Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG

We present the first real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG. We employ real-time adaptation of a single channel Electroencephalography (EEG) to infer from a Time-Distributed 1-D Deep Convolutional Neural Network. Polysomnography (PSG)-the gold standard for sleep staging, requires a human scorer and is both complex and resource-intensive. Our work demonstrates an end-to-end on-smartphone pipeline that can infer sleep stages in just single 30-second epochs, with an overall accuracy of 83.5% on 20-fold cross validation for five-class classification of sleep stages using the open Sleep-EDF dataset.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Aeilko H. Zwinderman,et al.  Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG , 2000, IEEE Transactions on Biomedical Engineering.

[3]  P. Anderer,et al.  Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard , 2009, Journal of sleep research.

[4]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[5]  D. Pressnitzer,et al.  Formation and suppression of acoustic memories during human sleep , 2017, Nature Communications.

[6]  Judith Amores,et al.  BioEssence: A Wearable Olfactory Display that Monitors Cardio-respiratory Information to Support Mental Wellbeing , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Judith Amores Fernandez.  Essence : olfactory interfaces for unconscious influence of mood and cognitive performance , 2016 .

[8]  Ju Lynn Ong,et al.  An end-to-end framework for real-time automatic sleep stage classification , 2018, Sleep.

[9]  Tommi S. Jaakkola,et al.  Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture , 2017, ICML.

[10]  Oliver Y. Chén,et al.  SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  J. Born,et al.  Odor Cues During Slow-Wave Sleep Prompt Declarative Memory Consolidation , 2007, Science.

[12]  K. Junghanns,et al.  The Difficulty of Staying Awake During Alpha/Theta Neurofeedback Training , 2015, Applied psychophysiology and biofeedback.

[13]  David M Holtzman,et al.  Comparison of a single‐channel EEG sleep study to polysomnography , 2016, Journal of sleep research.

[14]  Miad Faezipour,et al.  Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation , 2016, Entropy.

[15]  C. O’Reilly,et al.  Montreal Archive of Sleep Studies: an open‐access resource for instrument benchmarking and exploratory research , 2014, Journal of sleep research.

[16]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

[17]  R. Foster,et al.  Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease , 2010, Nature Reviews Neuroscience.

[18]  Chad C. Williams,et al.  Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research , 2017, Front. Neurosci..

[19]  M. Walker,et al.  Sleep loss causes social withdrawal and loneliness , 2018, Nature Communications.

[20]  Yaniv Zigel,et al.  Sleep staging using nocturnal sound analysis , 2018, Scientific Reports.

[21]  J. Born,et al.  Overnight memory consolidation facilitates rather than interferes with new learning of similar materials—a study probing NMDA receptors , 2017, bioRxiv.

[22]  Esther Rodriguez-Villegas,et al.  Wearable Electroencephalography , 2010, IEEE Engineering in Medicine and Biology Magazine.

[23]  Chao Wu,et al.  DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Akane Sano,et al.  0182 INFLUENCE OF WEEKLY SLEEP REGULARITY ON SELF-REPORTED WELLBEING , 2017 .

[25]  A. Muzet,et al.  Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients. , 1996, Sleep.

[26]  R. Spencer,et al.  Sleep-dependent enhancement of emotional memory in early childhood , 2018, Scientific Reports.

[27]  Laurent Vercueil,et al.  A convolutional neural network for sleep stage scoring from raw single-channel EEG , 2018, Biomed. Signal Process. Control..

[28]  Yike Guo,et al.  Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders , 2015, Annals of Biomedical Engineering.

[29]  Qian Zhang,et al.  RASS: A Portable Real-time Automatic Sleep Scoring System , 2012, 2012 IEEE 33rd Real-Time Systems Symposium.