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

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          Abstract

          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.

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          DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

          Akara Supratak, Hao Dong, Chao Wu … (2017)
          This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.
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            Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG.

            B. Kemp, A.H. Zwinderman, B Tuk … (2000)
            Increasing depth of sleep corresponds to an increasing gain in the neuronal feedback loops that generate the low-frequency (slow-wave) electroencephalogram (EEG). We derived the maximum-likelihood estimator of the feedback gain and applied it to quantify sleep depth. The estimator computes the fraction (0%-100%) of the current slow wave which continues in the near-future (0.02 s later) EEG. Therefore, this percentage was dubbed slow-wave microcontinuity (SW%). It is not affected by anatomical parameters such as skull thickness, which can considerably bias the commonly used slow-wave power (SWP). In our study, both of the estimators SW% and SWP were monitored throughout two nights in 22 subjects. Each subject took temazepam (a benzodiazepine) on one of the two nights. Both estimators detected the effects of age, temazepam, and time of night on sleep. Females were found to have twice the SWP of males, but no gender effect on SW% was found. This confirms earlier reports that gender affects SWP but not sleep depth. Subjectively assessed differences in sleep quality between the nights were correlated to differences in SW%, not in SWP. These results demonstrate that slow-wave microcontinuity, being based on a physiological model of sleep, reflects sleep depth more closely than SWP does.
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              Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard.

              Heidi Danker-Hopfe, Peter Anderer, Josef Zeitlhofer … (2009)
              Interrater variability of sleep stage scorings has an essential impact not only on the reading of polysomnographic sleep studies (PSGs) for clinical trials but also on the evaluation of patients' sleep. With the introduction of a new standard for sleep stage scorings (AASM standard) there is a need for studies on interrater reliability (IRR). The SIESTA database resulting from an EU-funded project provides a large number of studies (n = 72; 56 healthy controls and 16 subjects with different sleep disorders, mean age +/- SD: 57.7 +/- 18.7, 34 females) for which scorings according to both standards (AASM and R&K) were done. Differences in IRR were analysed at two levels: (1) based on quantitative sleep parameter by means of intraclass correlations; and (2) based on an epoch-by-epoch comparison by means of Cohen's kappa and Fleiss' kappa. The overall agreement was for the AASM standard 82.0% (Cohen's kappa = 0.76) and for the R&K standard 80.6% (Cohen's kappa = 0.68). Agreements increased from R&K to AASM for all sleep stages, except N2. The results of this study underline that the modification of the scoring rules improve IRR as a result of the integration of occipital, central and frontal leads on the one hand, but decline IRR on the other hand specifically for N2, due to the new rule that cortical arousals with or without concurrent increase in submental electromyogram are critical events for the end of N2.
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                Author and article information

                Journal
                Publication date Created: 25 November 2018
                Article
                ArXiV ID: 1811.10111
                SO-VID: 994e5df5-b329-4b8a-a59a-45516874aa90
                License:

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                ACM classes 68T05, 68T10
                Report No ML4H/2018/114
                Comments Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200
                Categories cs.HC cs.LG eess.SP q-bio.NC

                ScienceOpen disciplines: Neurosciences,Artificial intelligence,Electrical engineering,Human-computer-interaction
                Data availability:
                ScienceOpen disciplines: Neurosciences, Artificial intelligence, Electrical engineering, Human-computer-interaction

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