KMID : 1100220240230010001
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Dementia and Neurocognitive Disorders 2024 Volume.23 No. 1 p.1 ~ p.10
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Harnessing the Power of Voice: A Deep Neural Network Model for Alzheimer¡¯s Disease Detection
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Park Chan-Young
Kim Min-Soo Shim Yong-Soo Ryoo Na-Young Choi Hyun-Joo Jeong Ho-Tae Yun Gi-Hyun Lee Hun-Boc Kim Hyung-Ryul Kim Sang-Yun Youn Young-Chul
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Abstract
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Background and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer¡¯s disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer¡¯s disease dementia (ADD).
Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma.
Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%¡¾1.6% when evaluated against unseen test dataset.
Conclusions: Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.
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KEYWORD
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Voice, Machine Learning, Artificial Intelligence, Alzheimer Disease, Phonetics
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