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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
2.
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
3.
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
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 528-531, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018043

RESUMO

Current seizure detection systems rely on machine learning classifiers that are trained offline and subsequently require manual retraining to maintain high detection accuracy over long periods of time. For a true deploy-and-forget implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to the neural signal drifts over time. This work proposes SOUL: Stochastic-gradient-descent-based Online Unsupervised Logistic regression classifier, which provides continuous unsupervised online model updates that was initially trained with labels offline. SOUL was tested on two datasets, the CHB-MIT scalp EEG dataset, and a long (>250 hours) human ECoG dataset from the University of Melbourne. SOUL achieves an average cumulative sensitivity of 97.5% and 97.9% for the two datasets respectively, while maintaining <1.2 false alarms per day. When compared with state-of-the-art, a moderate sensitivity improvement of 1-3% is observed on the majority of subjects and a large sensitivity improvement of >12% is observed on three subjects with <1% impact on specificity.


Assuntos
Educação a Distância , Algoritmos , Eletroencefalografia , Humanos , Convulsões/diagnóstico , Sensibilidade e Especificidade
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 532-535, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018044

RESUMO

Absence seizures are expressed with distinctive spike-and-wave complexes in the electroencephalogram (EEG), which can be used to automatically distinguish them from other types of seizures and interictal activity. Considering the chaotic nature of the EEG signal, it is very unlikely that such continuous, repetitive patterns with strict periodic behavior would occur naturally under normal conditions. Searching for spectral activity in the range of 2.5-4.5 Hz and assessing the presence of synchronous, repeated patterns across multiple EEG channels in an unsupervised manner, the proposed methodology provides high absence seizure detection sensitivity of 93.94% with a low false detection rate of 0.168 FD/h using the open TUSZ dataset.


Assuntos
Epilepsia Tipo Ausência , Convulsões , Eletroencefalografia , Epilepsia Tipo Ausência/diagnóstico , Humanos , Convulsões/diagnóstico
6.
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
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 541-544, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018046

RESUMO

Epileptic seizure prediction explores the probability of forecasting the onset of epileptic seizure, which aids to timely treatment for patients. It provides a time lead compared to traditional seizure detection. In this paper, a spectral feature extraction is developed and the seizure prediction is performed based on uncorrelated multilinear discriminant analysis (UMLDA) and Support Vector Machine (SVM). To make best use of information in different dimension, we construct a three-order tensor in temporal, spectral and spatial domain by wavelet transform. And UMLDA implements the tensor-to-vector projection (TVP) with the minimum redundancy. The proposed solution employed 23 subjects' Electroencephalogram (EEG) data from Boston Children's Hospital-MIT scalp EEG dataset, each subject contains 40 minutes EEG signal. For the classification task of ictal state and preictal state, it exhibits an overall accuracy of 95%.


Assuntos
Algoritmos , Epilepsia , Boston , Criança , Análise Discriminante , Epilepsia/diagnóstico , Humanos , Convulsões/diagnóstico
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 569-575, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018053

RESUMO

Classification of seizure type is a key step in the clinical process for evaluating an individual who presents with seizures. It determines the course of clinical diagnosis and treatment, and its impact stretches beyond the clinical domain to epilepsy research and the development of novel therapies. Automated identification of seizure type may facilitate understanding of the disease, and seizure detection and prediction have been the focus of recent research that has sought to exploit the benefits of machine learning and deep learning architectures. Nevertheless, there is not yet a definitive solution for automating the classification of seizure type, a task that must currently be performed by an expert epileptologist. Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data. We first explore the performance of traditional deep learning techniques which use convolutional and recurrent neural networks, and enhance these architectures by using external memory modules with trainable neural plasticity. We show that our model achieves a state-of-the-art weighted F1 score of 0.945 for seizure type classification on the TUH EEG Seizure Corpus with the IBM TUSZ preprocessed data. This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and signal analysis more broadly.


