority class and up-sampling of the minority class, but each have performance drawbacks. This paper presents an improved method which involves reducing the majority class down to the most effective interictal outlier samples. Outliers are determined by using Exponentially Decaying Memory Signal Energy (EDMSE) features with Isolation Forests and an ANOVA-based method, which involves comparing a moving feature window to a baseline reference window. Outlier-based sampling is tested with two classifiers (KNN and Logistic Regression) and achieves higher accuracy (∼2% increase) and fewer false positives (∼38% decrease), along with a lower latency (∼3 seconds shorter) compared to conventional training set pre-processing methods.


Assuntos
Epilepsia , Aprendizado de Máquina , Algoritmos , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Convulsões/diagnóstico
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 523-527, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018042

RESUMO

Electroencephalogram (EEG) has been intensively used as a diagnosis tool for epilepsy. The traditional diagnostic procedure relies on a recording of EEG from several days up to a few weeks, and the recordings are visually inspected by trained medical professionals. This procedure is time consuming with a high misdiagnosis rate. In recent years, computer-aided techniques have been proposed to automate the epilepsy diagnosis by using machine learning methods to analyze EEG data. Considering the time-varying nature of EEG, the goal of this work is to characterize dynamic changes of EEG patterns for the detection and classification of epilepsy. Four different dynamic Bayesian modeling methods were evaluated using multi-subject epileptic EEG data. Experimental results show that an accuracy of 98.0% can be achieved by one of the four methods. The same method also provides an overall accuracy of 87.7% for the classification of seven different seizure types.


Assuntos
Eletroencefalografia , Epilepsia , Teorema de Bayes , Epilepsia/diagnóstico , Humanos , Aprendizado de Máquina , Convulsões/diagnóstico
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 536-540, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018045

RESUMO

Recent years have seen a growing interest in the development of non-invasive devices capable of detecting seizures which can be worn in everyday life. Such devices must be lightweight and unobtrusive which severely limit their on-board computing power and battery life. In this paper, we propose a novel technique based on hyperdimensional (HD) computing to detect epileptic seizures from 2-channel surface EEG recordings. The proposed technique eliminates the need for complicated feature extraction techniques required in conventional ML algorithms. The HD algorithm is also simple to implement and does not require expert knowledge for architectural optimizations needed for approaches based on neural networks. In addition, our proposed technique is light-weight and meets the computation and memory constraints of ultra-small devices. Experimental results on a publicly available dataset indicates our approach improves the accuracy compared to state-of-the-art techniques while consuming smaller or comparable power.


Assuntos
Eletroencefalografia , Epilepsia , Algoritmos , Epilepsia/diagnóstico , Humanos , Redes Neurais de Computação , Convulsões/diagnóstico
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