SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach (2019-03-05T00:00:00.000000Z)

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

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

Predictive Modeling of Biomedical Signals Using Controlled Spatial Transformation

A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach

Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units

Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

Remote ECG Monitoring Kit to Predict Patient-Specific Heart Abnormalities

SLEEPNET: Automated Sleep Staging System via Deep Learning

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

Learning to predict where to look in interactive environments using deep recurrent q-learning

Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

Deep Reinforcement Learning: An Overview

Training deep neural networks on imbalanced data sets

Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders

Automatic abstraction controller in reinforcement learning agent via automata

Sequence to Sequence Learning with Neural Networks

Analyzing Neural Time Series Data: Theory and Practice

Automatic sleep stage recurrent neural classifier using energy features of EEG signals

Automatic sleep stage recurrent neural classifier using energy features of EEG signals

Learning Decision Tree Using Neural Network for Stability and Flexibility

An ensemble system for automatic sleep stage classification using single channel EEG signal

Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier

Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG

Bidirectional recurrent neural networks

A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects.

Stages of Sleep and Sleep Cycles

Applying Q ( λ )-learning in Deep Reinforcement Learning to Play Atari Games

Using deep bidirectional recurrent neural networks for prosodic-target prediction in a unit-selection text-to-speech system

The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications

The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications

SMOTE: Synthetic Minority Over-sampling Technique

Physionet: components of a new research resource for complex physiologic signals

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