A Novel Multi-Class EEG-Based Sleep Stage Classification System

Memar, Pejman; Faradji, Farhad

doi:10.1109/tnsre.2017.2776149

Cited by 232 publications

(123 citation statements)

References 57 publications

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“…In the proposed method by extracting 59 features from EEG and EOG signals, the algorithm achieved higher accuracy compared to Hassan et al [13] and Jiang et al [59] studies which used higher number of features. 10. In the two-class mode (sleep and Wake), due to the fundamental differences in the characteristics of sleep and Wake stages and the simplicity of the classification problem, the classifiers have been able to obtain similar results, so that the lowest accuracy…”

Section: Experiments 3: Performance Evaluation Of the Propose Algormentioning

confidence: 99%

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Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

et al. 2020

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The most important part of sleep quality assessment is the classification of sleep stages, which helps to diagnose sleep-related disease. In the traditional sleep staging method, subjects have to spend a night in the sleep clinic for recording polysomnogram. Sleep expert classifies the sleep stages by monitoring the signals, which is time consuming and frustrating task and can be affected by human error. New studies propose fully automated techniques for classifying sleep stages that makes sleep scoring possible at home. Despite comprehensive studies have been presented in this field the classification results have not yet reached the gold standard due to the concentration on the use of a limited source of information such as single channel EEG. Therefore, this article introduces a new method for fusing two sources of information, including electroencephalogram (EEG) and electrooculogram (EOG), to achieve promising results in the classification of sleep stages. In the proposed method, extracted features from the EEG and EOG signals, are divided into two feature sets consisting of the EEG features and fused features of EEG and EOG. Then, each feature set transformed into a horizontal visibility graph (HVG). The images of the HVG are produced in a novel framework and classified by proposed transfer learning convolutional neural network for data fusion (TLCNN-DF). Employing transfer learning at the training stage of the model has accelerated the training process of the CNN and improved the performance of the model. The proposed algorithm is used to classify the Sleep-EDF and Sleep-EDFx benchmark datasets. The algorithm can classify the Sleep-EDF dataset with an accuracy of 93.58% and Cohen's kappa coefficient of 0.899. The results show proposed method can achieve superior performance compared to state-of-the-art studies on classification of sleep stages. Furthermore, it can attain reliable results as an alternative to conventional sleep staging. INDEX TERMS Convolutional neural network, data fusion, horizontal visibility graph, Sleep stage classification, transfer learning.

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Section: Experiments 3: Performance Evaluation Of the Propose Algormentioning

confidence: 99%

“…2. Using supervised learning algorithms such as support vector machine (SVM) [15], random forest (RF) [10], decision tree (DT) [16], k-nearest neighbor (KNN) [17] and deep neural networks (DNN) [18] to classify feature vectors that belong to different sleep stages.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

et al. 2020

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“…Extracting informative, descriptive, discriminative and independent features is a complex step to prepare a suitable set of values for a classifier [8]. This work employs a mixed approach (i.e., time-frequency analysis) to extract features from the EEG signal.…”

Section: Feature Extractionmentioning

confidence: 99%

Sleep Stage Classification for Prediction of Human Sleep Disorders by Using Machine Learning Approach

Rakhonde¹,

Wagh²,

Mante³

2020

IJISRT

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Sleep is a fundamental need of human body. In order to maintain health, sufficient sleep is must. Efficiency of sleep is based on sleep stages. Sleep stage classification is required to identify sleep disorders. Sleep stage classification identifies different stages of sleep. In this paper, we used Stochastic Gradient Descent(SGD) a machine learning algorithm for sleep stage classification. In feature extraction, Power Spectral Density(Welch method) is used. We acheived 89% overall accuracy using this model.

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“…The performance is evaluated in a private database with 4 subjects. In [15], 104 features are extracted from the EEG signals and selected features are used to classify 4 sleep stages (N1 and N2 are combined) by random forests. The following papers classify 5 sleep stages like our work.…”

Section: Introductionmentioning

confidence: 99%

Diffuse to fuse EEG spectra – Intrinsic geometry of sleep dynamics for classification

Liu

Malik

et al. 2020

Biomedical Signal Processing and Control

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We propose a novel algorithm for sleep dynamics visualization and automatic annotation by applying diffusion geometry based sensor fusion algorithm to fuse spectral information from two electroencephalograms (EEG). The diffusion geometry approach helps organize the nonlinear dynamical structure hidden in the EEG signal. The visualization is achieved by the nonlinear dimension reduction capability of the chosen diffusion geometry algorithms. For the automatic annotation purpose, the support vector machine is trained to predict the sleep stage. The prediction performance is validated on a publicly available benchmark database, Physionet Sleep-EDF [extended] SC * (SC = Sleep Cassette) and ST * (ST = Sleep Telemetry), with the leave-one-subjectout cross validation. When we have a single EEG channel (Fpz-Cz), the overall accuracy, macro F1 and Cohen's kappa achieve 82.72%,75.91% and 76.1% respectively in Sleep-EDF SC * and 78.63%, 73.58% and 69.48% in Sleep-EDF ST * . This performance is compatible with the state-of-the-art results. When we have two EEG channels (Fpz-Cz and Pz-Oz), the overall accuracy, macro F1 and Cohen's kappa achieve 84.44%,78.25% and 78.36% respectively in Sleep-EDF SC * and 79.05%, 74.73% and 70.31% in Sleep-EDF ST * . The results suggest the potential of the proposed algorithm in practical applications.

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A Novel Multi-Class EEG-Based Sleep Stage Classification System

Cited by 232 publications

References 57 publications

Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

Sleep Stage Classification for Prediction of Human Sleep Disorders by Using Machine Learning Approach

Diffuse to fuse EEG spectra – Intrinsic geometry of sleep dynamics for classification

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