TY - GEN
T1 - An improved simulated annealing genetic algorithm of EEG feature selection in sleep stage
AU - Ji, Yundong
AU - Bu, Xiangeng
AU - Sun, Jinwei
AU - Liu, Zhiyong
N1 - Publisher Copyright:
© 2016 Asia Pacific Signal and Information Processing Association.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - In order to establish a more reliable and robust EEG model in sleep stages, the reasonable choice of modeling parameters is necessary. The function of this step is to select a subset of d features from a set of D features based on some optimization criterion, and provide the most optimal input features of classification. In the present study, an improved simulated annealing genetic algorithm (ISAGA) was proposed. 25 feature parameters were extracted from the sleep EEG in MIT-BIH polysomnography database. The feature selection results demonstrated that ISAGA can get a higher classification accuracy with fewer feature number than the correlation coefficient algorithm (CCA), genetic algorithm (GA), adaptive genetic algorithm (AGA) and simulated annealing genetic algorithm (SAGA). Compared to using all the features in sleep staging, the classification accuracy of ISAGA with optimal features is about 92.00%, which improved about 4.83%.
AB - In order to establish a more reliable and robust EEG model in sleep stages, the reasonable choice of modeling parameters is necessary. The function of this step is to select a subset of d features from a set of D features based on some optimization criterion, and provide the most optimal input features of classification. In the present study, an improved simulated annealing genetic algorithm (ISAGA) was proposed. 25 feature parameters were extracted from the sleep EEG in MIT-BIH polysomnography database. The feature selection results demonstrated that ISAGA can get a higher classification accuracy with fewer feature number than the correlation coefficient algorithm (CCA), genetic algorithm (GA), adaptive genetic algorithm (AGA) and simulated annealing genetic algorithm (SAGA). Compared to using all the features in sleep staging, the classification accuracy of ISAGA with optimal features is about 92.00%, which improved about 4.83%.
UR - https://www.scopus.com/pages/publications/85013762739
U2 - 10.1109/APSIPA.2016.7820683
DO - 10.1109/APSIPA.2016.7820683
M3 - 会议稿件
AN - SCOPUS:85013762739
T3 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
BT - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Y2 - 13 December 2016 through 16 December 2016
ER -