Automatic Classification of Sleep Stages Based on Raw Single-Channel EEG

2020 ◽  
pp. 356-368
Author(s):  
Kailin Xu ◽  
Siyu Xia ◽  
Guang Li
Keyword(s):  
Single Channel ◽  
Automatic Classification ◽  
Sleep Stages

Automatic classification of sleep stages from single-channel electroencephalogram

2015 ◽  
Author(s):  
Ahnaf Rashik Hassan ◽  
Syed Khairul Bashar ◽  
Mohammed Imamul Hassan Bhuiyan
Keyword(s):  
Single Channel ◽  
Automatic Classification ◽  
Sleep Stages

scholarly journals Single‐channel EEG classification of sleep stages based on REM microstructure

2021 ◽  
Author(s):  
Irene Rechichi ◽  
Maurizio Zibetti ◽  
Luigi Borzì ◽  
Gabriella Olmo ◽  
Leonardo Lopiano
Keyword(s):  
Single Channel ◽  
Sleep Stages ◽  
Eeg Classification

Deep convolutional neural network for classification of sleep stages from single-channel EEG signals

2019 ◽  
Vol 324 ◽  
pp. 108312 ◽  
Author(s):  
Z. Mousavi ◽  
T. Yousefi Rezaii ◽  
S. Sheykhivand ◽  
A. Farzamnia ◽  
S.N. Razavi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Deep Convolutional Neural Network ◽  
Sleep Stages ◽  
Eeg Signals

WAVELET FEATURES BASED SLEEP STAGES DETECTION USING SINGLE CHANNEL EEG

2017 ◽  
Vol 5 (4) ◽  
pp. 99-102
Author(s):  
Vandana Roy ◽  
Anand Prakash ◽  
Shailja Shukla
Keyword(s):  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Single Channel ◽  
Kappa Coefficient ◽  
Sleep Stages ◽  
Chi Square ◽  
Wavelet Features ◽  
Stage Classification ◽  
Selection Of

The sleep stages determination is important for the identification and diagnosis of different diseases. An efficient algorithm of wavelet decomposition is used for feature extraction of single channel EEG. The Chi-Square method is applied for the selection of the best attributes from the extracted features. The classification of different staged techniques is applied with the help AdaBoost.M1 algorithm. The accuracy of 89.82% achieved in the six stage classification. The weighted sensitivity of all stages is 89.8% and kappa coefficient of 77.93% is obtained in the six stage classification.

RECOGNITION OF SLEEP STAGES BASED ON A COMBINED NEURAL NETWORK AND FUZZY SYSTEM USING WAVELET TRANSFORM FEATURES

2014 ◽  
Vol 26 (02) ◽  
pp. 1450029 ◽  
Author(s):  
Chuang-Chien Chiu ◽  
Bui Huy Hai ◽  
Shoou-Jeng Yeh
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Single Channel ◽  
Sleep Stages ◽  
Elman Neural Network ◽  
Sleep Studies ◽  
Average Accuracy ◽  
Wavelet Decompositions

Recognition of sleep stages is an important task in the assessment of the quality of sleep. Several biomedical signals, such as EEG, ECG, EMG and EOG are used extensively to classify the stages of sleep, which is very important for the diagnosis of sleep disorders. Many sleep studies have been conducted that focused on the automatic classification of sleep stages. In this research, a new classification method is presented that uses an Elman neural network combined with fuzzy rules to extract sleep features based on wavelet decompositions. The nine subjects who participated in this study were recruited from Cheng-Ching General Hospital in Taichung, Taiwan. The sampling frequency was 250 Hz, and a single-channel (C3-A1) EEG signal was acquired for each subject. The system consisted of a combined neural network and fuzzy system that was used to recognize sleep stages based on epochs (10-second segments of data). The classification results relied on the strong points of combined neural network and fuzzy system, which achieved an average specificity of approximately 96% and an average accuracy of approximately 94%.

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