Sleep stages classification by CW Doppler radar using bagged trees algorithm

Li Zhang, Junjun Xiong, Heng Zhao, Hong Hong, Xiaohua Zhu, Changzhi Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations


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%.

Original languageEnglish
Title of host publication2017 IEEE Radar Conference, RadarConf 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781467388238
StatePublished - Jun 7 2017
Event2017 IEEE Radar Conference, RadarConf 2017 - Seattle, United States
Duration: May 8 2017May 12 2017

Publication series

Name2017 IEEE Radar Conference, RadarConf 2017


Conference2017 IEEE Radar Conference, RadarConf 2017
Country/TerritoryUnited States


  • Bagged trees algorithm
  • CW Doppler radar
  • Feature extraction
  • Sleep stage


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