Table 1 Sleep classification techniques across different sleep-sensing modalities.
From: The future of sleep health: a data-driven revolution in sleep science and medicine
Technique | Technique variations | PSG/EEG | Wearable sensing |
|---|---|---|---|
Statistical | Latent dirichlet allocation | ||
Support vector machines | |||
Hidden Markov model | |||
Quadratic | |||
Bayesian | |||
Logistic regression | |||
Instance base | K-nearest neighbours | ||
Decision tree | Decision tree | ||
Ensemble model | Adaboost | ||
Bagging | |||
Random forest | |||
XGBoost | |||
Clustering | K-means classifier | ||
Spectral clustering GMM | |||
ANN and DNN | Convolutional NN | ||
Recurrent NN (LSTMs, GRUs) | |||
Others/heuristic | Fuzzy classifier | ||
Wavelet methods | |||
Sadeh | |||
Sazonov | |||
Oakley | |||
Cole-Kripke | |||
Webster | |||
ADAS | |||
Scripps clinic |
