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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Central Auditory Processing Tests as Diagnostic Tools for the Early Identification of Elderly Individuals with Mild
Bahram Jalaei1, Ayub Valadbeigi1, Rasool Panahi1
1Department of Audiology, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran.
Background And Objectives:
Mild cognitive impairment (MCI) is a disorder that usually occurs in the elderly, leading to dementia in some progressive cases. The purpose of this study is to examine the utility of central auditory processing tests as early diagnostic tools for identifying the elderly with MCI.
Subject And Methods:
This study was conducted on 20 elderly patients with MCI and 20 healthy matched peers. The speech perception ability in a quiet environment and in the presence of background noise and also temporal resolution were assessed by using Speech Perception in Noise (SPIN) and Gap in Noise (GIN) tests, respectively.
Results:
The results indicated that the ability to understand speech in a quiet environment did not differ significantly between the two groups. However, SPIN at the three signal-tonoise ratios and the temporal resolution scores were significantly different between the two groups (p<0.001).
Conclusions:
Individuals with MCI appear to have poorer speech comprehension in noise and a lower temporal resolution than those of the same age, but without cognitive defects. Considering the utility of these tests in identifying cognitive problems, we propose that since the GIN test seems to be less influenced by intervening factors, this test can therefore, be a useful tool for the early screening of elderly people with cognitive problems.
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