@article{Marzban_Dastgheib_Lithgow_Moussavi_2023, title={Selecting the Most Characteristic Vestibular Stimuli to be Used for Alzheimer’s Subtype Diagnosis}, volume={45}, url={https://proceedings.cmbes.ca/index.php/proceedings/article/view/1013}, abstractNote={<div class="w-full border-b border-black/10 dark:border-gray-900/50 text-gray-800 dark:text-gray-100 group bg-gray-50 dark:bg-[#444654]"> <div class="text-base gap-4 md:gap-6 m-auto md:max-w-2xl lg:max-w-2xl xl:max-w-3xl p-4 md:py-6 flex lg:px-0"> <div class="relative flex w-[calc(100%-50px)] flex-col gap-1 md:gap-3 lg:w-[calc(100%-115px)]"> <div class="flex flex-grow flex-col gap-3"> <div class="min-h-[20px] flex flex-col items-start gap-4 whitespace-pre-wrap"> <div class="markdown prose w-full break-words dark:prose-invert light"> <p>This study aimed to find the most effective tilts in electrovestibulography (EVestG) to differentiate Alzheimer’s disease (AD) from Alzheimer’s disease with cerebrovascular disease pathology (AD-CVD) using principal component analysis (PCA). EVestG records responses to physical stimuli (tilts), and the goal was to rank these in terms of their ability to separate AD from AD-CVD. The study analyzed EVestG signals from 28 AD and 24 AD-CVD individuals. PCA was used to determine the mean contribution of tilts to the first 26 principle components, which represent 81% of the data variation. The algorithm was tested on 80% of a randomly selected database and found that the Supine Up/down and (sitting) Up/down tilts, which predominantly stimulate the utricle and saccule respectively, were the most effective in separating AD from AD-CVD</p> </div> </div> </div> </div> </div> </div>}, journal={CMBES Proceedings}, author={Marzban, Sadegh and Dastgheib, Zeinab and Lithgow, Brian and Moussavi, Zahra}, year={2023}, month={May} }