1210 Development Of An Auditory Neurofeedback During Sleep Onset Process. (27th May 2020)

• Record Type: Journal Article
• Authors: Douch, M; Soubrier, M; Pinaud, C; Harris, M; Thorey, V
• Is Part Of: Sleep. Volume 43(2020)Supplement 1
• Page Start: A462
• Page End: A463
• Publication Date: 2020-05-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsaa056.1204
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Biofeedback is proposed as an alternative method to help patients with insomnia reducing their anxiety. Some studies have shown that auditory neurofeedback can be effective at reducing sleep-onset latency. However, the AASM sleep stage classification only describes the sleep-onset as a binary state (i.e. wake or N1) which makes it not adapted for neurofeedback. We introduced a simple 4-stages classification for sleep-onset, on 10 seconds EEG epoch. The aim of this study was to develop an automatic method to detect these stages, and an online algorithm embedded in the Dreem headband (DH) that adapted the auditory feedback based on the current stage. Methods: Fourteen subjects underwent an overnight PSG monitoring, from which the first sleep-onset period was extracted. We defined the simple 4-stages classification for sleep-onset on 10 seconds EEG epoch as following: SO1) > 75% of the epoch covered by alpha frequencies SO2) between 25% and 50% of the window covered with alpha frequencies, SO3) Alpha frequencies covered less than 25% and theta frequencies covered less than 30% of the epoch, and SO4) Theta frequency covered more than 30% of the epoch. For the manual scoring, 4 sleep scorers have been given the instructions and a Q&A session after scoring the first two records. For the algorithm, a sound triggering algorithm was linked to a neural network trained on the scored data, to dynamically adapt the sound to the sleep-onset stage. Results: The scorers reached an average agreement of 68 + 15% over all the records. The neural network reached an accuracy of 68%. Per state the accuracy was: 71 ± 32% (S1), 52 ± 22% (S2), 54 ± 23% (S3), 79 ± 21% (S4). The automatic neurofeedback was able to adapt sound stimulations in real-time based on stages and was well perceived among first testers. Conclusion: The results of this preliminary work show that we can reach a higher agreement by reducing the epoch duration and use this classification to produce automatic biofeedback during the sleep onset period. Further studies using a data-driven method should be conducted. Support: This study supported by Dreem sas.