title: Sleep-spindle identification on EEG signals from polysomnographie recordings using correntropy.
abstract: Sleep spindles (SSs) are characteristic electroencephalographic (EEG) waveforms of sleep stages N2 and N3. One of the main problems associated with SS detection is the high number of false positives. In this paper we propose a new periodogram based on correntropy to detect SSs and enhance their characterization. Correntropy is a generalized correlation, under the information theoretic learning framework. A non-negative matrix factorization decomposition of correntropy allows us to obtain a new periodogram, which shows an improved resolution capability compared to the conventional power spectrum density. Preliminary results show that the proposed method obtained a sensitivity rate of 0.868 with a false positive rate of 0.121.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Ulloa
authors.forename: Pablo A
authors.initials: PA
authors.lastname: Estevez
authors.forename: Pablo
authors.initials: P
authors.lastname: Huijse
authors.forename: Claudio M
authors.initials: CM
authors.lastname: Held
authors.forename: Claudio A
authors.initials: CA
authors.lastname: Perez
authors.forename: Rodrigo
authors.initials: R
authors.lastname: Chamorro
authors.forename: Marcelo
authors.initials: M
authors.lastname: Garrido
authors.forename: Cecilia
authors.initials: C
authors.lastname: Algarin
authors.forename: Patricio
authors.initials: P
authors.lastname: Peirano
mesh_terms: D000465:Algorithms; D002648:Child; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D017286:Polysomnography / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology; D012894:Sleep Stages / Q000502:physiology*