Dementia detection using weighted direction index histograms and SVM for clock drawing test

AbstractIncreasing the number of elderly persons who have dementia is one of the severe social problems. In Japan, the Ministry of Health, Labor and Welfare expects that the number of dementia patients will be around 5 million in 2025. It is also easily estimated that they require various living supports. Therefore, early detection and prevention of dementia are important. The authors have been developing a new system for quantitative and accurate evaluation of dementia. The basic concept of our system is evaluating a patient's dementia types and progression without awareness. To realize this, we are now developing the system using daily conversations, drawings, facial expressions and so on. In this paper, we focused on Clock Drawing Test (CDT) and proposed a dementia evaluation method for CDT. In the proposed method, Weighted Direction Index Histogram Method was used to extract features from given images, and Support Vector Machine (SVM) detected dementia cases from them. As a result of evaluation experiments, the proposed method could detect 97.1% of dementia cases correctly

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

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Resting state fMRI as a marker for progression from mild cognitive impairment to Alzheimer’s disease

The arc of progression in the most common neurological aiction known as Alzheimer's Disease (AD), is characterized by a prodromal stage of Mild Cognitive Impairment (MCI). MCI subjects have traditionally been diagnosed with a battery of cognitive tests, but in recent times two good biomarker predictors of incipient AD have been identied. The cerebrospinal uid levels of the protein residues amyloid-beta and phosphorylated tau can be quantied and directly relate to the imprint of associated pathologies in the brain. This work aims to elucidate the impact of tau- and amyloid-related pathologies on the functional networks of the brain, as gauged by a resting-state fMRI connectivity analysis. In this context, we aim to identify optimal model parameters that yield maximal contrast between subspecies of MCI and healthy controls, such as the most sensitive frequency interval for the Blood Oxygenation Level Dependent (BOLD) time-series and the resolution of whole-brain parcellation schemes. The connectivity analysis exposes the impact of biomarker pathology and outlines a tentative progression pattern, relating the decline of functional connectivity to increasingly pathological levels of biomarkers. A progression hypothesisis proposed, reviewing pattern progression in the light of neuronal communication breakdown and phase-lag. Furthermore, failure of key hubs are identied using graph theoretical centrality measures and the relative group separations with connectivity pattern is evaluated by means of support-vector machines. Relative healthy controls, MCI with non-pathological CSF levels of biomarkers exhibit a widespread pattern of reduced connectivity, likely due to a mix many dementia subtypes. MCI subjects with pathological amyloid CSF levels but normal values of tau, has a large set of failing links converg- ing on crucial network hubs: thalamus, caudate nucleus and putamen, and are additionally aected in key regions such as hippocampus. This nding supports the view of Alzheimer's progression in terms of global disconnection syndrome by failing hub regions. Furthermore, these patterns are man- ifested in relevant graph theoretical centrality measures. MCI with pathological levels of both CSF biomarkers produce the strongest contrast relative healthy controls, involving reduced connectivity beteween parietal and frontal areas, but also implicating areas linked with cognitive decline, such as hippocampus and posterior cingulate cortex. The similarity of this contrast to that of controls vs. Alzheimer's subjects, indicates the presence of a functional progression pattern with two biomarker levels. Our ndings merit further investigation of the biomarker progression line using larger cohorts further stratied with cognitive test scores

Dealing with heterogeneity in the prediction of clinical diagnosis

Le diagnostic assisté par ordinateur est un domaine de recherche en émergence et se situe à l’intersection de l’imagerie médicale et de l’apprentissage machine. Les données médi- cales sont de nature très hétérogène et nécessitent une attention particulière lorsque l’on veut entraîner des modèles de prédiction. Dans cette thèse, j’ai exploré deux sources d’hétérogénéité, soit l’agrégation multisites et l’hétérogénéité des étiquettes cliniques dans le contexte de l’imagerie par résonance magnétique (IRM) pour le diagnostic de la maladie d’Alzheimer (MA). La première partie de ce travail consiste en une introduction générale sur la MA, l’IRM et les défis de l’apprentissage machine en imagerie médicale. Dans la deuxième partie de ce travail, je présente les trois articles composant la thèse. Enfin, la troisième partie porte sur une discussion des contributions et perspectives fu- tures de ce travail de recherche. Le premier article de cette thèse montre que l’agrégation des données sur plusieurs sites d’acquisition entraîne une certaine perte, comparative- ment à l’analyse sur un seul site, qui tend à diminuer plus la taille de l’échantillon aug- mente. Le deuxième article de cette thèse examine la généralisabilité des modèles de prédiction à l’aide de divers schémas de validation croisée. Les résultats montrent que la formation et les essais sur le même ensemble de sites surestiment la précision du modèle, comparativement aux essais sur des nouveaux sites. J’ai également montré que l’entraînement sur un grand nombre de sites améliore la précision sur des nouveaux sites. Le troisième et dernier article porte sur l’hétérogénéité des étiquettes cliniques et pro- pose un nouveau cadre dans lequel il est possible d’identifier un sous-groupe d’individus qui partagent une signature homogène hautement prédictive de la démence liée à la MA. Cette signature se retrouve également chez les patients présentant des symptômes mod- érés. Les résultats montrent que 90% des sujets portant la signature ont progressé vers la démence en trois ans. Les travaux de cette thèse apportent ainsi de nouvelles con- tributions à la manière dont nous approchons l’hétérogénéité en diagnostic médical et proposent des pistes de solution pour tirer profit de cette hétérogénéité.Computer assisted diagnosis has emerged as a popular area of research at the intersection of medical imaging and machine learning. Medical data are very heterogeneous in nature and therefore require careful attention when one wants to train prediction models. In this thesis, I explored two sources of heterogeneity, multisite aggregation and clinical label heterogeneity, in an application of magnetic resonance imaging to the diagnosis of Alzheimer’s disease. In the process, I learned about the feasibility of multisite data aggregation and how to leverage that heterogeneity in order to improve generalizability of prediction models. Part one of the document is a general context introduction to Alzheimer’s disease, magnetic resonance imaging, and machine learning challenges in medical imaging. In part two, I present my research through three articles (two published and one in preparation). Finally, part three provides a discussion of my contributions and hints to possible future developments. The first article shows that data aggregation across multiple acquisition sites incurs some loss, compared to single site analysis, that tends to diminish as the sample size increase. These results were obtained through semisynthetic Monte-Carlo simulations based on real data. The second article investigates the generalizability of prediction models with various cross-validation schemes. I showed that training and testing on the same batch of sites over-estimates the accuracy of the model, compared to testing on unseen sites. However, I also showed that training on a large number of sites improves the accuracy on unseen sites. The third article, on clinical label heterogeneity, proposes a new framework where we can identify a subgroup of individuals that share a homogeneous signature highly predictive of AD dementia. That signature could also be found in patients with mild symptoms, 90% of whom progressed to dementia within three years. The thesis thus makes new contributions to dealing with heterogeneity in medical diagnostic applications and proposes ways to leverage that heterogeneity to our benefit

