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1.
Trials ; 22(1): 429, 2021 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-34225782

RESUMO

BACKGROUND: Routinely recorded data held in electronic health records can be used to inform the conduct of randomised controlled trials (RCTs). However, limitations with access and accuracy have been identified. OBJECTIVE: Using epilepsy as an exemplar condition, we assessed the attributes and agreement of routinely recorded data compared to data collected using case report forms in a UK RCT assessing antiepileptic drug treatments for individuals newly diagnosed with epilepsy. METHODS: The case study RCT is the Standard and New Antiepileptic Drugs II (SANAD II) trial, a pragmatic, UK multicentre RCT assessing the clinical and cost-effectiveness of antiepileptic drugs as treatments for epilepsy. Ninety-eight of 470 eligible participants provided consent for access to routinely recorded secondary care data that were retrieved from NHS Digital Hospital Episode Statistics (N=71) and primary and secondary care data from The Secure Anonymised Information Linkage Databank (N=27). We assessed data items relevant to the identification of individuals eligible for inclusion in SANAD II, baseline and follow-up visits. The attributes of routinely recorded data were assessed including the degree of missing data. The agreement between routinely recorded data and data collected on case report forms in SANAD II was assessed using calculation of Cohen's kappa for categorical data and construction of Bland-Altman plots for continuous data. RESULTS: There was a significant degree of missing data in the routine record for 15 of the 20 variables assessed, including all clinical variables. Agreement was poor for the majority of comparisons, including the assessments of seizure occurrence and adverse events. For example, only 23/62 (37%) participants had a date of first-ever seizure identified in routine datasets. Agreement was satisfactory for the date of prescription of antiepileptic drugs and episodes of healthcare resource use. CONCLUSIONS: There are currently significant limitations preventing the use of routinely recorded data for participant identification and assessment of clinical outcomes in epilepsy, and potentially other chronic conditions. Further research is urgently required to assess the attributes, agreement, additional benefits, cost-effectiveness and 'optimal mix' of routinely recorded data compared to data collected using standard methods such as case report forms at clinic visits for people with epilepsy. TRIAL REGISTRATION: Standard and New Antiepileptic Drugs II (SANAD II (EudraCT No: 2012-001884-64, registered 05/09/2012; ISRCTN Number: ISRCTN30294119 , registered 03/07/2012)).


Assuntos
Anticonvulsivantes , Epilepsia , Anticonvulsivantes/efeitos adversos , Registros Eletrônicos de Saúde , Epilepsia/diagnóstico , Epilepsia/tratamento farmacológico , Humanos , Convulsões/diagnóstico , Convulsões/tratamento farmacológico , Reino Unido
2.
Epilepsy Behav ; 121(Pt A): 108062, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34091129

RESUMO

INTRODUCTION: The diagnosis of epilepsy in children is difficult and misdiagnosis rates can be as much as 36%. Diagnosis in all countries is essentially clinical, based on asking a series of questions and interpreting the answers. Doctors experienced enough to do this are either scarce or absent in very many parts of the world so there is a need to develop a diagnostic aid to help less-experienced doctors or non-physician health workers (NPHWs) do this. We used a Bayesian approach to determine the most useful questions to ask based on their likelihood ratios (LR), and incorporated these into a Children's Epilepsy Diagnosis Aid (CEDA). METHODS: Ninety-six consecutive new referrals with possible epilepsy aged under 10 years attending a pediatric neurology clinic in Khartoum were included. Initially, their caregivers were asked 65 yes/no questions by a medical officer, then seen by pediatric neurologist and the diagnosis of epilepsy (E), not epilepsy (N), or uncertain (U) was made. The LR was calculated and then we selected the variables with the highest and lowest LRs which are the most informative at differentiating epilepsy from non-epilepsy. An algorithm, (CEDA), based on the most informative questions was constructed and tested on a new sample of 47 consecutive patients with a first attendance of possible epilepsy. We calculated the sensitivity and specificity for CEDA in the diagnosis of epilepsy. RESULTS: Sixty-nine (79%) had epilepsy and 18 (21%) non-epilepsy giving pre-test odds of having epilepsy of 3.83. Eleven variables with the most informative LRs formed the diagnostic aid (CEDA). The pre-test odds and algorithm were used to determine the probability of epilepsy diagnosis in a subsequent sample of 47 patients. There were 36 patients with epilepsy and 11 with nonepileptic conditions. The sensitivity of CEDA was 100% with specificity of 97% and misdiagnosis 8.3%. CONCLUSION: Children's Epilepsy Diagnosis Aid has the potential to improve pediatric epilepsy diagnosis and therefore management and is particularly likely to be useful in the many situations where access to epilepsy specialists is limited. The algorithm can be presented as a smartphone application or used as a spreadsheet on a computer.


