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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A Comparison of Speech Features between Mild Cognitive Impairment and Healthy Aging Groups
Ko Woon Kim1,2, Seung-Hoon Na3, Young-Chul Chung2,4
1Department of Neurology, Jeonbuk National University Medical School and Hospital, Jeonju, Korea.
Background And Purpose:
Language dysfunction is a symptom common to patients with Alzheimer's disease (AD). Speech feature analysis may be a patient-friendly screening test for early-stage AD. We aimed to investigate the speech features of amnestic mild cognitive impairment (aMCI) compared to normal controls (NCs).
Methods:
Spoken responses to test questions were recorded with a microphone placed 15 cm in front of each participant. Speech samples delivered in response to four spoken test prompts (free speech test, Mini-Mental State Examination [MMSE], picture description test, and sentence repetition test) were obtained from 98 patients with aMCI and 139 NCs. Each recording was transcribed, with speech features noted. The frequency of the ten speech features assessed was evaluated to compare speech abilities between the test groups.
Results:
Among the ten speech features, the frequency of pauses (p=0.001) and mumbles (p=0.001) were significantly higher in patients with aMCI than in NCs. Moreover, MMSE score was found to negatively correlate with the frequency of pauses (r=-0.441, p<0.001) and mumbles (r=-0.341, p<0.001).
Conclusions:
Frequent pauses and mumbles reflect cognitive decline in aMCI patients in episodic and semantic memory tests. Speech feature analysis may prove to be a speech-based biomarker for screening early-stage cognitive impairment.
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