SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

Published in

PLoS ONE(2019)

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TL;DR

An automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores.

Abstract

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the performance of the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and κ = 0.79. Our developed model can be applied to other sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.

Authors

Sajad Mousavi

2 papers

Fatemeh Afghah

2 papers

U. R. Acharya

1 papers

References40 items

An Australasian Commentary on the AASM Manual for the Scoring of Sleep and Associated Events

Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Inter- and Intra- Patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach

ECGNET: Learning Where to Attend for Detection of Atrial Fibrillation with Deep Visual Attention

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

Published in

PLoS ONE(2019)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Sajad Mousavi

2 papers

Fatemeh Afghah

2 papers

U. R. Acharya

1 papers

References40 items

An Australasian Commentary on the AASM Manual for the Scoring of Sleep and Associated Events

TL;DR

Abstract

Authors

References40 items

An Australasian Commentary on the AASM Manual for the Scoring of Sleep and Associated Events

Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Inter- and Intra- Patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach

ECGNET: Learning Where to Attend for Detection of Atrial Fibrillation with Deep Visual Attention

TL;DR

Abstract

Authors

References40 items

An Australasian Commentary on the AASM Manual for the Scoring of Sleep and Associated Events

Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Inter- and Intra- Patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach

ECGNET: Learning Where to Attend for Detection of Atrial Fibrillation with Deep Visual Attention

Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

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