TY - JOUR
T1 - Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models
AU - Liang, Sheng Fu
AU - Kuo, Chin En
AU - Hu, Yu Han
AU - Pan, Yu Hsiang
AU - Wang, Yung Hung
N1 - Funding Information:
Manuscript received May 19, 2011; revised October 11, 2011; accepted January 5, 2012. Date of publication March 6, 2012; date of current version May 11, 2012. This work was supported by the National Science Council of Taiwan under Grants NSC 98-2221-E-006-161-MY3 and NSC 100-2220-E-006-010. The Associate Editor coordinating the review process for this paper was Dr. Jiong Tang.
PY - 2012
Y1 - 2012
N2 - In this paper, we propose an automatic sleep-scoring method combining multiscale entropy (MSE) and autoregressive (AR) models for single-channel EEG and to assess the performance of the method comparatively with manual scoring based on full polysomnograms. This is the first time that MSE has ever been applied to sleep scoring. All-night polysomnograms from 20 healthy individuals were scored using the Rechtschaffen and Kales rules. The developed method analyzed the EEG signals of C3-A2 for sleep staging. The results of automatic and manual scorings were compared on an epoch-by-epoch basis. A total of 8480 30-s sleep EEG epochs were measured and used for performance evaluation. The epoch-by-epoch comparison was made by classifying the EEG epochs into five states (Wake/REM/S1/S2/SWS) by the proposed method and manual scoring. The overall sensitivity and kappa coefficient of MSE alone are 76.9% and 0.65, respectively. Moreover, the overall sensitivity and kappa coefficient of our proposed method of integrating MSE, AR models, and a smoothing process can reach the sensitivity level of 88.1% and 0.81, respectively. Our results show that MSE is a useful and representative feature for sleep staging. It has high accuracy and good home-care applicability because a single EEG channel is used for sleep staging.
AB - In this paper, we propose an automatic sleep-scoring method combining multiscale entropy (MSE) and autoregressive (AR) models for single-channel EEG and to assess the performance of the method comparatively with manual scoring based on full polysomnograms. This is the first time that MSE has ever been applied to sleep scoring. All-night polysomnograms from 20 healthy individuals were scored using the Rechtschaffen and Kales rules. The developed method analyzed the EEG signals of C3-A2 for sleep staging. The results of automatic and manual scorings were compared on an epoch-by-epoch basis. A total of 8480 30-s sleep EEG epochs were measured and used for performance evaluation. The epoch-by-epoch comparison was made by classifying the EEG epochs into five states (Wake/REM/S1/S2/SWS) by the proposed method and manual scoring. The overall sensitivity and kappa coefficient of MSE alone are 76.9% and 0.65, respectively. Moreover, the overall sensitivity and kappa coefficient of our proposed method of integrating MSE, AR models, and a smoothing process can reach the sensitivity level of 88.1% and 0.81, respectively. Our results show that MSE is a useful and representative feature for sleep staging. It has high accuracy and good home-care applicability because a single EEG channel is used for sleep staging.
KW - Automatic sleep scoring
KW - autoregressive (AR) model
KW - linear discriminant analysis (LDA)
KW - multiscale entropy (MSE)
KW - single-channel electroencephalogram (EEG)
UR - http://www.scopus.com/inward/record.url?scp=84862786511&partnerID=8YFLogxK
U2 - 10.1109/TIM.2012.2187242
DO - 10.1109/TIM.2012.2187242
M3 - 期刊論文
AN - SCOPUS:84862786511
SN - 0018-9456
VL - 61
SP - 1649
EP - 1657
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
IS - 6
M1 - 6165354
ER -