title: Improved spindle detection through intuitive pre-processing of electroencephalogram.
abstract: BACKGROUND
  Numerous signal processing techniques have been proposed for automated spindle detection on EEG recordings with varying degrees of success. While the latest techniques usually introduce computational complexity and/or vagueness, the conventional techniques attempted in literature have led to poor results. This study presents a spindle detection approach which relies on intuitive pre-processing of the EEG prior to spindle detection, thus resulting in higher accuracy even with standard techniques.
  
  
  METHODS
  The pre-processing techniques proposed include applying the derivative operator on the EEG, suppressing the background activity using Empirical Mode Decomposition and shortlisting candidate EEG segments based on eye-movements on the EOG.
  Results show that standard signal processing tools such as wavelets and Fourier transforms perform much better when coupled with apt pre-processing techniques. The developed algorithm also relies on data-driven thresholds ensuring its adaptability to inter-subject and inter-scorer variability. When tested on sample EEG segments scored by multiple experts, the algorithm identified spindles with average sensitivities of 96.14 and 92.85% and specificities of 87.59 and 84.85% for Fourier transform and wavelets respectively. These results are found to be on par with results obtained by other recent studies in this area.
authors.affiliation: Texas A&M University at Qatar, Doha, Qatar.
authors.forename: Abdul
authors.initials: A
authors.lastname: Jaleel
authors.affiliation: Texas A&M University at Qatar, Doha, Qatar.
authors.forename: Beena
authors.initials: B
authors.lastname: Ahmed
authors.affiliation: Texas A&M University at Qatar, Doha, Qatar. Electronic address: reza.tafreshi@qatar.tamu.edu.
authors.forename: Reza
authors.initials: R
authors.lastname: Tafreshi
authors.affiliation: Douglas Hospital Research Center, Montréal, Canada.
authors.forename: Diane B
authors.initials: DB
authors.lastname: Boivin
authors.affiliation: Weill Cornell Medical College in Qatar, Doha, Qatar.
authors.forename: Leopold
authors.initials: L
authors.lastname: Streletz
authors.affiliation: Weill Cornell Medical College in Qatar, Doha, Qatar.
authors.forename: Naim
authors.initials: N
authors.lastname: Haddad
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D004585:Electrooculography / Q000379:methods; D053483:Eye Movement Measurements; D005583:Fourier Analysis; D006801:Humans; D008959:Models, Neurological; D011336:Probability; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology*
keywords: Algorithms; EEG; EOG; Fourier transform; Spindle detection; Wavelets