Dementia

A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG.

In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroenc...

Screening for Early-Stage Alzheimer's Disease Using Optimized Feature Sets and Machine Learning.

BACKGROUND: Detecting early-stage Alzheimer's disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive assessments that are sensitive to very mild impairment, a likely contributor to the high rate of undetected dementia.

Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study.

BACKGROUND: Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impac...

Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer's Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach.

BACKGROUND: Olfactory dysfunction (OD) is an early symptom of Alzheimer's disease (AD). However, olfactory testing is not commonly performed to test O...

Immediate Beneficial Effect of Makeup Therapy on Behavioral and Psychological Symptoms of Dementia and Facial Appearance Analyzed by Artificial Intelligence Software.

BACKGROUND: Possible benefits of makeup therapy, in terms of immediate and late effects on cognitive and affective functions, have not been fully prov...

Identification of Blood-Based Glycolysis Gene Associated with Alzheimer's Disease by Integrated Bioinformatics Analysis.

BACKGROUND: Alzheimer's disease (AD) is one of many common neurodegenerative diseases without ideal treatment, but early detection and intervention ca...

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test.

BACKGROUND: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test ...

A Role for Prior Knowledge in Statistical Classification of the Transition from Mild Cognitive Impairment to Alzheimer's Disease.

BACKGROUND: The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer's disease and relat...

An Optimized Decision Tree with Genetic Algorithm Rule-Based Approach to Reveal the Brain's Changes During Alzheimer's Disease Dementia.

BACKGROUND: It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on...

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.

Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To s...

A systematic literature review of automatic Alzheimer's disease detection from speech and language.

OBJECTIVE: In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing....

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.

A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjec...

Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning.

Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive func...

A new method to predict anomaly in brain network based on graph deep learning.

Functional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent y...

A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular dis...

Segmentation of Tau Stained Alzheimers Brain Tissue Using Convolutional Neural Networks.

Alzheimers disease is characterized by complex changes in brain tissue including the accumulation of tau-containing neurofibrillary tangles (NFTs) and...

Deep learning with long short-term memory networks for classification of dementia related travel patterns.

Wandering pattern classification is important for early recognition of cognitive deterioration and other health conditions in people with dementia (PW...

Automatic Extraction of Risk Factors for Dialysis Patients from Clinical Notes Using Natural Language Processing Techniques.

Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascular diseases are risk factors for dialysis patients....

Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.

Identifying subtypes of Alzheimer's Disease (AD) can lead towards the creation of personalized interventions and potentially improve outcomes. In this...

Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process.

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's dise...