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Models/DeepSleepNet

DeepSleepNet

Reported on 16 benchmarks across 2 tasks · 2 papers · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical16 results

  • Sleep QualityonSleep-EDF
    Cohen's kappa· uses extra data· 2017-03-12
    0.76
    best: 0.82 (SleePyCo (Fpz-Cz only))
    SOTA
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGarXiv:1703.04046
  • Sleep QualityonSleep-EDF
    Macro-F1· uses extra data· 2017-03-12
    0.769
    best: 0.812 (SleePyCo (Fpz-Cz only))
    SOTA
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGarXiv:1703.04046
  • Sleep QualityonMASS SS3
    Cohen's kappa· 2017-03-12
    0.8
    best: 0.803 (CatBoost)
    SOTA
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGarXiv:1703.04046
  • Sleep QualityonMASS SS3
    Macro-F1· 2017-03-12
    0.817
    SOTA
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGarXiv:1703.04046
  • Sleep Stage DetectiononSleep-EDF
    Cohen's kappa· uses extra data· 2017-03-12
    0.76
    best: 0.82 (SleePyCo (Fpz-Cz only))
    SOTA
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGarXiv:1703.04046
  • Sleep Stage DetectiononSleep-EDF
    Macro-F1· uses extra data· 2017-03-12
    0.769
    best: 0.812 (SleePyCo (Fpz-Cz only))
    SOTA
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGarXiv:1703.04046
  • Sleep Stage DetectiononMASS SS3
    Cohen's kappa· 2017-03-12
    0.8
    best: 0.803 (CatBoost)
    SOTA
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGarXiv:1703.04046
  • Sleep Stage DetectiononMASS SS3
    Macro-F1· 2017-03-12
    0.817
    SOTA
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGarXiv:1703.04046
  • Sleep QualityonDODH
    Accuracy· 2019-10-31
    89.6
    best: 89.9 (SimpleSleepNet)
    Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingarXiv:1911.03221
  • Sleep QualityonDODH
    Kappa· 2019-10-31
    84.3
    best: 84.6 (SimpleSleepNet)
    Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingarXiv:1911.03221
  • Sleep QualityonDODO
    Accuracy· 2019-10-31
    87.5
    best: 88.7 (SimpleSleepNet)
    Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingarXiv:1911.03221
  • Sleep QualityonDODO
    Kappa· 2019-10-31
    80.4
    best: 82.3 (SimpleSleepNet)
    Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingarXiv:1911.03221
  • Sleep Stage DetectiononDODH
    Accuracy· 2019-10-31
    89.6
    best: 89.9 (SimpleSleepNet)
    Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingarXiv:1911.03221
  • Sleep Stage DetectiononDODH
    Kappa· 2019-10-31
    84.3
    best: 84.6 (SimpleSleepNet)
    Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingarXiv:1911.03221
  • Sleep Stage DetectiononDODO
    Accuracy· 2019-10-31
    87.5
    best: 88.7 (SimpleSleepNet)
    Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingarXiv:1911.03221
  • Sleep Stage DetectiononDODO
    Kappa· 2019-10-31
    80.4
    best: 82.3 (SimpleSleepNet)
    Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingarXiv:1911.03221