RobustSleepNet: Transfer Learning for Automated Sleep Staging at Scale

Guillot, Antoine; Thorey, Valentin

doi:10.1109/tnsre.2021.3098968

Cited by 78 publications

(52 citation statements)

References 24 publications

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“…13,19 By deciding the number of input epochs and corresponding output labels, strategies of model design can be divided into (i) one-to-one, [20][21][22][23] (ii) many-to-one, [24][25][26] and (iii) many-to-many. [27][28][29][30] Many recent papers are utilizing the many-to-many prediction strategy since it could achieve better performance by exploiting contextual input information and contextual output generation. We also recruited a many-to-many strategy for our proposed network.…”

Section: Discussionmentioning

confidence: 99%

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices

Hong¹,

Tran²,

Jung³

et al. 2022

NSS

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Purpose Nocturnal sounds contain numerous information and are easily obtainable by a non-contact manner. Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones. Patients and Methods Two different audio datasets were used: audio data routinely recorded by a solitary microphone chip during polysomnography (PSG dataset, N=1154) and audio data recorded by a smartphone (smartphone dataset, N=327). The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement from ambient noise. The proposed neural network model learns to first extract features from each 30-second epoch and then analyze inter-epoch relationships of extracted features to finally classify the epochs into sleep stages. Results Our model achieved 70% epoch-by-epoch agreement for 4-class (wake, light, deep, REM) sleep stage classification and robust performance across various signal-to-noise conditions. The model performance was not considerably affected by sleep apnea or periodic limb movement. External validation with smartphone dataset also showed 68% epoch-by-epoch agreement. Conclusion The proposed end-to-end deep learning model shows potential of low-quality sounds recorded from microphone chips to be utilized for sleep staging. Future study using nocturnal sounds recorded from mobile devices at home environment may further confirm the use of mobile device recording as an at-home sleep tracker.

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Section: Discussionmentioning

confidence: 99%

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices

Hong¹,

Tran²,

Jung³

et al. 2022

NSS

View full text Add to dashboard Cite

show abstract

“…There are several areas of transfer learning research which are underexplored for sleep staging. Much of supervised transfer learning is done using very simple methods such as re-training a few layers of the model (hereafter referred to as head re-training) or retraining the entire model at a smaller learning rate [13][14][15]. However, there are other more sophisticated transfer learning methods such as Correlation Alignment (CORAL) [16], Deep Domain Confusion (DDC) [17], and Subspace Alignment (SA) [18] which are rarely tested on sleep staging tasks.…”

Section: Limitations In Current Transfer Learning Researchmentioning

confidence: 99%

Comparison of deep transfer learning algorithms and transferability measures for wearable sleep staging

Waters

Clifford

2022

BioMed Eng OnLine

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Background Obtaining medical data using wearable sensors is a potential replacement for in-hospital monitoring, but the lack of data for such sensors poses a challenge for development. One solution is using in-hospital recordings to boost performance via transfer learning. While there are many possible transfer learning algorithms, few have been tested in the domain of EEG-based sleep staging. Furthermore, there are few ways for determining which transfer learning method will work best besides exhaustive testing. Measures of transferability do exist, but are typically used for selection of pre-trained models rather than algorithms and few have been tested on medical signals. We tested several supervised transfer learning algorithms on a sleep staging task using a single channel of EEG (AF7-Fpz) captured from an in-home commercial system. Results Two neural networks—one bespoke and another state-of-art open-source architecture—were pre-trained on one of six source datasets comprising 11,561 subjects undergoing clinical polysomnograms (PSGs), then re-trained on a target dataset of 75 full-night recordings from 24 subjects. Several transferability measures were then tested to determine which is most effective for assessing performance on unseen target data. Performance on the target dataset was improved using transfer learning, with re-training the head layers being the most effective in the majority of cases (up to 63.9% of cases). Transferability measures generally provided significant correlations with accuracy (up to $$r = -0.53$$ r = - 0.53 ). Conclusion Re-training the head layers provided the largest performance boost. Transferability measures are useful indicators of transfer learning effectiveness.

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“… 2017 ) 82.0 0.760 76.9 86.4 0.805 82.2 – – – FCNN+RNN (Phan et al. 2021 ) 83.5 0.775 77.7 86.4 0.806 82.1 88.1 0.832 80.9 TinySleepNet (Supratak and Guo 2020 ) 85.4 0.800 80.5 83.1 0.77 78.1 – – – RobustSleepNet (Guillot and Thorey 2021 ) – – 81.7 – – 82.5 – – 80.0 SleepTransformer (Phan et al. 2022 ) – – – – – – 87.7 0.828 80.1 SeqSleepNet (Phan et al.…”

Section: Applicationsmentioning

confidence: 99%

Deep learning on multi-view sequential data: a survey

et al. 2022

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With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions.

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RobustSleepNet: Transfer Learning for Automated Sleep Staging at Scale

Cited by 78 publications

References 24 publications

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices

Comparison of deep transfer learning algorithms and transferability measures for wearable sleep staging

Deep learning on multi-view sequential data: a survey

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