TY - GEN
T1 - Automatic EOG and EMG artifact removal method for sleep stage classification
AU - Gharbali, Ali Abdollahi
AU - Fonseca, José Manuel
AU - Najdi, Shirin
AU - Rezaii, Tohid Yousefi
N1 - sem pdf.
FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS.
PY - 2016
Y1 - 2016
N2 - In this paper, a new algorithm is proposed for artifact removing of sleep electroencephalogram (EEG) with application in sleep stage classification. Rather than other works which used artificial noise, in this study real EEG data contaminated with electro-oculogram (EOG) and electromyogram (EMG) are used for evaluating the proposed artifact removal algorithm’s efficiency using classification accuracy. The artifact detection is performed by thresholding the EEG-EOG and EEG-EMG cross correlation coefficients. Then, the segments considered contaminated are denoised by normalized least-mean squares (NLMS) adaptive filtering technique. Using a single EEG channel, four sleep stages consisting of Awake, Stage1 + REM, Stage 2 and Slow Wave Stage (SWS) are classified. A wavelet packet (WP) based feature set together with artificial neural network (ANN) are deployed for sleep stage classification purpose. Simulation results show that artifact removed EEG allows a classification accuracy improvement of around 14%.
AB - In this paper, a new algorithm is proposed for artifact removing of sleep electroencephalogram (EEG) with application in sleep stage classification. Rather than other works which used artificial noise, in this study real EEG data contaminated with electro-oculogram (EOG) and electromyogram (EMG) are used for evaluating the proposed artifact removal algorithm’s efficiency using classification accuracy. The artifact detection is performed by thresholding the EEG-EOG and EEG-EMG cross correlation coefficients. Then, the segments considered contaminated are denoised by normalized least-mean squares (NLMS) adaptive filtering technique. Using a single EEG channel, four sleep stages consisting of Awake, Stage1 + REM, Stage 2 and Slow Wave Stage (SWS) are classified. A wavelet packet (WP) based feature set together with artificial neural network (ANN) are deployed for sleep stage classification purpose. Simulation results show that artifact removed EEG allows a classification accuracy improvement of around 14%.
KW - Adaptive filtering
KW - Artifact detection
KW - Artifact removing
KW - Sleep stage classification
KW - Wavelet packet
UR - http://www.scopus.com/inward/record.url?scp=84962079867&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-31165-4_15
DO - 10.1007/978-3-319-31165-4_15
M3 - Conference contribution
AN - SCOPUS:84962079867
SN - 978-3-319-31164-7
VL - 470
T3 - IFIP Advances in Information and Communication Technology
SP - 142
EP - 150
BT - Technological Innovation for Cyber-Physical Systems - 7th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2016, Proceedings
PB - Springer New York LLC
T2 - 7th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2016
Y2 - 11 April 2016 through 13 April 2016
ER -