A report on Alzheimer's disease Action and Semantical Fluency

Abstract

The goal of this paper is to outline the relationship between onset and evident AD, as well as language functions and domains. With rising mortality and an older population, it is projected that almost 5.3 million individuals in India suffer from Alzheimer's disease. Even mild Alzheimer's disease causes linguistic alterations. Speech analysis is the best option since it accurately represents the speaker's brain function and data gathering is reasonably affordable when compared to brain imaging, blood tests, and other methods. Previously, researchers used the MMSE as one of the sources to detect the early start of Alzheimer's disease. We show that the progression from HA to AD is accompanied by a consistent pattern of speech changes, including an increase in duration and vocalization time, an increase in the number of pauses in speech, an emergence of modification in syllabic production, and reduced speech energy and intensity, resulting in speech disorder. Our planned research will provide a complete analysis of speech alterations in MCI and mild AD when compared to healthy ageing (HA), allowing harmful processes to be detected before clinical manifestations of AD. In the proposed work, we evaluate cognitive functions such as noting silence between words and their inaccuracy, which can play a significant role in recognizing the AD stages through emotional intelligence using a deep learning model. We can also add a cognitive phrase matching test to determine the severity of the Alzheimer's disease comprehension loss (AD) through emotional intelligence.