Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model - Journal of Biomedical Science and Engineering

JBiSE > Vol.10 No.5, May 2017

Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model ()

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DOI: 10.4236/jbise.2017.105B002 1,172 Downloads 2,379 Views

Author(s)

Jiabin Li¹, Bei Wang¹, Takenao Sugi², Yu Zhang¹, Masatoshi Nakamura³

Affiliation(s)

¹Department of Automation, East University of Science and Technology, Shanghai, China.
²Department of Electrical and Electronic Engineering, Saga University, Saga, Japan.
³Research Institute of Systems Control, Institute for Advanced Research and Education, Saga University, Saga, Japan.

ABSTRACT

Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

KEYWORDS

Sleep Spindle Detection, Complex Demodulation Method, Decision Tree Mod-el, EEG

Share and Cite:

Li, J. , Wang, B. , Sugi, T. , Zhang, Y. and Nakamura, M. (2017) Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model. Journal of Biomedical Science and Engineering, 10, 10-17. doi: 10.4236/jbise.2017.105B002.

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