The objective of this study was to evaluate relationships between quantitative EEG (qEEG) changes and cognitive disturbance (mild cognitive impairment or dementia) and the motor disturbance stage in Parkinson's disease (PD). Thirty-two PD patients (age = 67.2 +/- 10.0) and 26 normal subjects (age = 68.4 +/- 4.7) were assessed using a neurological evaluation, modified Hoehn and Yahr (HY) scale for PD, a Portuguese version of the CERAD neuropsychological battery (consortium to establish a registry for Alzheimer's disease) incorporating the Mini-mental Status Examination, Clinical Dementia Rating and an EEG analysis of absolute and relative band amplitude at rest. Four groups were compared: three with PD (7 patients with dementia, 10 with mild cognitive impairment and 15 with no cognitive disturbances) and the control group. The qEEG showed no significant differences between the control group and PD patients without cognitive disturbance. Abnormalities on the qEEG were essentially associated with the occurrence of mild cognitive impairment or dementia in patients with PD. There was an increase in the absolute and relative posterior theta amplitude in the groups with mild cognitive impairment or dementia and of the posterior absolute and relative delta amplitude in the group with dementia This study suggested qEEG as a possible physiological tool in the assessment of cognitive aspects in PD.

-The objective of this research was to assess the occurrence of cognitive impairment in 32 individuals (average age: 67.2 years old) with Parkinson's disease (PD). Procedures: clinical-neurological assessment; modified Hoehn and Yahr staging scale (HYS); standard neuropsychological battery of CERAD (Consortium to Establish a Registry for Alzheimer's Disease); Pfeffer questionnaire; and Clinical Dementia Rating. A comparison was made with a control group (CG), consisting of 26 individuals with similar age and educational level but without cognitive impairment. The PD patients showed an inferior performance in the CERAD battery when compared to the CG. Three PD sub-groups were characterised according to cognition: no cognitive impairment -15 cases; mild cognitive impairment -10; dementia -7 cases. There was a significant association between motor disability (HYS) and the occurrence of dementia. Dementia and mild cognitive impairment frequently occur in PD patients and should be investigated in a routine way.KEY woRDS: Parkinson disease, dementia, cognition. Demência e transtorno cognitivo leve em pacientes com doença de ParkinsonResumo -o objetivo desta pesquisa foi avaliar a ocorrência de déficits cognitivos em 32 indivíduos (idade média: 67,2 anos) com doença de Parkinson (DP). Procedimentos: avaliação clínico-neurológica, escala de Hoehn and Yahr modificada (EHY), bateria neurospicológica do CERAD (Consortium to Establish a Registry for Alzheimer's Disease), questionário de Pfeffer e escore clínico da demência (Clinical Dementia Rating). Foi feita comparação com grupo controle (GC) de 26 indivíduos sem declínio cognitivo, com idade e nível educacional similares. os pacientes com DP tiveram desempenho inferior na bateria CERAD, quando comparados ao do GC. Foram caracterizados 3 subgrupos com PD segundo a cognição: sem déficits cognitivos -15 casos; transtorno cognitivo leve -10; demência -7 casos. Houve associação entre comprometimento motor e ocorrência de demência. Demência e transtorno cognitivo leve são freqüentes em pacientes com DP e devem ser investigados de modo rotineiro.PAlAvRAS-CHAvE: doença de Parkinson, cognição, demência.

No abstract

Benign childhood epilepsy with centrotemporal spikes (BECTS) is common during childhood, but there are few reports in the literature recording the EEG during a seizure. We studied an 8-year-old boy with oropharyngeal seizures during wakefulness and sleep. Both his neuropsychomotor development and neurological examination were normal. While awake, the subjects's electroencephalogram (EEG) showed normal background activity and epileptiform activity characterized by spikes in the temporal regions (mid and anterior), central region of the right cerebral hemisphere and in the median central and parietal regions. During sleep, his EEG recorded an epileptic seizure that lasted 46 seconds. In the initial phase, the EEG showed an increase in the number of spikes with higher potential in the median central and parietal regions, followed by slow waves associated with the increase in slow waves in the right hemisphere. This was followed by a brief decrease in amplitude of the background activity, and then by rhythmic, diffuse discharges predominantly in the right centrotemporal region, of sharp waves at 12-13 Hz, with increasing potential. Slow waves of high amplitude then occurred for 5 seconds, and finally very high potential spikes reappeared in the central and temporal regions of the right cerebral hemisphere with normalization of the background activity. During these critical phases of the EEG, clonic lip movements and pouting could be observed with the mouth locked shut, associated with "throat noises," but there were no other motor manifestations. The child did not wake up during the seizure and there were no postictal signs or symptoms. Although there are some aspects in common in recordings of BECTS seizures, such as a reduction in amplitude followed by rhythmic discharges of increasing amplitude, differences exist that possibly correspond to the diverse characteristics of the electrical generators.

Direitos para esta edição cedidos à Atena Editora pelos autores. Open access publication by Atena Editora Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0). O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.

Direitos para esta edição cedidos à Atena Editora pelos autores. Open access publication by Atena Editora Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0). O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.

No abstract

Background: Epileptic seizures are often accompanied by strong patient motion that follows specific patterns. This pattern can be captured by a video-based system that will raise an alarm upon identification of an epileptic seizure, which is valuable for both inpatient as well as home monitoring scenarios. Objective: The aim of this work is to determine a model of epileptic seizures in video data, which allows to automatically identify epileptic seizures in real time. Patients and methods: In an epilepsy monitoring unit, 52 seizures were recorded with a combined infrared and depth imaging sensor at 15fps. We have obtained patient approval, as necessary. A deep convolutional neural network architecture (CNN) was trained on frames from the ictal as well as from the interictal phase. One CNN was used to process infrared frames and a second CNN to process depth frames. The output of both networks was combined to achieve a final decision for either ictal or interictal phase. Results: Cross-validation revealed high sensitivity (87%) and specificity (81%) for general convulsive seizures. Taking into account also tonic and automotor seizures, the average sensitivity was 80% at a 59% specificity, while competing methods such as the average motion frequency or image gradient histograms scored worse.Conclusions: Instead of a predefined model that expects only a certain type of motion, our model learns from raw input data and appears to capture more salient information from seizure recordings. Furthermore, including not only infrared but also depth data improved the ratio of correctly identified epileptic seizures.