MCI Detection Based on Deep Learning with Voice Spectrogram
Author:
Affiliation:
1. Yuan Ze University,Department of Electrical Engineering,Taiwan
2. Institute of Public Health, National Yang Ming Chiao Tung University,Department of Psychiatry, Far Eastern Memorial Hospital,Taiwan
Funder
Qualcomm Technologies, Inc.
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9944984/9944985/09945032.pdf?arnumber=9945032
Reference8 articles.
1. M/EEG-Based Bio-Markers to Predict the MCI and Alzheimer's Disease: A Review From the ML Perspective
2. Brain Network Analysis of Compressive Sensed High-Density EEG Signals in AD and MCI Subjects
3. Toward a Better Estimation of Functional Brain Network for Mild Cognitive Impairment Identification: A Transfer Learning View
4. Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification
5. Predicting Early Indicators of Cognitive Decline from Verbal Utterances
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1. Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review;Journal of Alzheimer's Disease;2024-06-25
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