Assuntos
Epilepsia , Idoso , Algoritmos , Teorema de Bayes , Criança , Epilepsia/diagnóstico , Epilepsia/epidemiologia , Humanos , Neurologistas , Sensibilidade e Especificidade
3.
Epilepsy Behav ; 121(Pt A): 108047, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34091130

RESUMO

Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.


Assuntos
Epilepsia , Transtornos Mentais , Adulto , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Convulsões/diagnóstico
4.
Epilepsy Behav ; 121(Pt A): 108078, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34111768

RESUMO

OBJECTIVE: In our canine scent detection research involving a specific volatile organic compound (VOC) associated with human epileptic seizure, we began to suspect involvement of the primitive neural networks associated with production of a previously undescribed human alarm pheromone as the origin of our seizure scent. We hypothesized that if we presented fear-scented sweat to our canine seizure scent detection team, and they identified the fear scent as their seizure scent, then that would suggest that they are identical compounds. METHODS: Following consent and approval, sweat samples taken from volunteers associated with the Brooke Gordon Comprehensive Epilepsy Center at Denver Health were processed by the Canine Assistants (CA) service dog team that had been imprinted to recognize the unique seizure scent from our previous study. In part one, sweat samples were collected from subjects, who had no prior history of epilepsy or seizures, under two different testing environments: watching a scary movie (It) and a neutral/comedy movie (Airplane!). In part two, a larger follow-up study utilizing fear sweat, exercise sweat, epilepsy sweat, and other distractor scents were provided in a multiple choice paradigm to better understand the inter-rater reliability of the canine responses. RESULTS: In part one, our canine seizure scent detection team identified fear-scented sweat samples as their seizure scent in 4 of 5 study participants. There was almost perfect agreement of seizure scent detection during fear scent trials between the canine seizure scent detectors with a kappa value of 0.814 (95% CI: 0.668-0.960). In part two, (utilizing eleven different subjects) our canine scent detection team identified samples of either fear or seizure sweat with a sensitivity of 82% and a specificity of 100% (no false positives) from among the multiple choices offered. Additionally, there was 92% agreement between the members of the canine scent detection team. SIGNIFICANCE: While this hypothesis testing study is small and deserves replication, it confirms that the Canine Assistants seizure scent detection team consistently and accurately identified fear-scented sweat as their seizure scent, implying that the VOC, menthone, is common to both conditions. This further implies that human seizure propagation and fear network circuitry may share a common anatomy, and that menthone may not only be an early seizure biomarker, but a newly described human alarm pheromone.


Assuntos
Epilepsia , Olfato , Animais , Cães , Epilepsia/diagnóstico , Medo , Seguimentos , Reprodutibilidade dos Testes
5.
Artigo em Inglês | MEDLINE | ID: mdl-34072232

RESUMO

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.


