Automatic Detection of Dementia and related Affective Disorders through Processing of Speech and Language

In 2019, dementia is has become a trillion dollar disorder. Alzheimer’s disease (AD) is a type of dementia in which the main observable symptom is a decline in cognitive functions, notably memory, as well as language and problem-solving. Experts agree that early detection is crucial to effectively develop and apply interventions and treatments, underlining the need for effective and pervasive assessment and screening tools. The goal of this thesis is to explores how computational techniques can be used to process speech and language samples produced by patients suffering from dementia or related affective disorders, to the end of automatically detecting them in large populations us- ing machine learning models. A strong focus is laid on the detection of early stage dementia (MCI), as most clinical trials today focus on intervention at this level. To this end, novel automatic and semi-automatic analysis schemes for a speech-based cogni- tive task, i.e., verbal fluency, are explored and evaluated to be an appropriate screening task. Due to a lack of available patient data in most languages, world-first multilingual approaches to detecting dementia are introduced in this thesis. Results are encouraging and clear benefits on a small French dataset become visible. Lastly, the task of detecting these people with dementia who also suffer from an affective disorder called apathy is explored. Since they are more likely to convert into later stage of dementia faster, it is crucial to identify them. These are the fist experiments that consider this task us- ing solely speech and language as inputs. Results are again encouraging, both using only speech or language data elicited using emotional questions. Overall, strong results encourage further research in establishing speech-based biomarkers for early detection and monitoring of these disorders to better patients’ lives.Im Jahr 2019 ist Demenz zu einer Billionen-Dollar-Krankheit geworden. Die Alzheimer- Krankheit (AD) ist eine Form der Demenz, bei der das Hauptsymptom eine Abnahme der kognitiven Funktionen ist, insbesondere des Gedächtnisses sowie der Sprache und des Problemlösungsvermögens. Experten sind sich einig, dass eine frühzeitige Erkennung entscheidend für die effektive Entwicklung und Anwendung von Interventionen und Behandlungen ist, was den Bedarf an effektiven und durchgängigen Bewertungsund Screening-Tools unterstreicht. Das Ziel dieser Arbeit ist es zu erforschen, wie computergest ützte Techniken eingesetzt werden können, um Sprach- und Sprechproben von Patienten, die an Demenz oder verwandten affektiven Störungen leiden, zu verarbeiten, mit dem Ziel, diese in großen Populationen mit Hilfe von maschinellen Lernmodellen automatisch zu erkennen. Ein starker Fokus liegt auf der Erkennung von Demenz im Frühstadium (MCI), da sich die meisten klinischen Studien heute auf eine Intervention auf dieser Ebene konzentrieren. Zu diesem Zweck werden neuartige automatische und halbautomatische Analyseschemata für eine sprachbasierte kognitive Aufgabe, d.h. die verbale Geläufigkeit, erforscht und als geeignete Screening-Aufgabe bewertet. Aufgrund des Mangels an verfügbaren Patientendaten in den meisten Sprachen werden in dieser Arbeit weltweit erstmalig mehrsprachige Ansätze zur Erkennung von Demenz vorgestellt. Die Ergebnisse sind ermutigend und es werden deutliche Vorteile an einem kleinen französischen Datensatz sichtbar. Schließlich wird die Aufgabe untersucht, jene Menschen mit Demenz zu erkennen, die auch an einer affektiven Störung namens Apathie leiden. Da sie mit größerer Wahrscheinlichkeit schneller in ein späteres Stadium der Demenz übergehen, ist es entscheidend, sie zu identifizieren. Dies sind die ersten Experimente, die diese Aufgabe unter ausschließlicher Verwendung von Sprache und Sprache als Input betrachten. Die Ergebnisse sind wieder ermutigend, sowohl bei der Verwendung von reiner Sprache als auch bei der Verwendung von Sprachdaten, die durch emotionale Fragen ausgelöst werden. Insgesamt sind die Ergebnisse sehr ermutigend und ermutigen zu weiterer Forschung, um sprachbasierte Biomarker für die Früherkennung und Überwachung dieser Erkrankungen zu etablieren und so das Leben der Patienten zu verbessern

Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance

International audienc

Using Neural Word Embeddings in the Analysis of the Clinical Semantic Verbal Fluency Task

