NONLINEAR VOLTERRA COEFFICIENTS FOR FEATURE EXTRACTION IN SLEEP STAGE CLASSIFICATION

2017 ◽  
Vol 29 (01) ◽  
pp. 1750007 ◽  
Author(s):  
Malihe Hassani ◽  
Mohammad-Reza Karami
Keyword(s):  
Sleep Stage ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Classification Rate ◽  
Sleep Scoring ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Males And Females ◽  
Neural Classifier ◽  
Better Than

This paper presents a new method for sleep scoring based on nonlinear Volterra features of EEG signals by using only one single EEG channel. The Volterra features are extracted from characteristic waves of EEG signals which can characterize different sleep stages individually. The recurrent neural classifier takes all the features extracted on 30s epochs from EEG signals and assigns them to one of the five possible stages: Wakefulness, NREM 1, NREM 2, SWS, and REM. Eight sleep recordings obtained from Caucasian males and females without any medication are utilized to validate the proposed method. Moreover, the performance of the proposed classifier in comparison with other classifiers is presented. The classification rate of the proposed classifier is better than that of the other classifier that does not use nonlinear Volterra feature. The results demonstrate that the proposed classifier with nonlinear Volterra features of the characteristic waves of EEG signals can classify sleep stages more efficiently and accurately using only a single EEG channel.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals SCL-SSC: Supervised Contrastive Learning for Sleep Stage Classification

2022 ◽  
Author(s):  
Chandra Bhushan Kumar
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Synthetic Data ◽  
Representation Learning ◽  
Feature Representation ◽  
Training Dataset ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification

<div>In this study, we have proposed SCL-SSC(Supervised Contrastive Learning for Sleep Stage Classification), a deep learning-based framework for sleep stage classification which performs the task in two stages, 1) feature representation learning, and 2) classification. The feature learner is trained separately to represent the raw EEG signals in the feature space such that the distance between the embedding of EEG signals of the same sleep stage has less than the distance between the embedding of EEG signals of different sleep stages in the euclidean space. On top of feature learners, we have trained the classifier to perform the classification task. The distribution of sleep stages is not uniform in the PSG data, wake(W) and N2 sleep stages appear more frequently than the other sleep stages, which leads to an imbalance dataset problem. This paper addresses this issue by using weighted softmax cross-entropy loss function and also dataset oversampling technique utilized to produce synthetic data points for minority sleep stages for approximately balancing the number of sleep stages in the training dataset. The performance of our proposed model is evaluated on the publicly available Physionet datasets EDF-Sleep 2013 and 2018 versions. We have trained and evaluated our model on two EEG channels (Fpz-Cz and Pz-Oz) on these datasets separately. The evaluation result shows that the performance of SCL-SSC is the best annotation performance compared to the existing state-of art deep learning algorithms to our best of knowledge, with an overall accuracy of 94.1071% with a macro F1 score of 92.6416 and Cohen’s Kappa coefficient(κ) 0.9197. Our ablation studies on SCL-SSC shows that both triplet loss based pre-training of feature learner and oversampling of minority classes are contributing to better performance of the model(SCL-SSC).</div>

scholarly journals SCL-SSC: Supervised Contrastive Learning for Sleep Stage Classification

2022 ◽  
Author(s):  
Chandra Bhushan Kumar
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Synthetic Data ◽  
Representation Learning ◽  
Feature Representation ◽  
Training Dataset ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification

<div>In this study, we have proposed SCL-SSC(Supervised Contrastive Learning for Sleep Stage Classification), a deep learning-based framework for sleep stage classification which performs the task in two stages, 1) feature representation learning, and 2) classification. The feature learner is trained separately to represent the raw EEG signals in the feature space such that the distance between the embedding of EEG signals of the same sleep stage has less than the distance between the embedding of EEG signals of different sleep stages in the euclidean space. On top of feature learners, we have trained the classifier to perform the classification task. The distribution of sleep stages is not uniform in the PSG data, wake(W) and N2 sleep stages appear more frequently than the other sleep stages, which leads to an imbalance dataset problem. This paper addresses this issue by using weighted softmax cross-entropy loss function and also dataset oversampling technique utilized to produce synthetic data points for minority sleep stages for approximately balancing the number of sleep stages in the training dataset. The performance of our proposed model is evaluated on the publicly available Physionet datasets EDF-Sleep 2013 and 2018 versions. We have trained and evaluated our model on two EEG channels (Fpz-Cz and Pz-Oz) on these datasets separately. The evaluation result shows that the performance of SCL-SSC is the best annotation performance compared to the existing state-of art deep learning algorithms to our best of knowledge, with an overall accuracy of 94.1071% with a macro F1 score of 92.6416 and Cohen’s Kappa coefficient(κ) 0.9197. Our ablation studies on SCL-SSC shows that both triplet loss based pre-training of feature learner and oversampling of minority classes are contributing to better performance of the model(SCL-SSC).</div>

COMBINATION OF HETEROGENEOUS EEG FEATURE EXTRACTION METHODS AND STACKED SEQUENTIAL LEARNING FOR SLEEP STAGE CLASSIFICATION

2013 ◽  
Vol 23 (03) ◽  
pp. 1350012 ◽  
Author(s):  
L. J. HERRERA ◽  
C. M. FERNANDES ◽  
A. M. MORA ◽  
D. MIGOTINA ◽  
R. LARGO ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Sleep Stage ◽  
Extraction Methods ◽  
Sequential Learning ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Accuracy Rate ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Electroencephalogram Eeg

This work proposes a methodology for sleep stage classification based on two main approaches: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier. The feature extraction methods used in this work include three representative ways of extracting information from EEG signals: Hjorth features, wavelet transformation and symbolic representation. Feature selection was then used to evaluate the relevance of individual features from this set of methods. Stacked sequential learning uses a second-layer classifier to improve the classification by using previous and posterior first-layer predicted stages as additional features providing information to the model. Results show that both approaches enhance the sleep stage classification accuracy rate, thus leading to a closer approximation to the experts' opinion.

scholarly journals Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Sarun Paisarnsrisomsuk ◽  
Carolina Ruiz ◽  
Sergio A. Alvarez
Keyword(s):  
Input Data ◽  
Single Channel ◽  
Sleep Stage ◽  
Quantitative Measure ◽  
Classification Performance ◽  
Sleep Stages ◽  
Layer Depth ◽  
Human Sleep ◽  
Stage Classification ◽  
Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

2016 ◽  
Vol 28 (10) ◽  
pp. 3095-3112 ◽  
Author(s):  
Mehmet Dursun ◽  
Seral Özşen ◽  
Cüneyt Yücelbaş ◽  
Şule Yücelbaş ◽  
Gülay Tezel ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Sleep Eeg ◽  
Eeg Signals ◽  
New Approach ◽  
Stage Classification ◽  
Sleep Stage Classification

End-to-end machine learning on raw EEG signals for sleep stage classification

2019 ◽  
Vol 64 ◽  
pp. S139
Author(s):  
E. Gunnlaugsson ◽  
H. Ragnarsdóttir ◽  
H.M. þráinsson ◽  
E. Finnsson ◽  
S.Æ. Jónsson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
End To End

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network

2018 ◽  
Vol 63 (2) ◽  
pp. 177-190 ◽  
Author(s):  
Junming Zhang ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Experimental Results ◽  
Sleep Stages ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Complex Valued ◽  
Learned Features

AbstractMany systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

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