Assuntos
Aprendizado Profundo , Epilepsia , Algoritmos , Inteligência Artificial , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Convulsões/diagnóstico
6.
Neurodiagn J ; 61(2): 95-103, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34110971

RESUMO

Due to the coronavirus disease 2019 (COVID-19) pandemic, the state of Texas-limited elective procedures to conserve beds and personal protective equipment (PPE); therefore, between March 22 and May 18, 2020, admission to the epilepsy monitoring unit (EMU) was limited only to urgent and emergent cases. We evaluated clinical characteristics and outcomes of these patients who were admitted to the EMU. Nineteen patients were admitted (one patient twice) with average age of 36.26 years (11 female) and average length of stay 3 days (range: 2-9 days). At least one event was captured on continuous EEG (cEEG) and video monitoring in all 20 admissions (atypical in one). One patient had both epileptic (ES) and psychogenic non-epileptic seizures (PNES) while 10 had PNES and 9 had ES. In 8 of 9 patients with ES, medications were changed, while in 5 patients with PNES, anti-epileptic drugs (AED) were stopped; the remaining 5 were not on medications. Of the 14 patients who had seen an epileptologist pre-admission, 13 (or 93%) had their diagnosis confirmed by EMU stay; a statistically significant finding. While typically an elective admission, in the setting of the COVID-19 pandemic, urgent and emergent EMU admissions were required for increased seizure or event frequency. In the vast majority of patients (13 of 19), admission lead to medication changes to either better control seizures or to change therapeutics as appropriate when PNES was identified.


Assuntos
COVID-19/prevenção & controle , Epilepsia , Hospitalização/legislação & jurisprudência , Adulto , Idoso , Tomada de Decisão Clínica , Epilepsia/diagnóstico , Epilepsia/terapia , Feminino , Unidades Hospitalares , Humanos , Masculino , Pessoa de Meia-Idade , Monitorização Fisiológica , SARS-CoV-2 , Convulsões/diagnóstico , Convulsões/terapia , Adulto Jovem
7.
Artigo em Russo | MEDLINE | ID: mdl-34190486

RESUMO

In clinical practice the identification of the dynamics of course of focal epilepsies on the basis of available clinical and neurophysiological indices (prognostication) is of great importance. The purpose of the study is the short-term prognostication of the course of focal frontal and temporal epilepsy. The materials and methods. The control (42 patients) and clinical (70 patients) groups were examined. The complex clinical physiological examination was carried out using electroencephalography, cognitive evoked potential, cardiac rhythm variability and the Schulte test. The cluster analysis was applied to allocate the observable patients into groups according to the dynamics of seizures frequency. The artificial neural networks technology based on physiological characteristics was applied to classify patients into groups with different course of disease. The results. The spectral characteristics of electroencephalographic signal had the greatest value for short-term prognostication of course of disease in the group of patients with focal frontal epilepsy. In patients with focal temporal epilepsy, the most significant predictors were the characteristics of cognitive evoked potential and characteristics of function of coherence of electroencephalogram. The conclusions. The developed algorithm of prognostication of unfavorable course of focal frontal epilepsy has high sensitivity, but lower specificity. Contrariwise, in case of temporal epilepsy, high specificity of the proposed algorithm is demonstrative, but its sensitivity is lower. It is recommended to apply these algorithms and to accentuate attention on characteristics of potential parameters at organization of diagnostic process in case of focal epilepsy.


Assuntos
Epilepsias Parciais , Epilepsia , Eletroencefalografia , Epilepsia/diagnóstico , Epilepsia/terapia , Humanos
8.
Medicine (Baltimore) ; 100(23): e26093, 2021 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-34114993

RESUMO

RATIONALE: Mutations of connector enhancer of kinase suppressor of Ras-2 (CNKSR2) gene were identified as the cause of Houge type of X-linked syndromic mental retardation. The mutations of CNKSR2 gene are rare, we reporta patient carrying a novel nonsense mutation of CNKSR2,c.625C > T(p.Gln209∗) and review the clinical features and mutations of CNKSR2 gene for this rare condition considering previous literature. PATIENT CONCERNS: We report a case of a 7-year and 5-month-old Chinese patient with clinical symptoms of intellectual disability, language defect, epilepsy and hyperactivity. Genetic study revealed a novel nonsense variant of CNKSR2, which has not been reported yet. DIAGNOSIS: According to clinical manifestations, genetic pattern and ACMG classification of mutation site as Class 1-cause disease, the patient was diagnosed as Houge type of X-linked syndromic mental retardation caused by CNKSR2 gene mutation. INTERVENTIONS: The patient was administrated with a gradual titration of valproic acid (VPA). OUTCOMES: On administration of valproic acid, he had no further seizures. LESSONS: This is the first time to report a nonsense variant in CNKSR2, c.625C > T(p.Gln209∗), this finding could expand the spectrum of CNKSR2 mutations and might also support the further study of Houge type of X-linked syndromic mental retardation.


