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.
State-of-the art network model SeqSleepNet @cite_17 processes multiple epochs and outputs the sleep labels all at once using end-to-end Hierarchical Recurrent Neural Networks. This uses all 3 channels namely, EEG, EMG and EOG in order to give the best overall accuracy of 87.1 Flexibility of this wearable also makes it more preferable than the bulky system used by @cite_18 . The smart-phone based nature of our sleep-staging application overcomes the need for a client-server architecture as used in Dreem headband @cite_0 . Our TensorFlow-Lite mobile application can also be adapted to other types of EEG devices for real-time settings.
@cite_0 @cite_18 @cite_17
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Summary Manual processing of sleep recordings is extremely time-consuming. Efforts to automate this process have shown promising results, but automatic systems are generally evaluated on private databases, not allowing accurate cross-validation with other systems. In lacking a common benchmark, the relative performances of different systems are not compared easily and advances are compromised. To address this fundamental methodological impediment to sleep study, we propose an open-access database of polysomnographic biosignals. To build this database, whole-night recordings from 200 participants [97 males (aged 42.9 ± 19.8 years) and 103 females (aged 38.3 ± 18.9 years); age range: 18–76 years] were pooled from eight different research protocols performed in three different hospital-based sleep laboratories. All recordings feature a sampling frequency of 256 Hz and an electroencephalography (EEG) montage of 4–20 channels plus standard electro-oculography (EOG), electromyography (EMG), electrocardiography (ECG) and respiratory signals. Access to the database can be obtained through the Montreal Archive of Sleep Studies (MASS) website (http: www.ceams-carsm.ca en MASS), and requires only affiliation with a research institution and prior approval by the applicant's local ethical review board. Providing the research community with access to this free and open sleep database is expected to facilitate the development and cross-validation of sleep analysis automation systems. It is also expected that such a shared resource will be a catalyst for cross-centre collaborations on difficult topics such as improving inter-rater agreement on sleep stage scoring. Summary An accurate home sleep study to assess electroencephalography (EEG)-based sleep stages and EEG power would be advantageous for both clinical and research purposes, such as for longitudinal studies measuring changes in sleep stages over time. The purpose of this study was to compare sleep scoring of a single-channel EEG recorded simultaneously on the forehead against attended polysomnography. Participants were recruited from both a clinical sleep centre and a longitudinal research study investigating cognitively normal ageing and Alzheimer's disease. Analysis for overall epoch-by-epoch agreement found strong and substantial agreement between the single-channel EEG compared to polysomnography (κ = 0.67). Slow wave activity in the frontal regions was also similar when comparing the single-channel EEG device to polysomnography. As expected, Stage N1 showed poor agreement (sensitivity 0.2) due to lack of occipital electrodes. Other sleep parameters, such as sleep latency and rapid eye movement (REM) onset latency, had decreased agreement. Participants with disrupted sleep consolidation, such as from obstructive sleep apnea, also had poor agreement. We suspect that disagreement in sleep parameters between the single-channel EEG and polysomnography is due partially to altered waveform morphology and or poorer signal quality in the single-channel derivation. Our results show that single-channel EEG provides comparable results to polysomnography in assessing REM, combined Stages N2 and N3 sleep and several other parameters, including frontal slow wave activity. The data establish that single-channel EEG can be a useful research tool. Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http: github.com pquochuy SeqSleepNet ). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen’s kappa of 87.1 , 83.3 , and 0.815 on a publicly available dataset with 200 subjects.