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 1201 Performance Evaluation Of A Novel Contactless Breathing Monitor And Machine Learning Algorithm For Sleep Stage Classification In A Healthy Population

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A459-A459
Author(s):  
T Lauteslager ◽  
S Kampakis ◽  
A J Williams ◽  
M Maslik ◽  
F Siddiqui
Keyword(s):  
Machine Learning ◽  
Home Environment ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
Sleep Stages ◽  
Obstructive Sleep ◽  
Longitudinal Data Collection ◽  
Sleep Assessment ◽  
Stage Classification ◽  
Sleep Stage Classification

Abstract Introduction Although polysomnography (PSG) remains the gold standard for sleep assessment in a lab setting, non-EEG signals such as respiration and motion are directly affected by sleep stages and can be used for sleep stage prediction. Importantly, these signals can be obtained in a low-cost and unobtrusive manner, allowing for large scale and longitudinal data collection in a home environment. The Circadia C100 System (FDA 510(k) clearance expected Q1 2020) is a novel ‘nearable’ device that uses radar for contactless monitoring of respiration and motion. The current study aims to validate the performance of the associated sleep analysis algorithm. Methods A total of 41 nights of sleep data were recorded from 33 healthy participants using the device, alongside PSG. Data were recorded both in a sleep lab and home environment. PSG data were scored by RPSGT-certified technicians. Respiration and movement features were extracted, and machine learning algorithms were developed to perform sleep stage classification and predict sleep metrics. Algorithms were trained and validated on PSG data using cross-validation. Results An epoch-by-epoch true positive rate of 56.2%, 79.4%, 55.5% and 72.6% was found for ‘Wake’, ‘REM’, ‘Light’ and ‘Deep’ respectively. No statistical differences in performance were found between home-recorded and lab-recorded contactless data. Mean absolute error of total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE) was 13.2 minutes, 11.3 minutes and 3%, respectively. The contactless monitor was found to outperform both medical grade and clinical grade actigraphy based devices: The Philips Actiwatch Spectrum Plus and the Fitbit Alta HR. Conclusion Current results are encouraging and suggest that the contactless monitor could be used for long-term sleep assessment and continuous evaluation of sleep therapy outcomes. Further clinical validation work is ongoing in subjects diagnosed with sleep disorders such as obstructive sleep apnea. Support -

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

A MULTI-LAYER HYBRID MACHINE LEARNING MODEL FOR AUTOMATIC SLEEP STAGE CLASSIFICATION

2018 ◽  
Vol 30 (06) ◽  
pp. 1850041
Author(s):  
Thakerng Wongsirichot ◽  
Anantaporn Hanskunatai
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Learning Model ◽  
Alternative Methods ◽  
Sleep Stages ◽  
Machine Learning Model ◽  
Subject Specific ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Hybrid Machine

Sleep Stage Classification (SSC) is a standard process in the Polysomnography (PSG) for studying sleep patterns and events. The SSC provides sleep stage information of a patient throughout an entire sleep test. A physician uses results from SSCs to diagnose sleep disorder symptoms. However, the SSC data processing is time-consuming and requires trained sleep technicians to complete the task. Over the years, researchers attempted to find alternative methods, which are known as Automatic Sleep Stage Classification (ASSC), to perform the task faster and more efficiently. Proposed ASSC techniques usually derived from existing statistical methods and machine learning (ML) techniques. The objective of this study is to develop a new hybrid ASSC technique, Multi-Layer Hybrid Machine Learning Model (MLHM), for classifying sleep stages. The MLHM blends two baseline ML techniques, Decision Tree (DT) and Support Vector Machine (SVM). It operates on a newly developed multi-layer architecture. The multi-layer architecture consists of three layers for classifying [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], [Formula: see text] in different epoch lengths. Our experiment design compares MLHM and baseline ML techniques and other research works. The dataset used in this study was derived from the ISRUC-Sleep database comprising of 100 subjects. The classification performances were thoroughly reviewed using the hold-out and the 10-fold cross-validation method in both subject-specific and subject-independent classifications. The MLHM achieved a certain satisfactory classification results. It gained 0.694[Formula: see text][Formula: see text][Formula: see text]0.22 of accuracy ([Formula: see text]) in subject-specific classification and 0.942[Formula: see text][Formula: see text][Formula: see text]0.02 of accuracy ([Formula: see text]) in subject-independent classification. The pros and cons of the MLHM with the multi-layer architecture were thoroughly discussed. The effect of class imbalance was rationally discussed towards the classification results.

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>

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

2020 ◽  
Vol 5 (7) ◽  
pp. 1268-1272
Author(s):  
Mayuri A. Rakhonde ◽  
Dr. Kishor P. Wagh ◽  
Prof. Ravi V. Mante
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Learning Algorithm ◽  
Sleep Stage ◽  
Stochastic Gradient Descent ◽  
Sleep Stages ◽  
Power Spectral ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Welch Method

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.

scholarly journals Multi-Branch Convolutional Neural Network for Automatic Sleep Stage Classification with Embedded Stage Refinement and Residual Attention Channel Fusion

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6592
Author(s):  
Tianqi Zhu ◽  
Wei Luo ◽  
Feng Yu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
Classification Problems ◽  
Model Classification ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Refinement Process

Automatic sleep stage classification of multi-channel sleep signals can help clinicians efficiently evaluate an individual’s sleep quality and assist in diagnosing a possible sleep disorder. To obtain accurate sleep classification results, the processing flow of results from signal preprocessing and machine-learning-based classification is typically employed. These classification results are refined based on sleep transition rules. Neural networks—i.e., machine learning algorithms—are powerful at solving classification problems. Some methods apply them to the first two processes above; however, the refinement process continues to be based on traditional methods. In this study, the sleep stage refinement process was incorporated into the neural network model to form real end-to-end processing. In addition, for multi-channel signals, the multi-branch convolutional neural network was combined with a proposed residual attention method. This approach further improved the model classification accuracy. The proposed method was evaluated on the Sleep-EDF Expanded Database (Sleep-EDFx) and University College Dublin Sleep Apnea Database (UCDDB). It achieved respective accuracy rates of 85.7% and 79.4%. The results also showed that sleep stage refinement based on a neural network is more effective than the traditional refinement method. Moreover, the proposed residual attention method was determined to have a more robust channel–information fusion ability than the respective average and concatenation methods.

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.

Automatic Sleep Stage Classification Applying Machine Learning Algorithms on EEG Recordings

2017 ◽  
Author(s):  
Panteleimon Chriskos ◽  
Dimitra S. Kaitalidou ◽  
Georgios Karakasis ◽  
Christos Frantzidis ◽  
Polyxeni T. Gkivogkli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Eeg Recordings
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