Assuntos
Proteínas Adaptadoras de Transdução de Sinal/genética , Epilepsia , Deficiência Intelectual , Transtornos da Linguagem , Retardo Mental Ligado ao Cromossomo X , Agitação Psicomotora , Ácido Valproico/administração & dosagem , Anticonvulsivantes/administração & dosagem , Criança , Códon sem Sentido , Epilepsia/diagnóstico , Epilepsia/genética , Epilepsia/prevenção & controle , Humanos , Deficiência Intelectual/diagnóstico , Deficiência Intelectual/genética , Transtornos da Linguagem/diagnóstico , Transtornos da Linguagem/genética , Masculino , Retardo Mental Ligado ao Cromossomo X/diagnóstico , Retardo Mental Ligado ao Cromossomo X/tratamento farmacológico , Retardo Mental Ligado ao Cromossomo X/genética , Mutação , Agitação Psicomotora/diagnóstico , Agitação Psicomotora/etiologia , Avaliação de Sintomas , Resultado do Tratamento
9.
Neurol India ; 69(3): 560-566, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34169842

RESUMO

Background: The study of seizure patterns in electroencephalography (EEG) requires several years of intensive training. In addition, inadequate training and human error may lead to misinterpretation and incorrect diagnosis. Artificial intelligence (AI)-based automated seizure detection systems hold an exciting potential to create paradigms for proper diagnosis and interpretation. AI holds the promise to transform healthcare into a system where machines and humans can work together to provide an accurate, timely diagnosis, and treatment to the patients. Objective: This article presents a brief overview of research on the use of AI systems for pattern recognition in EEG for clinical diagnosis. Material and Methods: The article begins with the need for understanding nonstationary signals such as EEG and simplifying their complexity for accurate pattern recognition in medical diagnosis. It also explains the core concepts of AI, machine learning (ML), and deep learning (DL) methods. Results and Conclusions: In this present context of epilepsy diagnosis, AI may work in two ways; first by creating visual representations (e.g., color-coded paradigms), which allow persons with limited training to make a diagnosis. The second is by directly explaining a complete automated analysis, which of course requires more complex paradigms than the previous one. We also clarify that AI is not about replacing doctors and strongly emphasize the need for domain knowledge in building robust AI models that can work in real-time scenarios rendering good detection accuracy in a minimum amount of time.


Assuntos
Epilepsia , Médicos , Inteligência Artificial , Atenção à Saúde , Epilepsia/diagnóstico , Humanos , Aprendizado de Máquina
10.
Epilepsy Behav ; 121(Pt A): 108030, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34029996

RESUMO

BACKGROUND: Psychogenic nonepileptic attacks (PNEA) are events of altered behavior that resemble epileptic seizures (ES) but are not caused by abnormal electrical cortical activity. Understanding which clinical signs and symptoms are associated with PNEA may allow better triaging for video-electroencephalogram monitoring (VEM) and for a more accurate prediction when such testing is unavailable. METHODS: We performed a systematic review searching Medline, Embase, and Cochrane Central from inception to March 29, 2019. We included original research that reported at least one clinical sign or symptom, included distinct groups of adult ES and PNEA with no overlap, and used VEM for the reference standard. Two authors independently assessed quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies tool. Pooled estimates of sensitivity and specificity of studies were evaluated using a bivariate random effects model. RESULTS: We identified 4028 articles, of which 33 were included. There was a female sex predominance in the PNEA population (n = 22). From our meta-analysis, pooled sensitivities (0.27-0.72) and specificities (0.51-0.89) for PNEA were modest for individual signs. History of sexual abuse had the highest pooled specificity (89%), while the most sensitive feature was female sex (72%). Individual studies (n = 4) reported high levels of accuracy for ictal eye closure (sensitivity 64-73.7% and specificity 76.9-100%) and post-traumatic stress disorder (no reported sensitivity or specificity). Assuming the pre-test probability for PNEA in a tertiary care epilepsy center is 14%, even the strongest meta-analyzed features only exert modest diagnostic value, increasing post-test probabilities to a maximum of 33%. CONCLUSIONS: This review reflects the limited certainty afforded by individual clinical features to distinguish between PNEA and ES. Specific demographic and comorbid features, even despite moderately high specificities, impart minimal impact on diagnostic decision making. This emphasizes the need for the development of multisource predictive tools to optimize diagnostic likelihood ratios.


