Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks

Phan, Huy; Andreotti, Fernando; Cooray, Navin; Chén, Oliver Y.; Vos, Maarten De

doi:10.1109/embc.2018.8512480

Cited by 112 publications

(96 citation statements)

References 13 publications

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“…Here, we employ a bidirectional RNN coupled with the attention mechanism [48], [49] to learn sequential features for epoch representation. Due to the RNN's sequential modelling capability, it is expected to capture temporal dynamics of input signals to produce good features [24]. For convenience, we interpret the image X after the filterbank layers as a sequence of T feature vectors X ≡ (…”

Section: Short-term Sequential Modellingmentioning

confidence: 99%

“…To this end, prior works can be grouped into one-to-one, many-to-one, one-to-many schemes as illustrated in Figure 1 (a)-(c), respectively. Following the one-to-one scheme, a classification model receives a single PSG epoch as input at a time and produces a single corresponding output label [14], [15], [24], [26]. Although being straightforward, this classification scheme cannot take into account the existing dependency between PSG epochs [4], [8], Figure 1: Illustration of the classification schemes used for automatic sleep staging.…”

Section: Introductionmentioning

confidence: 99%

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SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Phan

Andreotti

Cooray

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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412

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Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, 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 1 . 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 modelling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling 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 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.Index Terms-automatic sleep staging, hierarchical recurrent neural networks, end-to-end, sequence-to-sequence.H. Phan is with the School of Computing, University of Kent, Chatham Maritime, Kent ME4 4AG, United Kingdom and the Institute

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Section: Short-term Sequential Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Phan

Andreotti

Cooray

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

412

320

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“…finetuning entire network). [12] No 81.9 0.740 73.8 73.9 95.0 1-max CNN [17] No 79.8 0.720 72.0 − − Atentional RNN [23] No 79.1 0.700 69.8 − − Deep auto-encoder [14] No 78.9 − 73.3 − − Deep CNN [13] No The finetuning results also unveil that finetuning the softmax layer alone is not sufficient to overcome the channel-mismatch obstacle. Instead, it is important to additionally finetune those feature-learning layers, either the ARNN subnetwork for epoch-level feature learning or the SeqRNN for sequence-level feature learning or both collectively.…”

Section: Network Parametersmentioning

confidence: 99%

Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch

Phan

Chén

Koch

et al. 2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Many sleep studies suffer from the problem of insufficient data to fully utilize deep neural networks as different labs use different recordings set ups, leading to the need of training automated algorithms on rather small databases, whereas large annotated databases are around but cannot be directly included into these studies for data compensation due to channel mismatch. This work presents a deep transfer learning approach to overcome the channel mismatch problem and transfer knowledge from a large dataset to a small cohort to study automatic sleep staging with single-channel input. We employ the state-of-theart SeqSleepNet and train the network in the source domain, i.e. the large dataset. Afterwards, the pretrained network is finetuned in the target domain, i.e. the small cohort, to complete knowledge transfer. We study two transfer learning scenarios with slight and heavy channel mismatch between the source and target domains. We also investigate whether, and if so, how finetuning entirely or partially the pretrained network would affect the performance of sleep staging on the target domain. Using the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and the Sleep-EDF Expanded database consisting of 20 subjects as the target domain in this study, our experimental results show significant performance improvement on sleep staging achieved with the proposed deep transfer learning approach. Furthermore, these results also reveal the essential of finetuning the feature-learning parts of the pretrained network to be able to bypass the channel mismatch problem.

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“…We also compared DeepSleep with recent state-of-the-art methods in sleep stage scoring. These methods extracted features from 30-second epochs through short-time Fourier transform (STFT) 27,28 or Thomson’s multitaper 25,29 . They were originally designed for automatic sleep staging and we applied them to the task of detecting sleep arousals on the same 2018 PhysioNet data.…”

Section: Resultsmentioning

confidence: 99%

DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

Guan

2019

Preprint

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Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with many negative effects including daytime sleepiness and sleep disorders. High-quality annotation of polysomnographic recordings is crucial for the diagnosis of sleep arousal disorders. Currently, sleep arousals are mainly annotated by human experts through looking at millions of data points manually, which requires considerable time and effort. Here we present a deep learning approach, DeepSleep, which ranked first in the 2018 PhysioNet Challenge for automatically segmenting sleep arousal regions based on polysomnographic recordings. DeepSleep features accurate (area under receiver operating characteristic curve of 0.93), high-resolution (5-millisecond resolution), and fast (10 seconds per sleep record) delineation of sleep arousals.

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Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 112 publications

References 13 publications

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch

DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

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