Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer’s Disease Diagnosis

Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer’s Disease Diagnosis

Despite growing evidence of aberrant neuronal complexity in Alzheimer’s disease (AD), it remains unclear how this variation arises. Neural oscillations reportedly comprise different functions depending on their own properties. Therefore, in this study, we investigated details of the complexity of ne...

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Bibliographic Details
Main Authors: Naoki Furutani, Yuta Nariya, Tetsuya Takahashi, Sarah Noto, Albert C. Yang, Tetsu Hirosawa, Masafumi Kameya, Yoshio Minabe, Mitsuru Kikuchi
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-09-01
Series:Frontiers in Psychiatry
Subjects:
alpha oscillation
Alzheimer’s disease (AD)
amplitude complexity
ensemble empirical mode decomposition (EEMD)
magnetoencephalography (MEG)
multiscale entropy (MSE)
Online Access:https://www.frontiersin.org/article/10.3389/fpsyt.2020.531801/full

Internet

https://www.frontiersin.org/article/10.3389/fpsyt.2020.531801/full

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