Assuntos
Eletroencefalografia , Epilepsia , Epilepsia/diagnóstico , Humanos , Memória , Redes Neurais de Computação , Convulsões/diagnóstico
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 698-701, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018083

RESUMO

Over a third of patients suffering from epilepsy continue to live with recurrent disabling seizures and would greatly benefit from personalized seizure forecasting. While electroencephalography (EEG) remains most popular for studying subject-specific epileptic precursors, dysfunctions of the autonomous nervous system, notably cardiac activity measured in heart rate variability (HRV), have also been associated with epileptic seizures. This work proposes an unsupervised clustering technique which aims to automatically identify preictal HRV changes in 9 patients who underwent simultaneous electrocardiography (ECG) and intracranial EEG presurgical monitoring at the University of Montreal Hospital Center. A 2-class k-means clustering combined with a quantitative preictal HRV change detection technique were adopted in a subject- and seizure-specific manner. Results indicate inter and intra-patient variability in preictal HRV changes (between 3.5 and 6.5 min before seizure onset) and a statistically significant negative correlation between the time of change in HRV state and the duration of seizures (p<0.05). The presented findings show promise for new avenues of research regarding multimodal seizure prediction and unsupervised preictal time assessment.Clinical Relevance- This study proposed an unsupervised technique for quantitatively identifying preictal HRV changes which can be eventually used to implement an ECG-based seizure forecasting algorithm.


Assuntos
Epilepsia , Análise por Conglomerados , Eletroencefalografia , Frequência Cardíaca , Humanos , Convulsões/diagnóstico
10.
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
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: 3699-3702, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018804

RESUMO

Time- and frequency-domain studies of EEG signals are most commonly employed to study the electrical activities of the brain in order to diagnose potential neurological disorders. In this work, we applied the global coherence approach to help estimating the neural synchrony across multiple nodes in the brain, prior and during a seizure. The ratio of the largest eigenvalue to the sum of the eigenvalues of the cross spectral matrix at a certain frequency and time allowed detecting a strong coordinated neural activity in alpha sub-band for the frontal lobe epilepsy. Kruskal Wallis test reveals that global coherence is an efficient tool before the seizure for the temporal lobe epilepsy in a wide range of frequencies from Delta to Beta sub-bands.Clinical Relevance-The work introduces global coherence as a new and efficient feature in prediction of seizure and specifically for the frontal lobe epilepsy.


Assuntos
Epilepsia do Lobo Frontal , Epilepsia do Lobo Temporal , Encéfalo , Eletroencefalografia , Epilepsia do Lobo Frontal/diagnóstico , Epilepsia do Lobo Temporal/diagnóstico , Humanos , Convulsões/diagnóstico
13.
BMC Pregnancy Childbirth ; 20(1): 587, 2020 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-33023500

RESUMO

BACKGROUND: There are no published cases of tonic-clonic seizures and posterior bilateral blindness during pregnancy and Severe Acute Respiratory Syndrome (SARS) Coronavirus (COV) 2 (SARS-COV-2) infection. We do not just face new and unknown manifestations, but also how different patient groups are affected by SARS-COV-2 infection, such as pregnant women. Coronavirus Disease 2019 (COVID-19), preeclampsia, eclampsia and posterior reversible leukoencephalopathy share endothelium damage and similar pathophysiology. CASE PRESENTATION: A 35-year-old pregnant woman was admitted for tonic-clonic seizures and SARS-COV-2 infection. She had a normal pregnancy control and no other symptoms before tonic-clonic seizures development. After a Caesarean section (C-section) she developed high blood pressure, and we initiated antihypertensive treatment with labetalol, amlodipine and captopril. Few hours later she developed symptoms of cortical blindness that resolved in 72 h with normal brain computed tomography (CT) angiography. CONCLUSION: The authors conclude that SARS COV-2 infection could promote brain endothelial damage and facilitate neurological complications during pregnancy.


