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Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases and it is strongly associated with age. There are four AD stages: mild cognitive impairment (MCI), mild, moderate (ADM) and advanced (ADA). This work aims at developing a new tool capable of distinguishing the different stages of AD at scalp level. Features such as the conventional frequencies relative power of the power spectrum wavelet packet transform have been extracted from the electroencephalogram signals in order to feed four classifiers: random forest decision trees, linear and quadratic support-vector-machines and linear discriminant analysis. The obtained results were analyzed through topographic maps and enabled the distinguish between binary groups with the following overall accuracies: 85.5% (C-MCI); 88.2% (C-ADM); 91.4% (C-ADA); 89.7% (MCI-ADM); 82.4% (MCI-ADA) and 81.3% (ADM-ADA). The applied method was able to detect major differences in scalp areas above the frontal and temporal lobes of the brain as AD progresses.

1. Sofia Helena Ferreira, MD* 2. Carolina Lopes, MD* 3. Mariana Abreu, MD* 4. Claudia Melo, MD* 5. Dilio Alves, MD* 6. Raquel Sousa, MD* 1. *Department of Pediatrics of Centro Hospitalar de Sao Joao, Porto, Portugal 1. Address correspondence to Sofia Helena Ferreira, Department of Pediatrics of Centro Hospitalar de Sao Joao, Alameda Prof. Hernâni Monteiro 4200–319, Porto, Portugal. E-mail: sofiahferreira{at}gmail.com A 4-year-old girl with a 2-day history of progressive walking impairment, hands and legs pain, and bilateral palpebral ptosis was admitted to the emergency department. One week earlier she had an upper respiratory tract infection. Her past medical history is unremarkable. Her parents were nonconsanguineous, young, and healthy. At the emergency department, the child was sleepy and with episodic irritability. She complained of hands and feet pain and refused to walk. She had bilateral ptosis and deep tendon reflexes were decreased in the lower limbs. Her neurological examination was otherwise normal. Blood testing reveals normal complete blood count, renal and liver function, electrolytes, muscle enzymes, and C-reactive protein. Urine toxicology screen is negative. Head computerized tomography is normal. A lumbar puncture is performed and cerebrospinal fluid examination is normal, including normal cell count, glucose, and protein (with no cytoalbuminologic dissociation being present). Microscopic analysis and polymerase chain reaction for enterovirus, parvovirus, herpes 6, and herpes simplex 1 and 2 viruses are requested. Serologies for Borrelia , Mycoplasma pneumonia , parvovirus B19, cytomegalovirus, Epstein-Barr virus, herpes simplex 1 and 2, enterovirus, Haemophilus influenza , and Campylobacter jejuni were done. Because of the somnolence and irritable behavior, the possibility of encephalitis is considered. She is admitted and ceftriaxone and acyclovir infusions are initiated. On the second day, her clinical condition deteriorates: she becomes lethargic and develops ophthalmoplegia and gait impairment, dysesthesias, and high blood pressure. She is admitted to the intensive care unit. A brain magnetic resonance imaging and electromyography are performed, and a serum antibody test for GQ1b is requested. ### Differential Diagnosis and Actual Diagnosis Initially, regarding the presence of excessive sleepiness …

This work proposes the application of a new electroencephalogram (EEG) signal processing tool - the lacsogram - to characterize the Alzheimer's disease (AD) activity and to assist on its diagnosis at different stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). Statistical analyzes are performed to lacstral distances between conventional EEG subbands to find measures capable of discriminating AD in all stages and characterizing the AD activity in each electrode. Cepstral distances are used for comparison. Comparing all AD stages and Controls (C), the most important significances are the lacstral distances between subbands $\theta$ and $\alpha$ ( $p$ = 0.0014 $ 0.05). The topographic maps show significant differences in parietal, temporal and frontal regions as AD progresses. Machine learning models with a leave-one-out cross-validation process are applied to lacstral/cepstral distances to develop an automatic method for diagnosing AD. The following classification accuracies are obtained with an artificial neural network: 95.55% for All vs All, 98.06% for C vs MCI, 95.99% for C vs ADM, 93.85% for MCI vs ADM-ADA. In C vs MCI, C vs ADM and MCI vs ADM-ADA, the proposed method outperforms the state-of-art methods by 5%, 1%, and 2%, respectively. In All vs All, it outperforms the state-of-art EEG and non-EEG methods by 6% and 2%, respectively. These results indicate that the proposed method represents an improvement in diagnosing AD.

