title: Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.
abstract: OBJECTIVE
  Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge.
  
  
  METHODS
  In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely - Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors' knowledge. The proposed feature extraction scheme's performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature.
  
  
  RESULTS
  The performance of the proposed method is comparable or better than that of the state-of-the-art ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM.
  
  
  CONCLUSIONS
  Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research.
authors.affiliation: Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh.
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 1205, Bangladesh. Electronic address: ahnaf.hassan0@gmail.com.
authors.forename: Mohammed Imamul Hassan
authors.initials: MIH
authors.lastname: Bhuiyan
mesh_terms: D000328:Adult; D000368:Aged; D001331:Automation; D004569:Electroencephalography / Q000379:methods*; D036262:Empirical Research; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012894:Sleep Stages / Q000502:physiology*; D055815:Young Adult
keywords: EEG; EEMD; RUSBoost; Sleep stage classification; Statistical features