Assuntos
Epilepsia , Convulsões , Adulto , Eletroencefalografia , Epilepsia/diagnóstico , Feminino , Humanos , Convulsões/diagnóstico , Sensibilidade e Especificidade
11.
Epilepsy Behav ; 121(Pt A): 108040, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34058491

RESUMO

OBJECTIVE: Subclinical seizures (SCS) are often captured during intracranial EEG monitoring of pediatric patients with refractory focal epilepsy. However, their clinical significance remains uncertain. We aimed to characterize features associated with SCS and whether their presence impacts epilepsy outcomes post-surgically. METHODS: A single center retrospective chart review of patients with refractory focal epilepsy who underwent intracranial EEG monitoring at Boston Children's Hospital between 2004 and 2014 was conducted. Patient and seizure characteristics as well as post-operative outcome data were collected. RESULTS: Of the 104 patients included in the study, SCS were recorded in 66 (63%). Fifty-eight had electroclinical seizures (ECS) and SCS (ECS + SCS), and eight patients only had SCS. There were no significant patient characteristics associated with the presence of SCS. One hundred one of the 104 patients (97%) underwent surgical resection after the intracranial EEG monitoring, 53 of which had Engel 1 outcomes (52%). Incomplete resection (OR 0.15, 95% confidence interval (CI) [0.06, 0.40], p < 0.001) or presence of temporal plus epilepsy (OR 0.23, 95% CI [0.06, 0.80], p = 0.04) was associated with poor Engel outcomes (Engel 2-4). Presence of SCS was not associated with epilepsy surgical outcomes (p = 0.99). SIGNIFICANCE: Nearly 2/3 of patients in our study had SCS captured on intracranial EEG monitoring, and arose in overlapping regions with the ictal onset zone of ECS. Completeness of resection remains the most important predictor of seizure outcome, regardless of the presence of SCS. In the absence of ECS during intracranial EEG monitoring, SCS onset zones may provide useful localization information to guide surgical resection plans. This is the largest cohort reported in the literature describing characteristics associated with the presence of SCS and the impact of SCS on pediatric epilepsy surgery outcomes.


Assuntos
Epilepsias Parciais , Epilepsia , Boston , Criança , Eletrocorticografia , Eletroencefalografia , Epilepsia/diagnóstico , Epilepsia/cirurgia , Humanos , Estudos Retrospectivos , Resultado do Tratamento
12.
Medicine (Baltimore) ; 100(20): e25831, 2021 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-34011048

RESUMO

BACKGROUND: : The gene mutation of coding sodium channel is one of the most important mechanisms in the pathogenesis of epilepsy. There exists a large inter-individual variation in the efficacy of valproic acid (VPA) against epilepsy. What are the genetic polymorphism influences of sodium channels on VPA response is still under discussion. In this study, a meta-analysis was used to further explore the effects of SCN1A and SCN2A gene polymorphism on VPA response in children with epilepsy. METHODS: : The PubMed, EMBASE, Web of Science, Chinese National Knowledge Infrastructure, Chinese Science and Technique Journals Database, China Biology Medicine disc, and Wan Fang Database were searched up to April 2021 for appropriate studies regarding the association between SCN1A and SCN2A gene polymorphism on VPA response in children suffering from epilepsy. The meta-analysis was conducted by Review Manager 5.3 software. RESULTS: : The results of this meta-analysis will be submitted to a peer-reviewed journal for publication. CONCLUSION: : This meta-analysis will summarize the effects of SCN1A and SCN2A gene polymorphisms on VPA response in children with epilepsy. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/N2786.


