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allied
academies
March 14-16, 2019 | London, UK
12
th
International Conference on
8
th
International Conference on
Vascular Dementia and Dementia
Neurological Disorders and Stroke
Joint Event
&
Journal of Brain and Neurology | Volume 3
Framework utilizing machine learning to facilitate gait analysis as an indicator of Vascular Dementia
Arshia Khan
University of Minnesota Duluth, USA
V
ascular dementia (VD), the second most common type
of dementia, effects approximately 13.9 per cent of
people over the age of 71 in the United States alone. 26% of
individuals develop VD after being diagnosed with congestive
heart failure. Memory and cognition are increasingly affected
as dementia progresses. However, these are not the first
symptoms to appear in some types of dementia. Alterations in
gait and executive functioning have been associated Vascular
Cognitive Impairment (VCI). Research findings suggest that gait
may be one of the earliest affected systems during onset of VCI,
immediately following a vascular episode. The diagnosis tools
currently utilized for VD are focused on memory impairment,
which is only observed in later stages of VD. Hence we are
proposing a framework that isolates gait and executive
functioning analysis by applying machine learning to predict
VD before cognition is affected, so pharmacological treatments
can be used to postpone the onset of cognitive impairment.
Over a period of time, we hope to be able to develop prediction
algorithms that will not only identify but also predict vascular
dementia
.
e:
akhan@d.umn.edu