title: A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features.
abstract: Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme.
  
  
  
  In this work, we decompose sleep-EEG signal segments using tunable-Q factor wavelet transform (TQWT). Various spectral features are then computed from TQWT sub-bands. The performance of spectral features in the TQWT domain has been determined by intuitive and graphical analyses, statistical validation, and Fisher criteria. Random forest is used to perform classification. Optimal choices and the effects of TQWT and random forest parameters have been determined and expounded.
  
  
  
  Experimental outcomes manifest the efficacy of our feature generation scheme in terms of p-values of ANOVA analysis and Fisher criteria. The proposed scheme yields 90.38%, 91.50%, 92.11%, 94.80%, 97.50% for 6-stage to 2-stage classification of sleep states on the benchmark Sleep-EDF data-set. In addition, its performance on DREAMS Subjects Data-set is also promising.
  
  
  
  The performance of the proposed method is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient. Additionally, the proposed scheme gives high detection accuracy for sleep stages non-REM 1 and REM.
  
  
  
  Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously. The proposed scheme will alleviate the burden of the physicians, speed-up sleep disorder diagnosis, and expedite sleep research.
authors.affiliation: Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh. Electronic address: ahnaf.hassan0@gmail.com.
authors.forename: Ahnaf Rashik
authors.initials: AR
authors.lastname: Hassan
authors.affiliation: Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.
authors.forename: Mohammed Imamul Hassan
authors.initials: MI
authors.lastname: Bhuiyan
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000502:physiology; D003661:Decision Support Techniques; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D012894:Sleep Stages / Q000502:physiology; D012893:Sleep Wake Disorders / Q000175:diagnosis / Q000503:physiopathology; D058067:Wavelet Analysis; D055815:Young Adult
keywords: EEG; Random forest; Sleep stage classification; Spectral features; Tunable-Q factor wavelet transform