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pct_epoch_0_5to0_75pct
under
Sleep Monitoring/Polysomnography/Electroencephalogram
in
SHHS variables
Distribution of 30-s epochs, as a percent of total recording time in different Odds Ratio Product (ORP) deciles. Younes et al. 2022 (PubMed ID: 35272350) ORP is a continuous index of sleep depth and wake propensity. A value of 0 indicates very deep sleep; a value of 2.5 indicates full wakefulness. These values are used to construct the ORP histogram and ORP phenotype (:orp_type:). Search for all EEG variables within this dataset
pct_epoch_0to_0_25pct
under
Sleep Monitoring/Polysomnography/Electroencephalogram
in
SHHS variables
Distribution of 30-s epochs, as a percent of total recording time in different Odds Ratio Product (ORP) deciles. Younes et al. 2022 (PubMed ID: 35272350) ORP is a continuous index of sleep depth and wake propensity. A value of 0 indicates very deep sleep; a value of 2.5 indicates full wakefulness. These values are used to construct the ORP histogram and ORP phenotype (:orp_type:). Search for all EEG variables within this dataset
pct_epoch_1_5to1_75pct
under
Sleep Monitoring/Polysomnography/Electroencephalogram
in
SHHS variables
Distribution of 30-s epochs, as a percent of total recording time in different Odds Ratio Product (ORP) deciles. Younes et al. 2022 (PubMed ID: 35272350) ORP is a continuous index of sleep depth and wake propensity. A value of 0 indicates very deep sleep; a value of 2.5 indicates full wakefulness. These values are used to construct the ORP histogram and ORP phenotype (:orp_type:). Search for all EEG variables within this dataset
pct_epoch_0_25to0_5pct
under
Sleep Monitoring/Polysomnography/Electroencephalogram
in
SHHS variables
Distribution of 30-s epochs, as a percent of total recording time in different Odds Ratio Product (ORP) deciles. Younes et al. 2022 (PubMed ID: 35272350) ORP is a continuous index of sleep depth and wake propensity. A value of 0 indicates very deep sleep; a value of 2.5 indicates full wakefulness. These values are used to construct the ORP histogram and ORP phenotype (:orp_type:). Search for all EEG variables within this dataset
pct_epoch_1_25to1_5pct
under
Sleep Monitoring/Polysomnography/Electroencephalogram
in
SHHS variables
Distribution of 30-s epochs, as a percent of total recording time in different Odds Ratio Product (ORP) deciles. Younes et al. 2022 (PubMed ID: 35272350) ORP is a continuous index of sleep depth and wake propensity. A value of 0 indicates very deep sleep; a value of 2.5 indicates full wakefulness. These values are used to construct the ORP histogram and ORP phenotype (:orp_type:). Search for all EEG variables within this dataset
pct_epoch_2_25to2_5pct
under
Sleep Monitoring/Polysomnography/Electroencephalogram
in
SHHS variables
Distribution of 30-s epochs, as a percent of total recording time in different Odds Ratio Product (ORP) deciles. Younes et al. 2022 (PubMed ID: 35272350) ORP is a continuous index of sleep depth and wake propensity. A value of 0 indicates very deep sleep; a value of 2.5 indicates full wakefulness. These values are used to construct the ORP histogram and ORP phenotype (:orp_type:). Search for all EEG variables within this dataset
Posted by
szhivotovsky
on
October 25, 2023
in
Guest Blogger
...
efforts aimed at standardizing sleep scoring procedures, with the
...
EEG derivations, to detailed sleep scoring rules according to age. Automated sleep scoring systems - e.g. highly performing deep learning (DL) algorithms -
...
show that a DL-based sleep scoring algorithm may not
...
icc_rl_orp
under
Sleep Monitoring/Polysomnography/Electroencephalogram
in
SHHS variables
Intraclass correlation between right and left Odds Ratio Product (ORP) across all epochs in Total Recording Time (TRT). A high correlation indicates good agreement in sleep depth between right and left hemispheres. The reasons for a low correlation could be: a) reduced range of ORP across TRT (e.g., little stage wake or deep sleep) b) systematic difference between the two electrodes (e.g. noise in one electrode or unilateral brain pathology) c) true dissociation between right and left sleep (Dolphin sleep). :icc_ms_rows:, :icc_ms_col: and :icc_ms_error: distinguish between these three possibilities, respectively. Younes et al. 2021 (PubMed ID: 34156473)Dres et al. 2019 (PubMed ID: 30818966)Azarbarzin et al. 2021 (PubMed ID: 32441843)Search for all EEG variables within this dataset
Posted by
szhivotovsky
on
July 17, 2023
in
Guest Blogger
...
a realistic assessment of sleep stage classification (SSC) models
...
Most of the proposed deep learning-based models assume access
...
data. We examined existing sleep stage classification models, evaluated
...
ms204a
under
Sleep Monitoring/Polysomnography/Evening and Morning Survey/SHHS2
in
SHHS variables
Rate the actual quality of your sleep last night (Do not compare to usual sleep quality). My sleep last night was (circle a number for each): a. [5 point Likert scale from "Light" to "Dark"]
hprns2
under
Medical History/Medications/SHHS2
in
SHHS variables
Participant taking HEPARINS within two weeks of the Sleep Heart Health Study Visit Two (SHHS2) visit. All medications were recorded during the interview, and medication information was later categorized by physician review.
warf2
under
Medical History/Medications/SHHS2
in
SHHS variables
Participant taking ORAL ANTICOAGULANTS INCLUDING WARFARIN, COUMADIN, AND ANISINDIONE within two weeks of the Sleep Heart Health Study Visit Two (SHHS2) visit. All medications were recorded during the interview, and medication information was later categorized by physician review.
warf1
under
Medical History/Medications/SHHS1
in
SHHS variables
Participant taking ORAL ANTICOAGULANTS INCLUDING WARFARIN, COUMADIN, AND ANISINDIONE within two weeks of the Sleep Heart Health Study Visit One (SHHS1) visit. All medications were recorded during the interview, and medication information was later categorized by physician review.