TY - GEN
T1 - Sleep stage classification based on EEG signal by using EMD and DFA algorithm
AU - Zhang, Yan
AU - Xing, Jing
AU - Guo, Chuanjun
AU - Yang, Chunling
AU - Liu, Xin
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/12/26
Y1 - 2018/12/26
N2 - With the increasing pressure of life in the present society, sleep problem has gradually become an important problem that influences most people. Sleep stage monitoring has become a reference standard for evaluating sleep quality, diagnosing sleep disorders and preventing sleep apnea. At present, the commonly used polysomnography sleep stage monitoring method has some shortcomings, such as complicated operation process, huge volume of monitoring equipment, extremely inconvenient measurement process, and the possibility to induce a variety of physiological artifacts interference. In this paper, electroencephalogram (EEG) is adopted to analyze the signal of the brain and thus to classify the sleep stage. Empirical mode decomposition method is used to denoise the EEG signals and the detrended fluctuation analysis (DFA) method is applied to extract the scale characteristics of the EEG. Artificial neural network is further employed to classify the sleep stage. The results show that the proposed method can discriminate different stages during sleep.
AB - With the increasing pressure of life in the present society, sleep problem has gradually become an important problem that influences most people. Sleep stage monitoring has become a reference standard for evaluating sleep quality, diagnosing sleep disorders and preventing sleep apnea. At present, the commonly used polysomnography sleep stage monitoring method has some shortcomings, such as complicated operation process, huge volume of monitoring equipment, extremely inconvenient measurement process, and the possibility to induce a variety of physiological artifacts interference. In this paper, electroencephalogram (EEG) is adopted to analyze the signal of the brain and thus to classify the sleep stage. Empirical mode decomposition method is used to denoise the EEG signals and the detrended fluctuation analysis (DFA) method is applied to extract the scale characteristics of the EEG. Artificial neural network is further employed to classify the sleep stage. The results show that the proposed method can discriminate different stages during sleep.
KW - Detrended fluctuation index
KW - EEG
KW - Empirical mode decomposition
KW - Neural network
KW - Sleep stage
UR - https://www.scopus.com/pages/publications/85064259090
U2 - 10.1145/3303714.3303723
DO - 10.1145/3303714.3303723
M3 - 会议稿件
AN - SCOPUS:85064259090
T3 - ACM International Conference Proceeding Series
SP - 156
EP - 160
BT - Proceedings of the International Conference on Robotics, Control and Automation Engineering, RCAE 2018 and 2018 International Conference on Advanced Mechanical and Electrical Engineering, AMEE 2018
PB - Association for Computing Machinery
T2 - 2018 International Conference on Robotics, Control and Automation Engineering, RCAE 2018
Y2 - 26 December 2018 through 28 December 2018
ER -