Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases.
Objective: To assess the validity of an automatic EEG arousal detection algorithm using large patient samples and different heterogeneous databases.Methods: Automatic scorings were confronted with results from human expert scorers on a total of 2768 full-night PSG recordings obtained from two differ...
| Publicado en: | Sleep Medicine Vol. 57; pp. 6 - 15 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
May2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136349344&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136349344 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13899457 JJR jtl: Sleep Medicine issn: 13899457 maglogo: N pubinfo: dt: May2019 vid: 57 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 136349344 136349344 NLM30878899 136349344 10.1016/j.sleep.2019.01.025 NLM30878899 136349344 ppf: 6 ppct: 9 formats: tig: atl: Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases. aug: au: Alvarez-Estevez, Diego Fernández-Varela, Isaac affil: Sleep Center and Clinical Neurophysiology Department, Haaglanden Medisch Centrum, The Hague, The Netherlands sug: subj: Algorithms Sleep Physiology Resource Databases Electroencephalography Middle Age Human Polysomnography Arousal Reproducibility of Results Female Aged Male Validation Studies Comparative Studies Evaluation Research Multicenter Studies Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Objective: To assess the validity of an automatic EEG arousal detection algorithm using large patient samples and different heterogeneous databases.Methods: Automatic scorings were confronted with results from human expert scorers on a total of 2768 full-night PSG recordings obtained from two different databases. Of them, 472 recordings were obtained during a clinical routine at our sleep center and were subdivided into two subgroups of 220 (HMC-S) and 252 (HMC-M) recordings each, according to the procedure followed by the clinical expert during the visual review (semi-automatic or purely manual, respectively). In addition, 2296 recordings from the public SHHS-2 database were evaluated against the respective manual expert scorings.Results: Event-by-event epoch-based validation resulted in an overall Cohen's kappa agreement of κ = 0.600 (HMC-S), 0.559 (HMC-M), and 0.573 (SHHS2). Estimated inter-scorer variability on the datasets was, respectively, κ = 0.594, 0.561 and 0.543. Analyses of the corresponding Arousal Index scores showed associated automatic-human repeatability indices ranges of 0.693-0.771 (HMC-S), 0.646-0.791 (HMC-M), and 0.759-0.791 (SHHS2).Conclusions: Large-scale validation of our automatic EEG arousal detector on different databases has shown robust performance and good generalization results comparable to the expected levels of human agreement. Special emphasis was put on reproducibility of the results; implementation of our method has been made available online as open source code. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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