The World's health systems are now facing a global problem known as Alzheimer's disease (AD) that mainly affects the elderly. The goal of this work is to perform a classification methodology skilled with Artificial Neural Networks (ANN) to improve the discrimination accuracy amongst patients at AD different stages comparatively to the state-of-art. For that, several study features that characterized the Electroencephalogram (EEG) signals “slow-down” were extracted and presented to the ANN entries in order to classify the dataset. The classification results achieved in the present work are promising concerning AD early diagnosis and they show that EEG can be a good tool for AD detection (Controls (C) vs AD: accuracy 95%; C vs Mild-cognitive Impairment (MCI): accuracy 77%; MCI vs AD: accuracy 83%; All vs All: accuracy 90%).

The symptoms of autoimmune encephalitis with antibodies directed against neuronal cell surface antigens are generally reversible by removing the antibodies or the antibody-producing B cells.1

Alzheimer’s disease (AD) is a neurocognitive illness that leads to dementia and mainly affects the elderly. As the percentage of old people is strongly increasing worldwide, it is urgent to develop contributions to solve this complex problem. The early diagnosis at AD first stage known as Mild Cognitive Impairment (MCI) needs a better accuracy and there is not a biomarker able to detect AD without invasive tests. In this study, Electroencephalogram (EEG) signals have been used to serve as a way of finding parameters to improve AD diagnosis in first stages. For that, a hybrid method based on a Cepstral analysis of EEG Discrete Wavelet Transform (DWT) multiband decomposition was developed. Several Cepstral Distances (CD) were extracted to verify the lag between cepstra of conventional bands signals. The results showed that this hybrid method is a good tool for describing and distinguishing the AD EEG activity along its different stages because several statistically significant parameters variations were found between controls, MCI, moderate AD and advanced AD (the lowest p-value=0.003

The World’s health systems are now facing a global problem known as Alzheimer’s disease (AD) that mainly affects the elderly. The goal of this work is to perform a classification methodology skilled with Artificial Neural Networks (ANN) to improve the discrimination accuracy amongst patients at AD different stages comparatively to the state-of-art. For that, several study features that characterized the Electroencephalogram (EEG) signals “slow-down” were extracted and presented to the ANN entries in order to classify the dataset. The classification results achieved in the present work are promising concerning AD early diagnosis and they show that EEG can be a good tool for AD detection (Controls (C) vs AD: accuracy 95%; C vs Mild-cognitive Impairment (MCI): accuracy 77%; MCI vs AD: accuracy 83%; All vs All: accuracy 90%). The authors gratefully acknowledge the opportunity to publish this work to the International Conference on Health and Social Care Information Systems and Technologies Co-chairs and to Neurological Unity of “Hospital de São João” for supplying the EEG signals used in this study. info:eu-repo/semantics/publishedVersion

Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017.

Alzheimer’s Disease (AD) is considered one of the most debilitating illness in modern societies and the leading cause of dementia. This study is a new approach to detect early AD Electroencephalogram (EEG) temporal events in order to improve early AD diagnosis. For that, Self-Organized Maps (SOM) were used, and it was found that there are sequences of EEG energy variation, characteristic of AD, that appear with high incidence in Mild Cognitive Impairment (MCI) patients. Those AD events are related to the first cognitive changes in patients that interfered with the normal EEG signal pattern. Moreover, there are significant differences concerning the propagation time of those events between the study groups(p=0.0082

Alzheimer’s disease (AD) is currently an incurable illness that causes dementia and patient’s condition is progressively worse and it represents one of the greatest public health challenges worldwide. The main objective of this work was to develop a classification methodology for EEG signals to improve discrimination amongst patients at varying stages of the illness, Mild Cognitive Impairment (MCI) patients and non-patients either in order to obtain a more reliable methodology to identify AD in early stages. For this purpose, a surrogate decision tree classifier was used with 2 different ways of cross-validation (leave-one-out-crossvalidation and 10-fold-cross validation). The EEG studied features were the values of maxima (NMax) and minima (NMin), the zero-crossing (Zcr) rate, the mean derivative value at a point (Mdif), the Relative Power (RP) in each of the conventional bands and finally the spectral ratios (r). The best classification was obtained with vectors of 10 features as classifier entries in a leaveone- out-cross validation process, reaching 0.934 AUC, a sensitivity of 86.19%, a specificity of 99.35%, an accuracy of 94.88%, with a low out-of-sample classification error of 6.98% which indicates that the classifier generalizes fairly well. info:eu-repo/semantics/publishedVersion

