title: Detecting slow wave sleep using a single EEG signal channel.
abstract: BACKGROUND
  In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal.
  
  
  METHODS
  The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences.
  
  
  RESULTS
  The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812.
  
  
  CONCLUSIONS
  With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods.
authors: Department of Mechanical and Electro-mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
authors: Bo-Lin
authors: BL
authors: Su
authors: School of Engineering, Sun Yat-Sen University, Guangzhou 510006, China.
authors: Yuxi
authors: Y
authors: Luo
authors: Department of Mechanical and Electro-mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
authors: Chih-Yuan
authors: CY
authors: Hong
authors: Department of Mechanical Engineering, Marquette University, Milwaukee, WI, USA.
authors: Mark L
authors: ML
authors: Nagurka
authors: Department of Mechanical and Electro-mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan; Department of Physical Therapy, Kaohsiung Medical University, Kaohsiung, Taiwan. Electronic address: vincen@mail.nsysu.edu.tw.
authors: Chen-Wen
authors: CW
authors: Yen
mesh_terms: D000328:Adult; D000375:Aging / Q000502:physiology; D001921:Brain / Q000502:physiology* / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology*; D012891:Sleep Apnea Syndromes / Q000503:physiopathology
keywords: Automatic sleep staging; Electroencephalography; Sleep apnea; Slow wave sleep