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An accurate sleep staging system with novel feature generation and auto-mapping

https://doi.org/10.1109/icot.2017.8336079
0 signed · 16 inferred · view as graph

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11325-012-0757-4openalex-snapshot
Assessment of a wireless headband for automatic sleep scoring
inferredcites →https://doi.org/10.1109/embc.2014.6944071openalex-snapshot
Automatic sleep onset detection using single EEG sensor
inferredcites →https://doi.org/10.1109/embc.2015.7320261openalex-snapshot
Reduction in time-to-sleep through EEG based brain state detection and audio stimulation
inferredcites →https://doi.org/10.1161/01.cir.101.23.e215openalex-snapshot
PhysioBank, PhysioToolkit, and PhysioNet

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4064793openalex-extracted
Automatic Sleep Stage Classification for the Osa Patients with Feature Mining
inferred← citeshttps://doi.org/10.1007/s11571-023-10062-0openalex-snapshot
EEGNet classification of sleep EEG for individual specialization based on data augmentation
inferred← citeshttps://doi.org/10.1016/j.sleep.2022.09.004openalex-snapshot
Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape
inferred← citeshttps://doi.org/10.1088/1741-2552/ab255dopenalex-snapshot
An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue
inferred← citeshttps://doi.org/10.1109/access.2024.3514890openalex-snapshot
Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments
inferred← citeshttps://doi.org/10.1109/icist59754.2023.10367106openalex-snapshot
Classification of Sleep EEG Signals for Individual Specialization by 1D Deep Convolutional Network and Data Augmentation
inferred← citeshttps://doi.org/10.32657/10356/107596openalex-snapshot
Detecting passive fatigue with brain-computer interface in drowsy driving and stroke rehabilitation
inferred← citeshttps://doi.org/10.3390/s21103316openalex-snapshot
A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data
inferred← citeshttps://doi.org/10.3390/s26020474openalex-snapshot
EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection
inferred← citeshttps://doi.org/10.36306/konjes.1073932openalex-snapshot
DETERMINING THE MOST POWERFUL FEATURES IN THE DESIGN OF AN AUTOMATIC SLEEP STAGING SYSTEM
inferred← citeshttps://doi.org/10.4028/p-svwo5kopenalex-snapshot
Automatic Sleep Stage Classification for the Obstructive Sleep Apnea Patients with Feature Mining
inferred← citeshttps://doi.org/10.7717/peerj-cs.1988openalex-snapshot
Sleep stage prediction using multimodal body network and circadian rhythm
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