Assuntos
Anti-Hipertensivos/administração & dosagem , Betacoronavirus/isolamento & purificação , Cegueira Cortical , Cesárea/métodos , Infecções por Coronavirus , Eclampsia , Fibrinolíticos/administração & dosagem , Pandemias , Pneumonia Viral , Complicações Infecciosas na Gravidez , Convulsões , Adulto , Cegueira Cortical/diagnóstico , Cegueira Cortical/virologia , Encéfalo/diagnóstico por imagem , Angiografia por Tomografia Computadorizada/métodos , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/fisiopatologia , Diagnóstico Diferencial , Eclampsia/diagnóstico , Eclampsia/terapia , Eclampsia/virologia , Feminino , Humanos , Exame Neurológico/métodos , Pneumonia Viral/diagnóstico , Pneumonia Viral/fisiopatologia , Gravidez , Complicações Infecciosas na Gravidez/diagnóstico , Complicações Infecciosas na Gravidez/etiologia , Complicações Infecciosas na Gravidez/fisiopatologia , Resultado da Gravidez , Convulsões/diagnóstico , Convulsões/etiologia , Convulsões/terapia , Tomografia Computadorizada por Raios X/métodos , Resultado do Tratamento
14.
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.
Medicine (Baltimore) ; 99(44): e22965, 2020 Oct 30.
Artigo em Inglês | MEDLINE | ID: mdl-33126368

RESUMO

To figure out which diagnosis is more suitable and which antiepileptic drugs are more sensitive to epileptic negative myoclonus (ENM) as the first seizure type in atypical benign epilepsy with centrotemporal spikes.We reviewed the electroencephalogram (EEG) database of Linyi People's Hospital Affiliated to Shandong University and medical records of patients with ENM onset. The characteristics of epileptic seizures, onset age, treatment process, growth and development history, past disease history, family history, degree of mental deterioration, cranial imaging, and video-EEG were studied retrospectively and followed up.There were 4 cases with ENM onset and 1 with continuous ENM, 3 males and 1 female. The onset age was from 2 years 3 months to 8 years 7 months. The cranial magnetic resonance imaging (MRI) and developmental quotient, as well as the family, personal, and past disease history, were normal. Frequent falls and drops were the main clinical manifestations. Five months after the onset of ENM, case 1 had focal seizures in sleep. ENM was the first and only manifestation in all the other 3 children. Discharges of interictal EEG were in bilateral rolandic areas, especially in midline areas (Cz, Pz), electrical status epilepticus in sleep was found in 3 cases. One child was sensitive to levetiracetam, the other 3 were sensitive to clonazepam.ENM can affect the upper or lower extremities. ENM as the first or only symptom was a special phenomenon in benign epilepsy with centrotemporal spikes (BECTS) variants. Ignorance of midline spikes mainly in Cz or Pz in BECTS might lead to missed diagnosis of ENM. Whether benzodiazepines are viable as a choice of BECTS variants with electrical status epilepticus in sleep when ENM is the first symptom still needs a large sample evidence-based observation.


Assuntos
Epilepsia/diagnóstico , Mioclonia/diagnóstico , Convulsões/diagnóstico , Criança , Pré-Escolar , Eletroencefalografia , Epilepsia/fisiopatologia , Feminino , Humanos , Masculino , Mioclonia/fisiopatologia , Convulsões/fisiopatologia
17.
Medicine (Baltimore) ; 99(43): e22940, 2020 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-33120854

RESUMO

RATIONALE: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) has been reported almost exclusively in the Japanese population. PATIENT CONCERNS: A 17-month-old male patient presented with fever and seizures, and subsequently fell into a coma. On the second day, he recovered consciousness. On the fourth day, he developed complex partial seizures and fell into a coma again. On day 10, the fever and seizures subsided. Head computed tomography on the first day showed no abnormalities. Brain diffusion-weighted images on the fourth day revealed reduced diffusion in the bilateral subcortical white matter. DIAGNOSIS: A diagnosis of AESD was made. INTERVENTIONS: The patient was treated with corticosteroids and intravenous immunoglobulin. OUTCOMES: At the 4-month follow-up, the patient was able to walk independently, and the epileptic seizures were well controlled. LESSONS: AESD is a rare entity, and treatment with corticosteroids and intravenous immunoglobulin can lead to a favorable prognosis. Clinicians should be aware of this condition, and clinicoradiological features can suggest the diagnosis.


Assuntos
Encefalopatias/complicações , Imagem de Difusão por Ressonância Magnética/métodos , Convulsões/etiologia , Doença Aguda , Corticosteroides/uso terapêutico , Encefalopatias/diagnóstico , Encefalopatias/tratamento farmacológico , Coma/diagnóstico , Coma/etiologia , Quimioterapia Combinada , Febre/diagnóstico , Febre/etiologia , Humanos , Imunoglobulinas Intravenosas/uso terapêutico , Lactente , Masculino , Convulsões/diagnóstico , Convulsões/fisiopatologia , Tomografia Computadorizada por Raios X/métodos , Resultado do Tratamento , Substância Branca/diagnóstico por imagem , Substância Branca/patologia
18.
Emerg Med Clin North Am ; 38(4): 771-782, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32981616

RESUMO

Patients resuscitated from cardiac arrest require complex management. An organized approach to early postarrest care can improve patient outcomes. Priorities include completing a focused diagnostic work-up to identify and reverse the inciting cause of arrest, stabilizing cardiorespiratory instability to prevent rearrest, minimizing secondary brain injury, evaluating the risk and benefits of transfer to a specialty care center, and avoiding early neurologic prognostication.


Assuntos
Parada Cardíaca/terapia , Prevenção Secundária , Temperatura Corporal , Encéfalo/diagnóstico por imagem , Circulação Cerebrovascular , Eletrocardiografia , Eletroencefalografia , Serviço Hospitalar de Emergência , Oxigenação por Membrana Extracorpórea , Parada Cardíaca/etiologia , Humanos , Hipóxia-Isquemia Encefálica/prevenção & controle , Anamnese , Transferência de Pacientes , Intervenção Coronária Percutânea , Exame Físico , Prognóstico , Radiografia Torácica , Respiração Artificial , Convulsões/diagnóstico , Convulsões/etiologia , Tomografia Computadorizada por Raios X
19.
Artigo em Russo | MEDLINE | ID: mdl-32929932

RESUMO

Viral encephalitis, its complications and the newly diagnosed epilepsy in children require a complex approach to the differential diagnosis using laboratory and instrumental examinations. Possibilities of MRI in the differential diagnosis of seizures in children and in detection of ischemic-hypoxic and metabolic disorders in the suspected epileptic focus are demonstrated in the clinical observation.


Assuntos
Eletroencefalografia , Epilepsia/diagnóstico , Criança , Diagnóstico Diferencial , Humanos , Imagem por Ressonância Magnética , Convulsões/diagnóstico
20.
Clin Neurophysiol ; 131(11): 2651-2656, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32949985

RESUMO

OBJECTIVE: As concerns regarding neurological manifestations in COVID-19 (coronavirus disease 2019) patients increase, limited data exists on continuous electroencephalography (cEEG) findings in these patients. We present a retrospective cohort study of cEEG monitoring in COVID-19 patients to better explore this knowledge gap. METHODS: Among 22 COVID-19 patients, 19 underwent cEEGs, and 3 underwent routine EEGs (<1 h). Demographic and clinical variables, including comorbid conditions, discharge disposition, survival and cEEG findings, were collected. RESULTS: cEEG was performed for evaluation of altered mental status (n = 17) or seizure-like events (n = 5). Five patients, including 2 with epilepsy, had epileptiform abnormalities on cEEG. Two patients had electrographic seizures without a prior epilepsy history. There were no acute neuroimaging findings. Periodic discharges were noted in one-third of patients and encephalopathic EEG findings were not associated with IV anesthetic use. CONCLUSIONS: Interictal epileptiform abnormalities in the absence of prior epilepsy history were rare. However, the discovery of asymptomatic seizures in two of twenty-two patients was higher than previously reported and is therefore of concern. SIGNIFICANCE: cEEG monitoring in COVID-19 patients may aid in better understanding an epileptogenic potential of SARS-CoV2 infection. Nevertheless, larger studies utilizing cEEG are required to better examine acute epileptic risk in COVID-19 patients.


Assuntos
Infecções por Coronavirus/fisiopatologia , Eletroencefalografia/métodos , Monitorização Neurofisiológica/métodos , Pneumonia Viral/fisiopatologia , Convulsões/fisiopatologia , Idoso , Infecções por Coronavirus/complicações , Infecções por Coronavirus/diagnóstico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/complicações , Pneumonia Viral/diagnóstico , Convulsões/diagnóstico , Convulsões/etiologia
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