Automated Sleep Stage Classification Based on Multiple Channels of Electroencephalographic Signals Using Machine Learning Algorithm

2020 ◽  
pp. 69-83
Author(s):  
Santosh Kumar Satapathy ◽  
D. Loganathan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Sleep Stage ◽  
Machine Learning Algorithm ◽  
Multiple Channels ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Electroencephalographic Signals

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 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.

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

Sleep stage classification using fuzzy sets and machine learning techniques

2004 ◽  
Vol 58-60 ◽  
pp. 1137-1143 ◽  
Author(s):  
Pedro Piñero ◽  
Pavel Garcia ◽  
Leticia Arco ◽  
Alfredo Álvarez ◽  
M.Matilde Garcı́a ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fuzzy Sets ◽  
Sleep Stage ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Stage Classification ◽  
Sleep Stage Classification

scholarly journals Validation of sleep stage classification using non-contact radar technology and machine learning (Somnofy®)

2020 ◽  
Vol 75 ◽  
pp. 54-61 ◽  
Author(s):  
Ståle Toften ◽  
Ståle Pallesen ◽  
Maria Hrozanova ◽  
Frode Moen ◽  
Janne Grønli
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Stage Classification ◽  
Sleep Stage Classification

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 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 -

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