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

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

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

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

Dementia Detection Using LSTM and GRU

Neuro-degenerative infections, like dementia, can affect discourse, language, and the ability of correspondence.A new report to work on the precision of dementia identification examined the utilization of conversation analysis(CA) of meetings between patients and nervous system specialists to recognize reformist neuro-degenerative(ND) memory issues patients and those with (non-reformist) FMD (Functional Memory Disorder). In any case,manual CA is expensive for routine clinical use and hard proportional. In this work, we present an early dementiadiscovery framework utilizing discourse acknowledgment and examination dependent on NLP method andacoustic component handling strategy apply on various element extraction and learning using LSTM (LongShort-Term Memory) and GRU which strikingly catches the transient provisions and long haul conditions fromauthentic information to demonstrate the abilities of grouping models over a feed-forward neural organization inestimating discourse investigation related issues. Dementia dataset is taken where the audio file is considered forspeech recognition analysis on basis of that data is generated and it is predefined given in dementia data databank.That audio file is converted to text based on speech analysis. Using LSTM and GRU gives efficient results

Dementia detection using automatic analysis of conversations

Neurogenerative disorders, like dementia, can affect a person's speech, language and as a consequence, conversational interaction capabilities. A recent study, aimed at improving dementia detection accuracy, investigated the use of conversation analysis (CA) of interviews between patients and neurologists as a means to differentiate between patients with progressive neurodegenerative memory disorder (ND) and those with (non-progressive) functional memory disorders (FMD). However, doing manual CA is expensive and difficult to scale up for routine clinical use. In this paper, we present an automatic classification system using an intelligent virtual agent (IVA). In particular, using two parallel corpora of respectively neurologist- and IVA-led interactions, we show that using acoustic, lexical and CA-inspired features enable ND/FMD classification rates of 90.0% for the neurologist-patient conversations, and 90.9% for the IVA-patient conversations. Analysis of the differentiating potential of individual features show that some differences exist between the IVA and human-led conversations, for example in average turn length of patients

Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods

Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection. In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application. The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings. Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection