Sleep Stage Estimation by Non-invasive Bio-measurement

Author(s):  
Erina Komatsu ◽  
Yousuke Kurihara ◽  
Kajiro Watanabe
2010 ◽  
Vol E93-B (4) ◽  
pp. 811-818 ◽  
Author(s):  
Keiki TAKADAMA ◽  
Kazuyuki HIROSE ◽  
Hiroyasu MATSUSHIMA ◽  
Kiyohiko HATTORI ◽  
Nobuo NAKAJIMA

2021 ◽  
Author(s):  
Nikhil Vyas ◽  
Kelly Ryoo ◽  
Hosanna Tesfaye ◽  
Ruhan Yi ◽  
Marjorie Skubic

2004 ◽  
Vol 51 (10) ◽  
pp. 1735-1748 ◽  
Author(s):  
T. Watanabe ◽  
K. Watanabe

Author(s):  
Hirotaka Matsumoto ◽  
Shima OKADA ◽  
Tianyi WANG ◽  
Makikawa MASAAKI
Keyword(s):  

2018 ◽  
Vol 11 (1) ◽  
pp. 32-39
Author(s):  
Yusuke TAJIMA ◽  
Fumito UWANO ◽  
Akinori MURATA ◽  
Tomohiro HARADA ◽  
Keiki TAKADAMA
Keyword(s):  

2019 ◽  
Vol 9 (2) ◽  
pp. 257-265 ◽  
Author(s):  
Teruaki Nochino ◽  
Yuko Ohno ◽  
Takafumi Kato ◽  
Masako Taniike ◽  
Shima Okada

SLEEP ◽  
2017 ◽  
Vol 40 (suppl_1) ◽  
pp. A287-A287
Author(s):  
Y Oka ◽  
K Takadama ◽  
T Harada ◽  
T Kashima ◽  
M Morishima
Keyword(s):  

Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 927 ◽  
Author(s):  
Anna Gergely ◽  
Orsolya Kiss ◽  
Vivien Reicher ◽  
Ivaylo Iotchev ◽  
Enikő Kovács ◽  
...  

Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g., emotion processing) and cognitive (e.g., memory consolidation) domains, methodologically relevant questions about the reliability of sleep stage scoring still need to be addressed. In Study 1, we analyzed the effects of different coders and different numbers of visible EEG channels on the visual scoring of the same polysomnography recordings. The lowest agreement was found between independent coders with different scoring experience using full (3 h-long) recordings of the whole dataset, and the highest agreement within-coder, using only a fraction of the original dataset (randomly selected 100 epochs (i.e., 100 × 20 s long segments)). The identification of drowsiness was found to be the least reliable, while that of non-REM (rapid eye movement, NREM) was the most reliable. Disagreements resulted in no or only moderate differences in macrostructural and spectral variables. Study 2 targeted the task of automated sleep EEG time series classification. Supervised machine learning (ML) models were used to help the manual annotation process by reliably predicting if the dog was sleeping or awake. Logistic regression models (LogREG), gradient boosted trees (GBT) and convolutional neural networks (CNN) were set up and trained for sleep state prediction from already collected and manually annotated EEG data. The evaluation of the individual models suggests that their combination results in the best performance: ~0.9 AUC test scores.


SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A101-A102
Author(s):  
Selda Yildiz ◽  
Miranda Lim ◽  
Manoj Sammi ◽  
Katherine Powers ◽  
Charles Murchison ◽  
...  

Abstract Introduction Cellular mechanisms underlying changes in small animal brain lactate concentrations have been investigated for more than 70 years and report sharp reductions in lactate (12-35%) during sleep or anesthesia relative to wakefulness. The goal of this study was to investigate alterations in human cerebral lactate concentrations across sleep-wake cycles. Toward this goal, we developed a novel non-invasive methodology, quantified changes in human cerebral lactate during sleep stages, and investigated potential mechanisms associated with changes in lactate. Methods Nine subjects (four females, five males; 21-27 y-o, mean age 24.2 ±2) were sleep deprived overnight, and underwent (5:45~11:00 am) experiments combining simultaneous MR-spectroscopy (MRS) and polysomnography (PSG) in a 3 T MR instrument using a 64-channel head/neck coil. A single voxel MRS (1H-MRS) acquired signals from a volume of interest (12~24 cm3) for every 7.5-s for 88~180-min. Lactate signal intensity was determined from each 7.5-s spectrum, normalized to corresponding water signal, and averaged over 30-s for each PSG epochs. Artifact corrected PSG data were scored for each 30-s epoch using the standard criteria and classified into one of four stages: W, N1, N2 and N3. Group mean lactate levels were quantified using LCModel. Three subjects returned for lactate diffusivity measurements using diffusion-sensitized PRESS MRS sequence. Results Compared to W, group mean lactate levels within each sleep stage showed a reduction of [4.9 ± 4.9] % in N1, [10.4 ± 5.2] % in N2, and [24.0 ± 5.8] % in N3. We observed a significant decrease in lactate apparent diffusion coefficient (ADC) accompanied by reduced brain lactate in sleep compared to wake (P<0.002). There were no differences in ADC values between wake and sleep for H2O, NAA, tCr, or Cho. Conclusion This is the first in-vivo report of alterations in human brain lactate concentrations across sleep-wake cycles. Observed decline in lactate levels during sleep compared to wakefulness is consistent with, and extends results from invasive small animal brain studies first reported more than 70 years ago, and support the notion of altered lactate metabolism and/or increased glymphatic activity in sleeping human brain. Support (if any) The Paul. G. Allen Family Foundation funded the study.


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