Assuntos
Anticonvulsivantes/farmacologia , Epilepsia/tratamento farmacológico , Canal de Sódio Disparado por Voltagem NAV1.1/genética , Canal de Sódio Disparado por Voltagem NAV1.2/genética , Ácido Valproico/farmacologia , Anticonvulsivantes/uso terapêutico , Criança , Resistência a Medicamentos/genética , Eletroencefalografia , Epilepsia/diagnóstico , Epilepsia/genética , Humanos , Metanálise como Assunto , Polimorfismo de Nucleotídeo Único , Revisões Sistemáticas como Assunto , Resultado do Tratamento , Ácido Valproico/uso terapêutico
13.
Biomed Res Int ; 2021: 5567046, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33959658

RESUMO

Stroke is the main cause of acquired epilepsy in elderly people. Poststroke epilepsy (PSE) not only affects functional recovery after stroke but also brings considerable social consequences. While some factors such as cortical involvement, hemorrhagic transformation, and stroke severity are associated with increased seizure risk, so far that remains controversial. In recent years, there are an increasing number of studies on potential biomarkers of PSE as tools for diagnosing and predicting epileptic seizures. Biomarkers such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), glutamate, and S100 calcium-binding protein B (S100B) in blood are associated with the occurrence of PSE. This review is aimed at summarizing the progress on potential biomarkers of PSE.


Assuntos
Biomarcadores/sangue , Epilepsia , Acidente Vascular Cerebral/complicações , Epilepsia/sangue , Epilepsia/diagnóstico , Epilepsia/etiologia , Ácido Glutâmico/sangue , Humanos , Interleucina-6/sangue , Subunidade beta da Proteína Ligante de Cálcio S100/sangue , Fator de Necrose Tumoral alfa/sangue
14.
J Neurosci Methods ; 358: 109220, 2021 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-33971201

RESUMO

BACKGROUND: Many electroencephalography (EEG) based seizure detection paradigms have been developed and validated over the last two decades. The majority of clinical approaches use scalp or intracranial EEG electrodes. Scalp EEG is limited by patient discomfort and short duration of useful EEG signals. Intracranial EEG involves an invasive surgical procedure associated with significant risk making it unsuitable for widespread use as a practical clinical biometric. A less invasive EEG monitoring approach, that is between invasive intracranial procedures and noninvasive methods, would fill the need of a safe, accurate, chronic (ultra-long term) and objective seizure detection method. We present validation of a continuous EEG seizure detection paradigm using human single-channel EEG recordings from subcutaneously placed electrodes that could be used to fulfill this need. METHODS: Ten-minute long sleep, awake and ictal EEG epochs obtained from 21 human subjects with subscalp electrodes and validated against simultaneous iEEG recordings were analyzed by three experienced clinical neurophysiologists. The 201subscalp EEG time series epochs where classified as diagnostic for awake, asleep, or seizure by the clinicians who were blinded to all other information. Seventy of the epochs were classified in this way as representing seizure activity. A subject specific seizure detection algorithm was trained and then evaluated offline for each patient in the data set using the expert consensus classification as the gold standard. RESULTS: The average seizure detection performance of the algorithm across 21 subjects exceeded 90 % accuracy: 97 % sensitivity, 91 % specificity, and 93 % accuracy. For 19 of 21 patient datasets the algorithm achieved 100 % sensitivity. For 15 of 21 patients, the algorithm achieved 100 % specificity. For 13 of 21 patients the algorithm achieved 100 % accuracy. COMPARISON: No comparable published methods are available for subgaleal EEG seizure detection. CONCLUSIONS: These findings suggest that a simple seizure detection algorithm using subcutaneous EEG signals could provide sufficient accuracy and clinical utility for use in a low power, long-term subcutaneous brain monitoring device. Such a device would fill a need for a large number of people with epilepsy who currently have no means for accurately quantifying their seizures thereby providing important information to healthcare providers not currently available.


