Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data

• URL: http://arxiv.org/abs/2007.03615v1
• Date: Fri, 3 Jul 2020 18:46:49 GMT
• Title: Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data
• Authors: Rafael Poyiadzi and Weisong Yang and Yoav Ben-Shlomo and Ian Craddock and Liz Coulthard and Raul Santos-Rodriguez and James Selwood and Niall Twomey
• Abstract summary: This paper seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in the textitearly stages of the disease. We introduce bespoke behavioural models and analyses of key symptoms and deploy these on a novel dataset of longitudinal sensor data from persons with MCI and AD. Preliminary findings show the relationship between levels of sleep quality and wandering can be subtly different between patients in the early stages of dementia and healthy cohabiting controls.
• Score: 3.390976757989381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This paper specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in the \textit{early} stages of the disease. We introduce bespoke behavioural models and analyses of key symptoms and deploy these on a novel dataset of longitudinal sensor data from persons with MCI and AD. We present preliminary findings that show the relationship between levels of sleep quality and wandering can be subtly different between patients in the early stages of dementia and healthy cohabiting controls.

Related papers

• Alzheimer's disease detection in PSG signals [2.8691549050152965]
  Alzheimers disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of early-stage AD. This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals for the early detection of AD. Our primary focus is on exploring semi-supervised Deep Learning techniques for the classification of EEG signals due to the limited data availability.
  arXiv  Detail & Related papers  (2024-04-04T15:56:23Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data [52.40058724040671]
  Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
  arXiv  Detail & Related papers  (2021-10-19T11:45:01Z)
• Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction [0.5735035463793008]
  This article overviews various categories of models used for Alzheimer's disease prediction with their respective learning methods. It establishes a comparative study of early prediction and detection Alzheimer's disease progression. Finally, a robust and precise detection model is proposed.
  arXiv  Detail & Related papers  (2021-10-10T11:44:16Z)
• Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans [10.120835953459247]
  Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training.
  arXiv  Detail & Related papers  (2020-11-28T14:25:30Z)
• Predicting Early Indicators of Cognitive Decline from Verbal Utterances [2.387625146176821]
  Dementia is a group of irreversible, chronic, and progressive neurodegenerative disorders resulting in impaired memory, communication, and thought processes. We measure the feasibility of using the linguistic characteristics of verbal utterances elicited during neuropsychological exams to distinguish between elderly control groups, people with MCI, people diagnosed with possible Alzheimer's disease (AD), and probable AD. Our experiments show that a combination of contextual and psycholinguistic features extracted by a Support Vector Machine improved distinguishing the verbal utterances of elderly controls, people with MCI, possible AD, and probable AD.
  arXiv  Detail & Related papers  (2020-11-19T02:24:11Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression [4.034948808542701]
  Alzheimer's disease (AD) is one of the major causes of dementia and is characterized by slow progression over several years. We propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status.
  arXiv  Detail & Related papers  (2020-05-06T08:08:00Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.