Audio Based Signal Processing and Computational Models for Early Detection and Prediction of Dementia and Mood Disorders

Neurodegenerative diseases causing dementia are known to affect a person’s speech and language. There is an increasing emphasis on earlier diagnosis of neurodegenerative disorders as evolving treatments are likely to be more effective before irreversible changes have occurred in the brain. The incorporation of novel methods based on the automatic analysis of speech signals may provide more information about a person’s ability to interact, which could contribute to the diagnostic process. This thesis demonstrates that purely acoustic features, extracted from recordings of patients’ answers to a neurologist’s questions in a specialist memory clinic can support the initial distinction between patients presenting with cognitive concerns attributable to progressive neurodegenerative disorders (ND) or Functional Memory Disorder (FMD, i.e., subjective memory concerns unassociated with objective cognitive deficits or a risk of progression). The thesis also shows that the acoustic features extracted from speech recordings for patients describing a picture can be used to construct a non-invasive and simple tool to infer early signs of dementia of Alzheimer's Disease (AD). This is further developed to firstly, identify patients with mild cognitive impairments and secondly to show a capability to assist the doctors in monitoring the progression of AD by predicting the MMSE scores in a longitudinal dataset. Further, novel acoustic features are introduced in this thesis that correlate with mood disorders such as major depression and bipolar. Combing the newly extracted features with state of the art features, led to developing a language-agnostic screening system for depression and bipolar disease. Finally, the results obtained show the discriminative power of purely acoustic approaches that could be integrated into diagnostic pathways for patients presenting with memory concerns or mood disorders. These approaches are computationally less demanding than methods focusing on linguistic elements of speech and language that require automatic speech recognition and understanding

Disease state index and disease state fingerprint: supervised learning applied to clinical decision support in Alzheimer’s disease

Due to scientific and technological advancements, investigations in modern medicine are producing more measurement data than ever before. Since a large amount of information exists, and it is also being produced at ever-increasing rates, no single person can digest all current knowledge of diseases. Data collected from large patient cohorts may contain valuable knowledge of diseases, which could be useful to clinicians when making diagnoses or choosing treatments. Making use of the large volumes of data in clinical decision-making requires ancillary help from information technologies, but such systems have not yet become widely available. This thesis addresses the challenge by proposing a computer-based decision support method that is suited to clinical use.This thesis presents the Disease State Index (DSI), a supervised machine learning method intended for the analysis of patient data. The DSI comprehensively compares patient data with previously diagnosed cases with or without a disease. Based on this comparison, the method provides an estimate of the state of disease progression in the patient. Interpreting the DSI is made possible by its visual counterpart, the Disease State Fingerprint (DSF), which allows domain experts to gain a comprehensive view of patient data and the state of the disease at a quick glance. In the design and development of these methods, both performance and applicability in clinical use were taken into account equally.Alzheimer’s disease (AD) is a slowly progressing neurodegenerative disease and one of the largest social and economic burdens in the world today, and it will continue to be so in the future. Studies with large patient cohorts have significantly improved our knowledge of AD during the last decade. This information should be made extensively available at memory clinics to maximize the benefits for diagnostics and treatment of the disease. The DSI and DSF methods proposed in this thesis were studied in the early diagnosis of AD and as a measure of disease progression in six original publications. The methods themselves and their implementation within a clinical decision support system, the PredictAD tool, were quantitatively evaluated with regard to their performance and potential benefits in clinical use. The results show that the methods and clinical decision support tool based on these methods can be used to follow disease progression objectively and provide earlier diagnoses of AD. These, in turn, could improve treatment efficacy due to earlier interventions and make drug trials more efficient by allowing better patient selection

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others