International audienceThe Semantic Verbal Fluency Task is a common neuropsychological assessment for cognitive disorders: patients are prompted to name as many words from a semantic category as possible in a time interval; the count of correctly named concepts is assessed. Patients often organise their retrieval around semantically related clusters. The definition of clusters is usually based on handmade taxonomies and the patient's performance is manually evaluated. In order to overcome limitations of such an approach, we propose a statistical method using distributional semantics. Based on transcribed speech samples from 100 French elderly, 53 diagnosed with Mild Cognitive Impairment and 47 healthy, we used distributional semantic models to cluster words in each sample and compare performance with a taxonomic baseline approach in a realistic classification task. The distributional models outperform the baseline. Comparing different linguistic corpora as basis for the models, our results indicate that models trained on larger corpora perform better

Remote data collection speech analysis and prediction of the identification of Alzheimer’s disease biomarkers in people at risk for Alzheimer’s disease dementia: the Speech on the Phone Assessment (SPeAk) prospective observational study protocol

International audienceIntroduction Identifying cost-effective, non-invasive biomarkers of Alzheimer's disease (AD) is a clinical and research priority. Speech data are easy to collect, and studies suggest it can identify those with AD. We do not know if speech features can predict AD biomarkers in a preclinical population. Methods and analysis The Speech on the Phone Assessment (SPeAk) study is a prospective observational study. SPeAk recruits participants aged 50 years and over who have previously completed studies with AD biomarker collection. Participants complete a baseline telephone assessment, including spontaneous speech and cognitive tests. A 3-month visit will repeat the cognitive tests with a conversational artificial intelligence bot. Participants complete acceptability questionnaires after each visit. Participants are randomised to receive their cognitive test results either after each visit or only after they have completed the study. We will combine SPeAK data with AD biomarker data collected in a previous study and analyse for correlations between extracted speech features and AD biomarkers. The outcome of this analysis will inform the development of an algorithm for prediction of AD risk based on speech features. Ethics and dissemination This study has been approved by the Edinburgh Medical School Research Ethics Committee (REC reference 20-EMREC-007). All participants will provide informed consent before completing any study-related procedures, participants must have capacity to consent to participate in this study. Participants may find the tests, or receiving their scores, causes anxiety or stress. Previous exposure to similar tests may make this more familiar and reduce this anxiety. The study information will include signposting in case of distress. Study results will be disseminated to study participants, presented at conferences and published in a peer reviewed journal. No study participants will be identifiable in the study results

Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task

The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines

Detecting Apathy in Older Adults with Cognitive Disorders Using Automatic Speech Analysis

International audienceBackground: Apathy is present in several psychiatric and neurological conditions and has been found to have a severe negative effect on disease progression. In older people, it can be a predictor of increased dementia risk. Current assessment methods lack objectivity and sensitivity, thus new diagnostic tools and broad-scale screening technologies are needed. Objective: This study is the first of its kind aiming to investigate whether automatic speech analysis could be used for characterization and detection of apathy. Methods: A group of apathetic and non-apathetic patients (n = 60) with mild to moderate neurocognitive disorder were recorded while performing two short narrative speech tasks. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, examined between the groups and compared to baseline assessments. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.Results: Correlations between apathy sub-scales and features revealed a relation between temporal aspects of speech and the subdomains of reduction in interest and initiative, as well as between prosody features and the affective domain. Group differences were found to vary for males and females, depending on the task. Differences in temporal aspects of speech were found to be the most consistent difference between apathetic and non-apathetic patients. Machine learning models trained on speech features achieved top performances of AUC = 0.88 for males and AUC = 0.77 for females. Conclusions: These findings reinforce the usability of speech as a reliable biomarker in the detection and assessment of apathy

Automatic Detection of Apathy using Acoustic Markers extracted from Free Emotional Speech

International audienceApathy is a frequent neuropsychiatric syndrome in people with dementia. It leads to diminished motivation for physical, cognitive and emotional activity. Apathy is highly underdiagnosed since its criteria have been only recently established and rely heavily on the subjective evaluation of human observers. In this paper we analyse speech samples from demented people with and without apathy. Speech was provoked by asking patients two emotional questions. Acoustic features were extracted and used in a classification task. The resulting models show performances of AUC = 0:71 and AUC = 0:63. This is a decent first step into the direction of automatic detection of apathy from speech. Usefulness of stimuli to elicit free speech is found to depend on patients gender

Validation of the Remote Automated ki:e Speech Biomarker for Cognition in Mild Cognitive Impairment:Verification and Validation following DiME V3 Framework

INTRODUCTION: Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer's disease. While most well-established measures for cognition might not fit tomorrow's decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society's V3 framework: verification, analytical validation, and clinical validation. METHODS: Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. RESULTS: Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. CONCLUSION: Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials