Prognosis of automated sleep staging based on two-layer ensemble learning stacking model using single-channel EEG signal

2021 ◽  
Author(s):  
Santosh Kumar Satapathy ◽  
D. Loganathan
Keyword(s):  
Ensemble Learning ◽  
Single Channel ◽  
Eeg Signal ◽  
Sleep Staging

Automated Sleep Staging System Based on Ensemble Learning Model Using Single-Channel EEG Signal

2021 ◽  
pp. 186-202
Author(s):  
Santosh Kumar Satapathy ◽  
Hari Kishan Kondaveeti ◽  
Ravisankar Malladi
Keyword(s):  
Ensemble Learning ◽  
Single Channel ◽  
Staging System ◽  
Learning Model ◽  
Eeg Signal ◽  
Sleep Staging

Intelligent Terminal Based on Single Channel EEG Signal LSTM Sleep Staging

2019 ◽  
Vol 09 (06) ◽  
pp. 1156-1168
Author(s):  
振东 陈
Keyword(s):  
Single Channel ◽  
Eeg Signal ◽  
Sleep Staging ◽  
Intelligent Terminal

A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal

2018 ◽  
Vol 104 ◽  
pp. 277-293 ◽  
Author(s):  
Saman Seifpour ◽  
Hamid Niknazar ◽  
Mohammad Mikaeili ◽  
Ali Motie Nasrabadi
Keyword(s):  
Single Channel ◽  
Staging System ◽  
Eeg Signal ◽  
Sleep Staging ◽  
Local Extrema ◽  
Statistical Behavior

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

An Automated System for Diagnosis of Sleep Apnea Syndrome Using Single-Channel EEG Signal

2019 ◽  
Author(s):  
Saba Bayatfar ◽  
Saman Seifpour ◽  
Mohammadreza Asghari Oskoei ◽  
Ali Khadem
Keyword(s):  
Sleep Apnea ◽  
Single Channel ◽  
Sleep Apnea Syndrome ◽  
Automated System ◽  
Eeg Signal ◽  
Apnea Syndrome

scholarly journals Identification of optimum segment in single channel EEG biometric system

2021 ◽  
Vol 23 (3) ◽  
pp. 1847
Author(s):  
Muhammad Afif Hendrawan ◽  
Pramana Yoga Saputra ◽  
Cahya Rahmad
Keyword(s):  
Single Channel ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Alpha Band ◽  
Biometric System ◽  
Linear Discriminant ◽  
Power Spectral ◽  
Eyes Open ◽  
High Level

Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.

scholarly journals Effective automated method for detection and suppression of muscle artefacts from single‐channel EEG signal

2020 ◽  
Vol 7 (2) ◽  
pp. 35-40 ◽  
Author(s):  
Manali Saini ◽  
Udit Satija ◽  
Madhur Deo Upadhayay
Keyword(s):  
Single Channel ◽  
Eeg Signal ◽  
Automated Method
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