Abstract: Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.
Abstract: This research paper presents a framework for classifying Magnetic Resonance Imaging (MRI) images for Dementia. Dementia, an age-related cognitive decline is indicated by degeneration of cortical and sub-cortical structures. Characterizing morphological changes helps understand disease development and contributes to early prediction and prevention of the disease. Modelling, that captures the brain’s structural variability and which is valid in disease classification and interpretation is very challenging. Features are extracted using Gabor filter with 0, 30, 60, 90 orientations and Gray Level Co-occurrence Matrix (GLCM). It is proposed to normalize and fuse the features. Independent Component Analysis (ICA) selects features. Support Vector Machine (SVM) classifier with different kernels is evaluated, for efficiency to classify dementia. This study evaluates the presented framework using MRI images from OASIS dataset for identifying dementia. Results showed that the proposed feature fusion classifier achieves higher classification accuracy.
Abstract: This study aimed at investigating whether the
functional brain networks constructed using the initial EEG (obtained
when patients first visited hospital) can be correlated with the
progression of cognitive decline calculated as the changes of
mini-mental state examination (MMSE) scores between the latest and
initial examinations. We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions, and the network analysis based
on graph theory to investigate the organization of functional networks
in aMCI. Our finding suggested that higher integrated functional
network with sufficient connection strengths, dense connection
between local regions, and high network efficiency in processing
information at the initial stage may result in a better prognosis of the
subsequent cognitive functions for aMCI. In conclusion, the functional
connectivity can be a useful biomarker to assist in prediction of
cognitive declines in aMCI.