Efficient sleep classification based on entropy features and a support vector machine classifier-Reference-Cited by-同舟云学术

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Efficient sleep classification based on entropy features and a support vector machine classifier

Published:2018-11-26 Issue:11 Volume:39 Page:115005
ISSN:1361-6579
Container-title:Physiological Measurement
language:
Short-container-title:Physiol. Meas.

Author:

Zhang Zhimin^ORCID,Wei Shoushui,Zhu Guohun^ORCID,Liu Feifei,Li Yuwen,Dong Xiaotong,Liu Chengyu^ORCID,Liu Feng^ORCID

Funder

Guangxi Cloud Computing and Big Data Collaborative Innovation Center

UQ 2016 Philanthropic Grant for Early Career Engineering Researchers

Shandong Province Key Research and Development Plan

China Postdoctoral Science Foundation

China Scholarship Council

Publisher

IOP Publishing

Subject

Physiology (medical),Biomedical Engineering,Physiology,Biophysics

Link

http://iopscience.iop.org/article/10.1088/1361-6579/aae943/pdf

Reference43 articles.

1. Efficient sleep stage classification based on EEG signals

2. Sleep–wake stages classification and sleep efficiency estimation using single-lead electrocardiogram

3. Fuzzy entropy: a brief survey

4. Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis

5. Sleep devices: wearables and nearables, informational and interventional, consumer and clinical

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3. Sleep Stage Classification based on BCG using Improved Deep Convolutional Generative Adversarial Networks;2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC);2023-07-21

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