Assuntos
Eletroencefalografia , Epilepsia , Algoritmos , Encéfalo , Epilepsia/diagnóstico , Humanos , Convulsões/diagnóstico
16.
Epilepsy Behav ; 118: 107943, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33839449

RESUMO

OBJECTIVE: Recent epilepsy quality measure recommendations for depression and anxiety screening endorse ultra-brief screeners, the Patient Health Questionnaire-2 (PHQ-2) and Generalized Anxiety Disorder-2 (GAD-2). Thus, it is important to assess how symptom detection may be affected using ultra-brief screeners compared with slightly longer, well-validated instruments: Neurological Disorders Depression Inventory-Epilepsy (NDDI-E) and Generalized Anxiety Disorder-7 (GAD-7). The objective was to compare symptom detection by brief versus ultra-brief depression and anxiety screeners in a large real-world epilepsy clinic sample. METHODS: This was a prospective, cross-sectional assessment of consecutive patients in an adult tertiary epilepsy practice who completed the GAD-7 and NDDI-E with embedded ultra-brief scales (GAD-2; GAD-Single Item: GAD-SI; NDDI-E 2 item: NDDIE-2) on a tablet and had clinic staff administered ultra-brief PHQ-2 (yes/no version) documented in the medical record at the same visit. Prevalences of positive anxiety and depression screens were calculated for each instrument overall, and by epilepsy status. Concordance correlation coefficients (CCC) were calculated comparing the ultra-brief with brief anxiety and depression instruments, and receiver operating curves (ROC) were calculated using the longer instruments as alternative standards. RESULTS: Among N = 422 individuals the prevalence of positive anxiety screen by GAD-7 was 24% and positive depression screen by NDDI-E was 20%. Positive anxiety and depression screens were significantly less prevalent among seizure-free individuals than those with continued seizures. The verbally administered yes/no PHQ-2 had only 1 positive screen (0.2%). Other than poor concordance between the PHQ-2 and NDDI-E, the screener pairs had acceptable concordance (CCC 0.79 to 0.92). Areas under the ROC curves were acceptable for the NDDIE-2, GAD-2 and GAD-SI (0.96, 0.98, and 0.89, respectively). SIGNIFICANCE: In this sample, clinic staff interview-administered yes/no PHQ-2 had exceedingly low sensitivity compared with the NDDI-E self-reported on a tablet. Further investigation is warranted to assess if poor detection is due to characteristics of this PHQ-2 in epilepsy samples, or method of administration in this clinic. The other ultra-brief anxiety and depression instruments demonstrated good concordance with the longer, well-validated instruments and may be useful in clinical practice.


Assuntos
Depressão , Epilepsia , Adulto , Ansiedade/diagnóstico , Ansiedade/epidemiologia , Ansiedade/etiologia , Transtornos de Ansiedade/diagnóstico , Transtornos de Ansiedade/epidemiologia , Estudos Transversais , Depressão/diagnóstico , Depressão/epidemiologia , Epilepsia/diagnóstico , Epilepsia/epidemiologia , Humanos , Programas de Rastreamento , Estudos Prospectivos , Escalas de Graduação Psiquiátrica , Reprodutibilidade dos Testes
17.
Z Gerontol Geriatr ; 54(4): 395-408, 2021 Jul.
Artigo em Alemão | MEDLINE | ID: mdl-33891210

RESUMO

Epilepsy is the third most frequent neurological disorder in aged patients after stroke and dementia. The incidence of epilepsy increases with age with the highest rates in patients ≥ 65 years old. Due to demographic changes the number of aged patients with epilepsy is expected to increase further in the coming years. The leading cause of new onset epilepsy in aged patients is cerebrovascular disease followed by dementia. The recognition of seizures in aged patients is often delayed. Status epilepticus occurs more frequently in aged patients and is associated with a high mortality and morbidity. Antiepileptic drug (AED) treatment of aged patients is complicated by comorbidities and polypharmacy and AEDs with a low interaction profile and high tolerability should be selected. Levetiracetam and lamotrigine are the AEDs of choice due to low interactions and good tolerability.


