The Dreem Headband compared to polysomnography for e ectroencephalographic signal acquisition and sleep staging

被引:210
作者
Arnal, Pierrick J. [1 ]
Thorey, Valentin [2 ]
Debellemaniere, Eden [1 ]
Ballard, Michael E. [1 ]
Hernandez, Albert Bou [2 ]
Guillot, Antoine [2 ]
Jourde, Hugo [2 ]
Harris, Mason [2 ]
Guillard, Mathias [3 ,4 ]
Van Beers, Pascal [3 ,4 ]
Chennaoui, Mounir [3 ,4 ]
Sauvet, Fabien [3 ,4 ]
机构
[1] Sci Team, Dreem, 450 Pk Ave S, New York, NY 10016 USA
[2] Algorithm Team, Dreem, Paris, France
[3] French Armed Forces Biomed Res Inst IRBA, Fatigue & Vigilance Unit, Bretigny Sur Orge, France
[4] Paris Descartes Univ, EA 7330 VIFASOM, Paris, France
关键词
sleep; EEG; machine learning; sleep stages; device; AMERICAN ACADEMY; RELIABILITY; VALIDATION; VARIABILITY; WIRELESS;
D O I
10.1093/sleep/zsaa097
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Study Objectives: The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical for advancing sleep science. The aim of this study was to assess the signal acquisition and the performance of the automatic sleep staging algorithms of a reduced-montage dry-electroencephalographic (EEG) device (Dreem headband, DH) compared to the gold-standard polysomnography (PSG) scored by five sleep experts. Methods: A total of 25 subjects who completed an overnight sleep study at a sleep center while wearing both a PSG and the DH simultaneously have been included in the analysis. We assessed (1) similarity of measured EEG brain waves between the DH and the PSG; (2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG; and (3) the performance of the DH's automatic sleep staging according to American Academy of Sleep Medicine guidelines versus PSG sleep experts manual scoring. Results: The mean percentage error between the EEG signals acquired by the DH and those from the PSG for the monitoring of a was 15 +/- 3.5%, 16 +/- 4.3% for 13, 16 +/- 6.1% for lambda, and 10 +/- 1.4% for theta frequencies during sleep. The mean absolute error for heart rate, breathing frequency, and RRV was 1.2 +/- 0.5 bpm, 0.3 +/- 0.2 cpm, and 3.2 +/- 0.6%, respectively. Automatic sleep staging reached an overall accuracy of 83.5 +/- 6.4% (F1 score: 83.8 +/- 6.3) for the DH to be compared with an average of 86.4 +/- 8.0% (F1 score: 86.3 +/- 7.4) for the 5 sleep experts. Conclusions: These results demonstrate the capacity of the DH to both monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for, large-scale, longitudinal sleep studies.
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页数:13
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共 37 条
[1]  
[Anonymous], 2018, SLEEP
[2]   Validation of the Insomnia Severity Index as an outcome measure for insomnia research [J].
Bastien, Celyne H. ;
Vallieres, Annie ;
Morin, Charles M. .
SLEEP MEDICINE, 2001, 2 (04) :297-307
[3]  
Biswal S, SLEEPNET UNPUB
[4]   The variability of the apnoea-hypopnoea index [J].
Bittencourt, LRA ;
Suchecki, D ;
Tufik, S ;
Peres, C ;
Togeiro, SM ;
Bagnato, MDC ;
Nery, LE .
JOURNAL OF SLEEP RESEARCH, 2001, 10 (03) :245-251
[5]   A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series [J].
Chambon, Stanislas ;
Galtier, Mathieu N. ;
Arnal, Pierrick J. ;
Wainrib, Gilles ;
Gramfort, Alexandre .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2018, 26 (04) :758-769
[6]   DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal [J].
Charnbon, S. ;
Thorey, V. ;
Arnal, P. J. ;
Mignot, E. ;
Gramfort, A. .
JOURNAL OF NEUROSCIENCE METHODS, 2019, 321 :64-78
[7]   Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard [J].
Danker-Hopfe, Heidi ;
Anderer, Peter ;
Zeitlhofer, Josef ;
Boeck, Marion ;
Dorn, Hans ;
Gruber, Georg ;
Heller, Esther ;
Loretz, Erna ;
Moser, Doris ;
Parapatics, Silvia ;
Saletu, Bernd ;
Schmidt, Andrea ;
Dorffner, Georg .
JOURNAL OF SLEEP RESEARCH, 2009, 18 (01) :74-84
[8]   Performance of an Ambulatory Dry-EEG Device for Auditory Closed- Loop Stimulation of Sleep Slow Oscillations in the Home Environment [J].
Debellemaniere, Eden ;
Chambon, Stanislas ;
Pinaud, Clemence ;
Thorey, Valentin ;
Dehaene, David ;
Leger, Damien ;
Chennaoui, Mounir ;
Arnal, Pierrick J. ;
Galtier, Mathieu N. .
FRONTIERS IN HUMAN NEUROSCIENCE, 2018, 12
[9]   Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions [J].
Depner, Christopher M. ;
Cheng, Philip C. ;
Devine, Jaime K. ;
Khosla, Seema ;
de Zambotti, Massimiliano ;
Robillard, Rebecca ;
Vakulin, Andrew ;
Drummond, Sean P. A. .
SLEEP, 2020, 43 (02)
[10]   Validation of a Wireless, Self-Application, Ambulatory Electroencephalographic Sleep Monitoring Device in Healthy Volunteers [J].
Finan, Patrick H. ;
Richards, Jessica M. ;
Gamaldo, Charlene E. ;
Han, Dingfen ;
Leoutsakos, Jeannie Marie ;
Salas, Rachel ;
Irwin, Michael R. ;
Smith, Michael T. .
JOURNAL OF CLINICAL SLEEP MEDICINE, 2016, 12 (11) :1443-1451