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

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

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

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

Acoustic analysis in mild cognitive diagnosis: systematic review of the literature in 2008–2020

Introducción: En investigaciones recientes se han descrito cambios en la producción de tono y timbre vocal que ocurren en la edad adulta tardía. Estos cambios indican alteraciones cognitivas tempranas, incluso en etapas preclínicas de deterioro cognitivo. Este estudio tiene como objetivo identificar hallazgos relevantes de la literatura con respecto al análisis acústico en adultos mayores con deterioro cognitivo. Material y métodos: Se realizó un estudio de revisión sistemática, en el que se consultaron las siguientes bases de datos: PlosOne, Science Direct, PubMed/pmc y Google Scholar. Se utilizaron buscadores como análisis acústico, enfermedad de Alzheimer, deterioro cognitivo leve, prosodia, análisis de voz y producción de voz. Adicionalmente, se incluyen artículos empíricos que describen el análisis acústico en adultos mayores con riesgo cognitivo. La evaluación fue realizada de forma independiente por dos evaluadores, quienes determinaron el riesgo de sesgo en la revisión. Se encontraron un total de 59 artículos relacionados con el tema, de los cuales 25 cumplieron con los criterios de inclusión. Resultados: Los artículos revisados ​​identificaron cambios en la prosodia lingüística y paralingüística, el timbre y la tonalidad vocal, que se asocian con el deterioro cognitivo en los adultos mayores. Conclusión: Los protocolos de estudio en el análisis acústico podrían ser una buena herramienta para apoyar el diagnóstico clínico diferencial del deterioro cognitivo en la edad adulta tardía y una buena oportunidad para identificar el riesgo en estadios preclínicos de demencia.Introduction: In recent research, changes in the vocal tone and timbre production that occur in late adulthood have been described. These changes indicate early cognitive disturbances, even in preclini-cal stages of cognitive decline. This study aims to identify relevant findings from the literature regarding acoustic analysis in elderly adults with cognitive impairment. Material and methods: A systematic review study was conducted, in which the following databases were consulted: PlosOne, Science Direct, PubMed/pmc, and Google Scholar. Search engines such as acoustic analysis, Alzheimer’s disease, mild cognitive impairment, prosody, voice analysis, and voice production were used. Additionally, empirical articles describing the acoustic analysis in elderly adults with cognitive risk are included. The evaluation was independently performed by two evaluators, who determined the risk of bias in the review. A total of 59 articles related to the topic were found, of which 25 met the inclusion criteria. Results: The reviewed articles identified changes in linguistic and paralinguistic prosody, timbre, and vocal tonality, which are associated with cognitive decline in the elderly. Conclusion: Study protocols in the acoustic analysis could be a good tool to support the differential clinical diagnosis of cognitive deterioration in late adulthood and a good opportunity to identify the risk in preclinical stages of dementia

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD