title: Development and comparison of four sleep spindle detection methods.
abstract: OBJECTIVE
  The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles.
  
  
  METHODS
  All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored bilateral spindles occurring in frontopolar or central brain location. In the present work a new sigma index for spindle detection was developed, based on the fast Fourier transform (FFT) spectrum, aiming at approximating our previous fuzzy spindle detector. The sigma index was complemented with spindle amplitude analysis, based on finite impulse response (FIR) filtering, to form of a combination detector of bilateral spindles. In this combination detector, the spindle amplitude distribution of each recording was estimated and used to tune two different amplitude thresholds. This combination detector was compared to bilaterally extracted sigma indexes and fuzzy detections, which aim to be independent of absolute spindle amplitudes. As a fourth method a fixed spindle amplitude detector was included.
  
  
  RESULTS
  The combination detector provided the best overall performance; in S2 sleep a 70% true positive rate was reached with a specificity of 98.6%, and a false-positive rate of 32%. The bilateral sigma indexes provided the second best results, followed by fuzzy detector, while the fixed amplitude detector provided the poorest results so that in S2 sleep a 70% true positive rate was reached with a specificity of 97.7% and false-positive rate of 46%. The spindle amplitude distributions automatically determined for each recording by the combination detector were compared to amplitudes of visually scored spindles and they proved to correspond well. Inter-hemispheric amplitude variation of visually scored bilateral spindles is also presented.
  
  
  CONCLUSIONS
  Flexibility is beneficial in the detection of bilateral spindles. The present work advances automated spindle detection and increases the knowledge of bilateral sleep spindle characteristics.
authors.affiliation: Department of Clinical Neurophysiology, Medical Imaging Centre, Pirkanmaa Hospital District, P.O. Box 2000, Tampere, Finland. eero.huupponen@elisanet.fi
authors.forename: Eero
authors.initials: E
authors.lastname: Huupponen
authors.forename: Germán
authors.initials: G
authors.lastname: Gómez-Herrero
authors.forename: Antti
authors.initials: A
authors.lastname: Saastamoinen
authors.forename: Alpo
authors.initials: A
authors.lastname: Värri
authors.forename: Joel
authors.initials: J
authors.lastname: Hasan
authors.forename: Sari-Leena
authors.initials: SL
authors.lastname: Himanen
mesh_terms: D000328:Adult; D001931:Brain Mapping; D004569:Electroencephalography; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D012890:Sleep / Q000502:physiology*; D012894:Sleep Stages / Q000502:physiology