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

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

Speech- and Language-Based Classification of Alzheimer’s Disease: A Systematic Review

Background: Alzheimer’s disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer’s Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.This work was partially supported by FCT- UIDB/04730/2020 project.info:eu-repo/semantics/publishedVersio

Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: A fractal dimension approach

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

On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment

Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann-Whitney U test and Support Vector Machine Attribute evaluation.This work has been supported by FEDER and MICINN, TEC2016-77,791-C4-2-R, and UPV/EHU-Basque Research Groups IT11156 and Basque Country EleKin Research Grou

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

Hondatze kognitibo arinaren detekzio goiztiarrerako hizketa ezagutza automatikoan oinarrituriko ekarpenak

302 p.Alzheimerdun gaixoengan, mintzamena ez ezik, erantzun emozionala ere kaltetu egiten da. Emozioak giza gogoaren arkitekturarekin zerikusia dituzten prozesu kognitiboak dira, eta erabakiak hartzearekin eta oroimenaren kudeaketa edota arretarekin zerikusia dute, eta aldi berean ere, horiek hertsiki lotuta dauden komunikazioarekin. Hortaz, erantzun eta kudeaketa emozionalak ere badira gaitzaren hasierako fase horietan nahasten diren beste komunikazio-elementu batzuk, eta disfluentzia bezala, emozio-erantzuna narriadura kognitiboa neurtzeko adierazlea izan daiteke.Hortaz, zenbait atazaren bidez sortutako ahots-laginen azterketak direla medio, disfluentzia eta emozio-erantzuna jaso daitezke. Hizkuntzarekiko independenteak diren parametroak bildu eta horien hizkeraren nahasmenduak ezaugarritu badaitezke, ekarpena lagungarria izan daiteke diagnostikoa egingo duten espezialistentzat.Lehengaiak ahots-laginak direnez, ingurune kliniko zein etxeko ingurunean egindako ataza desberdinen bidez grabazioak egin eta datu-baseak osatu dira, osasun-guneen irizpide etikoak kontuan hartuta eta. Datu-base horien ikerketaren bidez, galera kognitiboaren garapena neurtu, kuantifikatu, balioztatu eta sailkatu nahi da. Gaitzaren etapa desberdinak hautematen laguntzeko ekarpena egin nahi da, eta horretarako, hizkuntzarekiko independenteak diren parametroen azterketa automatikorako teknika eta metodologiak garatu dira. Mintzamen automatikoaren analisian oinarritutako multi-hurbilketa ez-lineala egin da, zeinak hizketa-analisian erabiltzen diren denborazko serieen konplexutasunaren neurtze kuantitatiboa eman diezaguke

Hondatze kognitibo arinaren detekzio goiztiarrerako hizketa ezagutza automatikoan oinarrituriko ekarpenak

302 p.Alzheimerdun gaixoengan, mintzamena ez ezik, erantzun emozionala ere kaltetu egiten da. Emozioak giza gogoaren arkitekturarekin zerikusia dituzten prozesu kognitiboak dira, eta erabakiak hartzearekin eta oroimenaren kudeaketa edota arretarekin zerikusia dute, eta aldi berean ere, horiek hertsiki lotuta dauden komunikazioarekin. Hortaz, erantzun eta kudeaketa emozionalak ere badira gaitzaren hasierako fase horietan nahasten diren beste komunikazio-elementu batzuk, eta disfluentzia bezala, emozio-erantzuna narriadura kognitiboa neurtzeko adierazlea izan daiteke.Hortaz, zenbait atazaren bidez sortutako ahots-laginen azterketak direla medio, disfluentzia eta emozio-erantzuna jaso daitezke. Hizkuntzarekiko independenteak diren parametroak bildu eta horien hizkeraren nahasmenduak ezaugarritu badaitezke, ekarpena lagungarria izan daiteke diagnostikoa egingo duten espezialistentzat.Lehengaiak ahots-laginak direnez, ingurune kliniko zein etxeko ingurunean egindako ataza desberdinen bidez grabazioak egin eta datu-baseak osatu dira, osasun-guneen irizpide etikoak kontuan hartuta eta. Datu-base horien ikerketaren bidez, galera kognitiboaren garapena neurtu, kuantifikatu, balioztatu eta sailkatu nahi da. Gaitzaren etapa desberdinak hautematen laguntzeko ekarpena egin nahi da, eta horretarako, hizkuntzarekiko independenteak diren parametroen azterketa automatikorako teknika eta metodologiak garatu dira. Mintzamen automatikoaren analisian oinarritutako multi-hurbilketa ez-lineala egin da, zeinak hizketa-analisian erabiltzen diren denborazko serieen konplexutasunaren neurtze kuantitatiboa eman diezaguke

Automatic Analysis of Archimedes’ Spiral for Characterization of Genetic Essential Tremor Based on Shannon’s Entropy and Fractal Dimension

Among neural disorders related to movement, essential tremor has the highest prevalence; in fact, it is twenty times more common than Parkinson's disease. The drawing of the Archimedes' spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users; and taking into account social and economic points of view, it could be very helpful in real complex environments.This research was partially funded by the Basque Goverment, the University of the Basque Country by the IT1115-16 project-ELEKIN, Diputacion Foral de Gipuzkoa, University of Vic-Central University of Catalonia under the research grant R0947, and the Spanish Ministry of Science and Innovation TEC2016-77791-C04-R