Assuntos
Carbamazepina , Epilepsia , Idoso , Anticonvulsivantes/uso terapêutico , Carbamazepina/uso terapêutico , Epilepsia/diagnóstico , Epilepsia/tratamento farmacológico , Epilepsia/epidemiologia , Humanos , Lamotrigina/uso terapêutico , Levetiracetam/uso terapêutico
18.
Artigo em Inglês | MEDLINE | ID: mdl-33830925

RESUMO

Drug refractory epilepsy (RE) is believed to be associated with structural lesions, but some RE patients show no significant structural abnormalities (RE-no-SA) on conventional magnetic resonance imaging scans. Since most of the medically controlled epilepsy (MCE) patients also do not exhibit structural abnormalities, a reliable assessment needs to be developed to differentiate RE-no-SA patients and MCE patients to avoid misdiagnosis and inappropriate treatment. Using resting-state scalp electroencephalogram (EEG) datasets, we extracted the spatial pattern of network (SPN) features from the functional and effective EEG networks of both RE-no-SA patients and MCE patients. Compared to the performance of traditional resting-state EEG network properties, the SPN features exhibited remarkable superiority in classifying these two groups of epilepsy patients, and accuracy values of 90.00% and 80.00% were obtained for the SPN features of the functional and effective EEG networks, respectively. By further fusing the SPN features of functional and effective networks, we demonstrated that the highest accuracy value of 96.67% could be reached, with a sensitivity of 100% and specificity of 92.86%. Overall, these findings not only indicate that the fused functional and effective SPN features are promising as reliable measurements for distinguishing RE-no-SA patients and MCE patients but also may provide a new perspective to explore the complex neurophysiology of refractory epilepsy.


Assuntos
Epilepsia Resistente a Medicamentos , Epilepsia , Epilepsia Resistente a Medicamentos/diagnóstico , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Imageamento por Ressonância Magnética
19.
Acta Neurol Scand ; 144(1): 67-75, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-33835491

RESUMO

OBJECTIVES: To investigate the interaction among the efficacy, tolerability and overall effectiveness of the first antiseizure medication in patients 16 years or older with newly diagnosed epilepsy. MATERIALS AND METHODS: The study included 584 patients who were referred to the Tampere University Hospital between 1 January 1995 and 31 December 2005 and were diagnosed with epilepsy. All individuals were retrospectively followed up until 31 December 2006, until reaching at least one year of seizure freedom, or until death if before the cut-off date. RESULTS: Overall, after thorough validation of the epilepsy diagnosis 459 patients comprised the study cohort; among these patients, 73% of males and 60% of females became seizure-free for at least one year with the first antiseizure medication. The seizure freedom rate for focal epilepsy was 67%. There was no significant difference in focal epilepsy to achieve seizure freedom between oxcarbazepine, carbamazepine or valproic acid. The seizure freedom rate among patients above 60 years of age was 67%. For patients with structural and unknown aetiology, seizure freedom rates were 61.5% and 75.3%, respectively. Additionally, epileptiform activity on EEG in patients with focal epilepsy decreased odds of seizure freedom in adjusted logistic regression models (OR 0.55, p=0.036). CONCLUSIONS: This study provides a more positive prediction of seizure freedom compared with previous studies with the onset of epilepsy at 16 years or older with an overall estimation that two-thirds of patients with new-onset epilepsy obtain seizure freedom with the first antiseizure medication.


Assuntos
Anticonvulsivantes/uso terapêutico , Epilepsia/diagnóstico , Epilepsia/tratamento farmacológico , Adulto , Idoso , Carbamazepina/uso terapêutico , Estudos de Coortes , Feminino , Seguimentos , Humanos , Masculino , Pessoa de Meia-Idade , Oxcarbazepina/uso terapêutico , Indução de Remissão/métodos , Estudos Retrospectivos , Resultado do Tratamento , Ácido Valproico/uso terapêutico , Adulto Jovem
20.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 38(1): 39-46, 2021 Feb 25.
Artigo em Chinês | MEDLINE | ID: mdl-33899426

RESUMO

At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.


Assuntos
Epilepsia , Máquina de Vetores de Suporte , Algoritmos , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Processamento de Sinais Assistido por Computador
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