TY - GEN
T1 - Sleep stages classification by CW Doppler radar using bagged trees algorithm
AU - Zhang, Li
AU - Xiong, Junjun
AU - Zhao, Heng
AU - Hong, Hong
AU - Zhu, Xiaohua
AU - Li, Changzhi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/7
Y1 - 2017/6/7
N2 - The quality of sleep has a great impact on health and life quality. A classification of sleep stage is very important for managing the quality of sleep. This paper presents a method for classifying sleep stages based on the bagged trees classifier with a continuous-wave (CW) Doppler radar. In the experiment, a subject was asked to sleep all night with polysomnography (PSG) to get the labels of the nine features extracted from the radar signals. Four kinds of tree classifiers were selected as the machine learning algorithm to classify wakefulness, rapid eye movement sleep (REM), light sleep and deep sleep. A 10-fold cross-validation procedure was used for testing the classification performance. Compared to PSG results, the bagged trees classifier has the best classification accuracy rate among the four classifiers. Using appropriate parameter of the base learner, the accuracy rate can be improved to 78.6%.
AB - The quality of sleep has a great impact on health and life quality. A classification of sleep stage is very important for managing the quality of sleep. This paper presents a method for classifying sleep stages based on the bagged trees classifier with a continuous-wave (CW) Doppler radar. In the experiment, a subject was asked to sleep all night with polysomnography (PSG) to get the labels of the nine features extracted from the radar signals. Four kinds of tree classifiers were selected as the machine learning algorithm to classify wakefulness, rapid eye movement sleep (REM), light sleep and deep sleep. A 10-fold cross-validation procedure was used for testing the classification performance. Compared to PSG results, the bagged trees classifier has the best classification accuracy rate among the four classifiers. Using appropriate parameter of the base learner, the accuracy rate can be improved to 78.6%.
KW - Bagged trees algorithm
KW - CW Doppler radar
KW - Feature extraction
KW - Sleep stage
UR - http://www.scopus.com/inward/record.url?scp=85021424260&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2017.7944310
DO - 10.1109/RADAR.2017.7944310
M3 - Conference contribution
AN - SCOPUS:85021424260
T3 - 2017 IEEE Radar Conference, RadarConf 2017
SP - 788
EP - 791
BT - 2017 IEEE Radar Conference, RadarConf 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Radar Conference, RadarConf 2017
Y2 - 8 May 2017 through 12 May 2017
ER -