On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Noves perspectives en el diagnòstic del deteriorament cognitiu lleu i la malaltia de l'Alzheimer

Els criteris per al diagnòstic clínic de la malaltia d’Alzheimer es van establir el 1984 pel National Institute of Neurological and Communicative Disorders and Stroke (NINCDS) i la Alzheimer’s Disease and Related Disorders Association (ADRDA). D’aplicació continuada fins a l’actualitat, aquests criteris estan quedant obsolets i per tant des de diversos àmbits s’ha abogat per una revisió profunda dels mateixos. Tres grups d’experts formats per reconeguts especialistes del National Institute on Aging (NIA) i la Alzheimer’s Association proposen un conjunt de recomanacions per modificar aquests criteris en l’àmbit de la investigació clínica. Dues diferències remarcables s’inclouen en aquests nous criteris: la incorporació de biomarcadors i la formalització de diferents estadis de la malaltia d’Alzheimer. D’aquesta manera, el deteriorament cognitiu lleu s’incorpora al procés diagnòstic com un estadi més de la patologia. Tanmateix, aquests criteris es troben en revisió i, de moment sols son aplicables en l’àmbit de recerca per tal d’arribar a un consens definitiu que permeti la modificació definitiva dels criteris clínics universals a aplicar. En aquest article es presenten els principals avenços en la investigació referents a la malaltia d’Alzheimer i al Deteriorament Cognitiu lleu per tal d’emmarcar els nous criteris de recerca.

Indicadores cognitivos del proceso de envejecimiento en las personas con síndrome de Down

Fundamentos: el aumento de la esperanza de vida en las personas con síndrome de Down plantea nuevos interrogantes sobre el proceso de su envejecimiento. La revisión bibliográfica sobre el tema muestra acuerdo sobre algunos aspectos diferenciales respecto a la población con discapacidad psíquica y la población general. Entre ellos, destacamos dos: a) la precocidad del inicio del proceso y b) el aumento de la probabilidad de desarrollar un envejecimiento patológico a causa de la demencia tipo Alzheimer. El objetivo del presente estudio se centra en la aportación de datos que ayuden a delimitar los posibles indicadores del declive cognitivo de las personas adultas con síndrome de Down relacionados con un posible deterioro propio de la demencia tipo Alzheimer. Método: el estudio se realiza en una muestra de 84 personas adultas con discapacidad psíquica, 42 de las cuales presentan el síndrome de Down. La media de edad se sitúa entorno a los 40 años y su nivel de retraso mental es medio. Se aplica de forma longitudinal en un período de dos años el test d’Aptituds Cognitives per a Deficiència del 65% (Castelló, Carrillo y Barnosell, 1996). Se utiliza un diseño factorial mixto de medidas repetidas controlando las variables etiología, edad cronológica, nivel de retraso mental y paso del tiempo. Resultados: se observa con el paso del tiempo, un declive cognitivo significativo de las personas con síndrome de Down de más de 38 años y nivel de retraso mental ligero respecto al grupo con discapacidad psíquica de referencia. Los indicadores cognitivos se sitúan preferentemente en las áreas de lenguaje y coordinación visomotora. Conclusiones: las personas con síndrome de Down de más de 38 años y nivel de retraso mental ligero manifiestan una probabilidad mayor de desarrollar un declive cognitivo relacionado con un probable deterioro propio de la demencia Alzheimer.

EEG Windowed statitical wavelet deviation for estimation of muscular artifacts

Electroencephalographic (EEG) recordings are, most of the times, corrupted by spurious artifacts, which should be rejected or cleaned by the practitioner. As human scalp EEG screening is error-prone, automatic artifact detection is an issue of capital importance, to ensure objective and reliable results. In this paper we propose a new approach for discrimination of muscular activity in the human scalp quantitative EEG (QEEG), based on the time-frequency shape analysis. The impact of the muscular activity on the EEG can be evaluated from this methodology. We present an application of this scoring as a preprocessing step for EEG signal analysis, in order to evaluate the amount of muscular activity for two set of EEG recordings for dementia patients with early stage of Alzheimer’s disease and control age-matched subjects.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Hipertensión arterial y enfermedad de Alzheimer

Hypertension (HT) is the most prevalent cardiovascular risk factor associated with dementia and Alzheimer disease (AD). The evidence about the association within hight blood pressure (BP) level in the middle age and dementia and Alzheimer’s disease incidence in the advanced age has increased. Longitudinal studies show that in the previous years of onset AD, BP is similar or lower in the patients who develop AD with regard to those who not develop. When patients has developed AD the case is the same that the previous one. Most studies show that BP reduction is beneficious to prevent cognitive impairment, dementia and AD. Is spite of some discordant studies, health authorities recommend to treat isolated systolic HT with an ‘A’ evidence degree, level 1, to prevent AD. Animal experimentation prove different ways to act of antihypertensive drugs in the AD prevention and has established the pathophysiological bases in this relation, until now only showed through various clinical studies

Estudi sobre la qualitat de vida de les persones amb demència ateses a la Unitat d'avaluació de deteriorament cognitiu de l'Hospital de la Santa Creu

OBJECTIU: determinar la qualitat de vida de les persones amb demència ateses en una unitat avaluadora de deteriorament cognitiu. MÈTODE: estudi descriptiu transversal amb una mostra consecutiva no probabilística, formada per 42 persones amb demència tipus Alzheimer lleu o moderada i els seus cuidadors. La Qualitat de Vida (QV) es va avaluar amb el qüestionari QoL-AD (Quality of Life Alzheimer’s Disease) en les versions per al pacient (QoL-ADp) i per al cuidador (QoL-ADc). RESULTATS: la mitjana de puntuació del QoL-ADp va ser de 35,38 punts (DE = 5,24) i del QoL-Adc, de 30,60 (DE 5,33). La diferència entre aquests resultats és significativa (p<0,001). Els pacients amb simptomatologia depressiva i els seus cuidadors van puntuar significativament més baix el QoL-AD (p<0,001). En les freqüències per ítems del QoL-ADp s’observa que: més del 75% van valorar com a bona/excel·lent les condicions de vida, família, matrimoni/relació estreta, vida social, situació financera i vida en general; el 61% valoraren bona/excel·lent la capacitat per realitzar tasques a casa; prop del 50% pensava que l’estat d’ànim, l’energia, la salut física, la capacitat per fer coses per diversió i la visió de si mateixos era dolenta/regular, i el 85,7% opinava que la seva memòria era dolenta/regular. CONCLUSIONS: els resultats obtinguts en el QoL-AD no difereixen dels obtinguts en altres investigacions. Suggereixen que les intervencions que genera l’avaluació de la QV en la pràctica clínica inclouen aspectes centrats pròpiament en la malaltia i aspectes vinculats amb les relacions socials.