Large-Scale Automated Sleep Staging

Sun, Haoqi; Jia, Jian; Goparaju, Balaji; Huang, Guang-Bin; Sourina, Olga; Bianchi, Matt T.; Westover, M. Brandon

doi:10.1093/sleep/zsx139

Cited by 99 publications

(84 citation statements)

References 30 publications

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“…To assess the impact of providing information about sleep stages, we use a recently published algorithm that performs at a level similar to human experts in assigning sleep stages to consecutive 30 second epochs of EEG (Sun et al 2017). The model outputs a probability for stages NREM1, NREM2, NREM3, REM or awake.…”

Section: Methodsmentioning

confidence: 99%

Detecting abnormal electroencephalograms using deep convolutional networks

Leeuwen

Sun

Tabaeizadeh

et al. 2019

Clinical Neurophysiology

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Objectives: Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of contextual factors, including age and sleep stage. Our objectives were to validate prior work on an independent data set suggesting that deep learning methods can discriminate between normal vs abnormal EEGs, to understand whether age and sleep stage information can improve discrimination, and to understand what factors lead to errors. Methods: We train a deep convolutional neural network on a heterogeneous set of 8,522 routine EEGs from the Massachusetts General Hospital. We explore several strategies for optimizing model performance, including accounting for age and sleep stage. Results: The area under the receiver operating characteristic curve (AUC) on an independent test set (n = 851) is 0.917 marginally improved by including age (AUC=0.924), and both age and sleep stages (AUC= 0.925), though not statistically significant. Conclusions: The model architecture generalizes well to an independent dataset. Adding age and sleep stage to the model does not significantly improve performance. Significance: Insights learned from misclassified examples, and minimal improvement by adding sleep stage and age suggest fruitful directions for further research.

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Section: Methodsmentioning

confidence: 99%

Detecting abnormal electroencephalograms using deep convolutional networks

Leeuwen

Sun

Tabaeizadeh

et al. 2019

Clinical Neurophysiology

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“…To address the first issue of time-consuming sleep stage scoring process performed by clinicians, automatic sleep staging systems have been proposed based on full PSG recordings [19,20] -these include the electroencephalogram (EEG), electrooculogram (EOG), and chin electromyogram (EMG) -or more recently based on single channel EEG recordings [21]. The corresponding automatic sleep stage classification approaches employ various machine learning algorithms and have been validated on both publicly available datasets [22] as well as on proprietary data recorded as part of various research projects [21].…”

Section: Introductionmentioning

confidence: 99%

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Nakamura

Alqurashi

Morrell

et al. 2020

IEEE Trans. Biomed. Eng.

111

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Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear-EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1 % in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with PSG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The 'standardised' one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

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“…Automatic PSG analysis in sleep medicine has been explored and debated for some time, but has yet to be widely adopted in clinical practice. In recent years, dozens of algorithms have been published that achieve expert-level performance for automated analysis of PSG data (9)(10)(11)(12). Indeed, scientists and engineers have used artificial intelligence (AI) methods to develop automated sleep stage classifiers and EEG pattern detectors, thanks to open access sleep data sets such as the National Sleep Research Resource (https://sleepdata.org).…”

Section: Introductionmentioning

confidence: 99%

The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

Thorey²,

et al. 2019

Preprint

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Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorders. We introduced the Dreem headband (DH) as an affordable, comfortable, and user-friendly alternative to polysomnography (PSG). The purpose of this study was to assess the signal acquisition of the DH and the performance of its embedded automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 5 sleep experts. Thirty-one subjects completed an over-night sleep study at a sleep center while wearing both a PSG and the DH simultaneously. We assessed 1) the EEG signal quality 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 AASM guidelines vs. PSG sleep experts manual scoring. Results demonstrate a strong correlation between the EEG signals acquired by the DH and those from the PSG, and the signals acquired by the DH enable monitoring of alpha (r= 0.71 ± 0.13), beta (r= 0.71 ± 0.18), delta (r = 0.76 ± 0.14), and theta (r = 0.61 ± 0.12) 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 five sleep experts. These results demonstrate the capacity of the DH to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies. Sleep | EEG | Machine learning | Sleep stages | DeviceCorrespondence: research@dreem.com

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Large-Scale Automated Sleep Staging

Abstract: Training with a large data set enables automated sleep staging that compares favorably with human scorers. Because testing was performed on a large and heterogeneous data set, the performance estimate has low variance and is likely to generalize broadly.

Cited by 99 publications

References 30 publications

Detecting abnormal electroencephalograms using deep convolutional networks

Detecting abnormal electroencephalograms using deep convolutional networks

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

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