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Explore Intrinsic Geometry of Sleep Dynamics and Predict Sleep Stage by Unsupervised Learning Techniques
Gi Ren Liu, Yu Lun Lo,
Yuan Chung Sheu
, Hau Tieng Wu
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引文 斯高帕斯（Scopus）
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Keyphrases
Sleep Stages
100%
Unsupervised Learning Techniques
100%
Sleep Dynamics
100%
Intrinsic Geometry
100%
Diffusion Algorithm
50%
Automatic Annotation
50%
Alternating Diffusion
50%
Diffusion Maps
50%
Clinical Application
25%
Benchmark Dataset
25%
Learning Algorithm
25%
Harmonic Analysis
25%
Unsupervised Method
25%
Hidden Markov Model
25%
Supervised Learning
25%
Geometric Structure
25%
Time-frequency Analysis
25%
Electroencephalogram
25%
Performance Prediction
25%
Publicly Available
25%
Overall Accuracy
25%
Spectral Information
25%
Nonlinear Geometry
25%
Leave-one-subject-out
25%
Reduction Property
25%
PhysioNet
25%
Nonlinear Dimensionality Reduction
25%
Dynamic Exploration
25%
Synchrosqueezing Transform
25%
Engineering
Clinical Application
100%
Spectral Information
100%
Learning Technique
100%
Prediction Performance
100%
Harmonics
100%
Time Frequency Analysis
100%
Computer Science
Unsupervised Learning
100%
Learning Technique
100%
Automatic Annotation
100%
Supervised Learning
50%
Clinical Application
50%
Prediction Performance
50%
Spectral Information
50%
Reduction Property
50%
Time Frequency Analysis
50%