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Speech production in patients suffering of dementias of Alzheimer?s type is known to experience noticeable changes with respect to normative speakers. Classically this kind of speech has been described as presenting altered prosody, rhythmic pace, anomy, or impaired semantics. Phonation, conceived as the production of voice in voiced speech fragments remains as an unexplored field. The aim of the present paper is to open a preliminary study presenting biomechanical estimates from phonation produced by two patients (male and female) suffering Alzheimer?s Disease (AD), contrasted on two controls of both genders (CS: control speakers). A vocal fold biomechanical model is inverted to facilitate estimates of the vocal fold stiffness to analyze significant segments of phonated speech as long vowels and fillers. The estimates of both the AD patients and CS subjects are contrasted on a database of phonation features from a normative speaker population of both genders, as well as in paired tests contrasting AD and CS subjects. Results show the possibility of establishing significant discrimination between AD and CS when using f0, as well as vocal fold body stiffness, although this last feature seems to be more relevant and shows larger statistical significance.

Alzheimer’s disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Westerncountries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by usingautomatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selectedis based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work isfeature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. Thefeature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speechthat are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful whentraining data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters inthe feature vector in order to enhance the performance of the original system while controlling the computational cost.© 2014 Elsevier Ltd. All rights reserved.

The apolipoprotein E epsilon 4 allele (APOE4) and family history of dementia (FH) are well-known risk factors for the development of sporadic Alzheimer's disease. We assessed the effects of these risk factors on gray matter (GM) volume in 295 cognitively healthy middle-aged community-dwelling subjects. Voxel-based morphometry was used to study GM volume differences between high-and low-risk subjects, based on APOE4 carriership (n = 74), first-degree FH (n = 228), or both (n = 62). No significant results were found using a corrected p value. Using a more lenient threshold (p

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 through a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to the 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. The developed system obtains hopeful results for early diagnosis.

Alzheimer's disease (AD) is the most prevalent form of progressive degenerative dementia. Its diagnosis made by analyzing many biomarkers and test but nowadays a definitive confirmation requires a post-mortem examination of the patients' brain tissue. The purpose of this paper is to examine the potential of applying intelligent algorithms to the results obtained from non-invasive analysis methods on suspected patients in order to contribute to the improvement of both early diagnosis of AD and its degree of severity. This work deals with Emotional Response Automatic Analysis (ERAA) based on classical and new speech features: Emotional Temperature (ET) and Higuchi Fractal Dimension (FD). The method has the great advantage of being, in addition to non-invasive, of low cost and without any side effects. This is a pre-clinic studio oriented to validate future diagnosis tests and biomarkers. ERAA showed very satisfactory and promising results for the definition of features oriented to early diagnosis of AD.

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients. his work has been partially supported by a SAIOTEK from the Basque Government, University of Vic under the research grant R0904, and the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03. Iciar Martinez (Research Center for Experimental Marine Biology and Biotechnology-Plentziako Itsas Estazioa (PIE), University of the Basque Country & IKERBASQUE, Basque Foundation for Science

Alzheimer's disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Spontaneous Speech. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis. Nowadays our feature set offers some hopeful conclusions but fails to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is scarce. In this work, the Fractal Dimension (FD) of the observed time series is combined with lineal parameters in the feature vector in order to enhance the performance of the original system.

Alzheimer Disease (AD) is one of the most common dementia and their socio-economic relevance is growing. Its diagnosis is sometimes made by excluding other dementias, but definitive confirmation must await the study post-mortem with brain tissue of the patient. According to internationally accepted criteria, we can only speak about probable or possible Alzheimer's disease. The purpose of this paper is to contribute to improve early diagnosis of dementia and severity from automatic analysis performed by non-invasive automated intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET). These methodologies have the great advantage of being non invasive, low cost methodologies and have no side effects.

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

Alzheimer's Disease (AD) speech production is being classicaly studied under different aspects as prosody, rhythmic or semantics, phonation being an unexplored field. The present work is devoted to the study of several cases under the scope of phonation, including AD speakers and healthy controls. Temporal events with interest in phonation are studied. These include fillers (spontaneous voiced pauses or lengthenings), which may be characterized by phonation biomechanics. Discrimination capabilities of biomechanical estimates as fundamental frequency and vocal fold body stiffness are used in comparative hypothesis tests between the study cases and a normative database, and among themselves. The conclusions derived pose the problem of normative database design, and the continuation of the research including other biomechanical correlates.