A convolutional neural network for sleep stage scoring from raw single-channel EEG

Sors, Arnaud; Bonnet, Stéphane; Mirek, Sébastien; Vercueil, Laurent; Payen, Jean‐François

doi:10.1016/j.bspc.2017.12.001

Cited by 357 publications

(294 citation statements)

References 20 publications

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“…Nu ber of e)a ples 10 −2 10 −1 10 0 10 1 10 2 10 3 10 4 Ratio (e)a ples/ in) datasets with a much greater number of subjects: [132,160,188,149] all used datasets with at least 250 subjects, while [22] and [49] used datasets with 10,000 and 16,000 subjects, respectively. As explained in Section 3.7.4, the untapped potential of DL-EEG might reside in combining data coming from many different subjects and/or datasets to train a model that captures common underlying features and generalizes better.…”

Section: Subjectsmentioning

confidence: 99%

“…In [183], EEG segments from the interictal class were split into smaller subgroups of equal size to the preictal class. In [160], cost-sensitive learning and oversampling were used to solve the class imbalance problem for sleep staging but the overall performance using these approaches did not improve. In [144], the authors randomly replicated subjects from the minority class to balance classes.…”

Section: Data Augmentationmentioning

confidence: 99%

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Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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Section: Subjectsmentioning

confidence: 99%

Section: Data Augmentationmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…The Sleep-EDF database, though widely employed in quantitative experiments, lacks a standard test bench for fair comparison between different ASSC frameworks. Evaluation metrics are susceptible to changes in class distributions, number of epochs used in the evaluation task and also the epoch selection scheme [20]. We can see a 8.8% reduction in the accuracy of [11] in the 6-stage SC-task compared to the paper reported metric, which can be due to an increased number of data epochs and patient independent splitting during evaluation.…”

Section: A Number Of Epochs and Splitmentioning

confidence: 89%

“…A number of sleep health studies have been compiled into datasets that are used in quantitative experiments, i.e. the Sleep Heart Health Study (SHHS) [20], Montreal Archive of Sleep Studies (MASS) [10] and the Physionet Sleep-EDF Database [21]. We use the Physionet Sleep-EDF Expanded Database in our experiments for its data volume and commonality in existing literature [9].…”

Section: Datasetmentioning

confidence: 99%

End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

Humayun

Sushmit

Hasan

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed. The network takes raw single channel electroencephalogram (Fpz-Cz) signal as input and yields hypnogram annotations for each 30s segments as output. Experiments are carried out for two different scoring standards (5 and 6 stage classification) on the expanded PhysioNet Sleep-EDF dataset, which contains multi-source data from hospital and household polysomnography setups. The performance of the proposed network is compared with that of the state-of-the-art algorithms in patient independent validation tasks. The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6.8% and 6.3% on the household data and multi-source data, respectively. Codes are made publicly available on Github.

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“…Also, it would be interesting to add memory to the model using recurrent networks, as the classification of some inputs, following the clinical definition, depends as well on the status of the neighbouring epochs. Biswal et al [22] Massachusetts General Hospital, 1000 recordings 0,77 0,81 0,70 0,77 0,83 0,92 Längkvist et al [18] St Vicent's University Hospital, 25 recordings 0,63 0,73 0,44 0,65 0,86 0,80 Sors et al [23] SHHS, 1730 recordings 0,81 0,91 0,43 0,88 0,85 0,85 Supratak et al [21] MASS dataset, 62 recordings 0,80 0,87 0,60 0,90 0,82 0,89 Supratak et al [21] SleepEDF, 20 recordings 0,76 0,85 0,47 0,86 0,85 0,82 Tsinalis et al [19] SleepEDF, 39 recordings 0,71 0,72 0,47 0,85 0,84 0,81 Tsinalis et al [20] SleepEDF, 39 recordings 0,66 0,67 0,44 0,81 0,85 0,76…”

Section: Discussionmentioning

confidence: 99%

A Convolutional Network for the Classification of Sleep Stages

Fernández-Varela

Hernández-Pereira

Moret‐Bonillo

2018

XoveTIC Congress 2018

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Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep. However, it is complex and tedious, and even the trained expert can spend several hours scoring a single night recording. Multiple automatic methods have tried to solve these problems in the past, most of them by classifying a feature vector that is engineered for a specific dataset. In this work, we avoid this bias using a deep learning model that learns relevant features without human intervention. Particularly, we propose an ensemble of 5 convolutional networks that achieves a kappa index of 0.83 when classifying a dataset of 500 sleep recordings.

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A convolutional neural network for sleep stage scoring from raw single-channel EEG

Cited by 357 publications

References 20 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

A Convolutional Network for the Classification of Sleep Stages

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