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tinysleepnet

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software/tinysleepnet

TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG by Akara Supratak and Yike Guo from The Faculty of ICT, Mahidol University and Imperial College…

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
Apache-2.0(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
biosignals · cnn · deep-learning · eeg · neural-networks · rnn · sleep-analysis · sleep-stage-scoring
Regulatory
unknown
built by · 1

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • AttnSleepeeg · sleep-stage-scoring

    [TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"

  • NeuroNeteeg · sleep-stage-scoring

    [Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG

  • deepsleepnetbiosignals · cnn · eeg

    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

  • Official source code of Arrhythmia Detection

  • In this project, we wish to identify psychiatric disorders through patient's speech

  • elata-bio-sdkbiosignals · eeg

    Elata SDK is the cross-platform biosignal toolkit for building neurotechnology and remote biosensing apps on web and native.

sources
  1. api.github.com/repos/akaraspt/tinysleepnet
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2023-08-23, 177 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/49.json→ .entries["tinysleepnet"]

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