Made available in DSpace on 2016-01-28T16:54:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2014. Added 1 bitstream(s) on 2016-01-28T17:01:49Z : No. of bitstreams: 1 ISSN2317-3009-2014-03-03-84-93.pdf: 529557 bytes, checksum: 3ba04b22e2e54bd93a971151336e755b (MD5) A má oclusão de Classe III é a relação oclusal menos comum, abrangendo menos de 5% da população. Existem diversas formas de tratamento na má oclusão de Classe III. Dependendo da forma de como a Classe III se expressa e da idade do paciente, os tratamentos poderão ser ortopédicos, ortodônticos ou ortodônticos cirúrgicos. Objetivou-se uma revisão de literatura dos últimos 10 anos sobre as formas de tratamento compensatório da má oclusão de Classe III. Foram encontrados vários artigos publicados entre 04/2003 e 04/2013 na base de dados Pubmed a partir da palavra chave “Class III malocclusion”. Porém, foram selecionados 19 artigos que abordavam somente o tratamento compensatório de Classe III. Com base nos artigos selecionados concluiu-se que o tratamento da má oclusão de Classe III de crianças antes do pico de crescimento puberal tem melhor prognóstico com maiores efeitos ortopédicos e menores efeitos ortodônticos. A opção de tratamento ideal para essa condição é a expansão rápida da maxila associada a tração reversa da mesma. O tratamento da má oclusão de Classe III em jovens após o pico de crescimento puberal tem prognóstico duvidoso. Pode-se optar em tratamento de expansão rápida da maxila e tração reversa da mesma ou com aparelho fixo, porém, os efeitos ortopédicos podem ser iguais ou menores do que os efeitos ortodônticos, dependendo da idade do paciente. Dependendo do grau da má oclusão de Classe III em adultos, o tratamento consistirá em compensações dentárias ou cirurgia ortognática. Class III malocclusion is less common occlusal relationship, covering less than 5% of the population. There are various forms of treatment in Class III malocclusion. Depending on how the form is expressed Class III and age of the patient, the therapy may be orthopedic and orthodontic surgical orthodontics. The objective was to review the literature of the last 10 years about ways to compensatory treatment of Class III malocclusion. Several articles were published between 04/2003 and 04/2013 in the Pubmed database from the keyword "Class III malocclusion". However, only 19 articles that addressed the compensatory treatment of Class III were selected. Based on the selected items it was concluded that the treatment of Class III malocclusions in children before the peak of pubertal growth has better prognosis with greater effects orthopedic and orthodontic minor effects. The ideal treatment option for this condition is the Rapid maxillary expansion associated with maxillary protraction of the same. The treatment of Class III malocclusion in young people after the peak of pubertal growth is doubtful prognosis. You can opt to treat rapid maxillary expansion and maxillary protraction of the same or fixed appliance, however, orthopedic effects can be the same or smaller than the orthodontic effects, depending on the age of the patient. Depending on the degree of Class III malocclusion in adults, the treatment will consist of dental compensations or orthognathic surgery. La maloclusión de Clase III es la relación oclusal menos común, que cubre menos del 5% de la población. Hay varias formas de tratamiento en la maloclusión de Clase III. Dependiendo de cómo se expresa la forma de Clase III y la edad del paciente, la terapia puede ser la ortodoncia quirúrgica de ortopedia y ortodoncia. El objetivo fue revisar la literatura de los últimos 10 años sobre las formas de trato compensatorio de maloclusión de Clase III. Se encontraron varios artículos publicados entre 04/2003 y 04/2013 en la base de datos Pubmed de la palabra clave "Clase III maloclusión". Sin embargo, sólo se seleccionaron 19 artículos que abordan el tratamiento compensatorio de Clase III. Con base en los elementos seleccionados se concluyó que el tratamiento de las maloclusiones de clase III en los niños antes de que el pico de crecimiento puberal tiene mejor pronóstico con mayores efectos ortopédicos y efectos menores de ortodoncia. La opción de tratamiento ideal para esta condición es la expansión maxilar rápida asociado con la protracción maxilar del mismo. El tratamiento de la maloclusión de Clase III en los jóvenes después del pico de crecimiento puberal es el pronóstico dudoso. Usted puede optar para tratar la expansión rápida del maxilar y la protracción maxilar del mismo o fijo aparato, sin embargo, los efectos ortopédicos pueden ser el mismo o menor que los efectos de ortodoncia, dependiendo de la edad del paciente. Dependiendo del grado de maloclusión de Clase III en adultos, el tratamiento consistirá en compensaciones dentales o cirugía ortognática. Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Odontologia de Araçatuba, Departamento de Odontologia Infantil e Social, Araçatuba, Rua José Bonifácio - 1193, Vila Mendonça, CEP 16015050, SP, Brasil Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Odontologia de Araçatuba, Departamento de Odontologia Infantil e Social, Araçatuba, Rua José Bonifácio - 1193, Vila Mendonça, CEP 16015050, SP, Brasil