Literature DB >> 20829636

Computer-assisted sleep classification according to the standard of the American Academy of Sleep Medicine: validation study of the AASM version of the Somnolyzer 24 × 7.

Peter Anderer1, Arnaud Moreau, Michael Woertz, Marco Ross, Georg Gruber, Silvia Parapatics, Erna Loretz, Esther Heller, Andrea Schmidt, Marion Boeck, Doris Moser, Gerhard Kloesch, Bernd Saletu, Gerda M Saletu-Zyhlarz, Heidi Danker-Hopfe, Josef Zeitlhofer, Georg Dorffner.   

Abstract

BACKGROUND: In 2007, the AASM Manual for the Scoring of Sleep and Associated Events was published by the American Academy of Sleep Medicine (AASM). Concerning the visual classification of sleep stages, these new rules are intended to replace the rules by Rechtschaffen and Kales (R&K).
METHODS: We adapted the automatic R&K sleep scoring system Somnolyzer 24 × 7 to comply with the AASM rules and subsequently performed a validation study based on 72 polysomnographies from the Siesta database (56 healthy subjects, 16 patients, 38 females, 34 males, aged 21-86 years). Scorings according to the AASM rules were performed manually by experienced sleep scorers and semi-automatically by the AASM version of the Somnolyzer. Manual scorings and Somnolyzer reviews were performed independently by at least 2 out of 8 experts from 4 sleep centers.
RESULTS: In the quality control process, sleep experts corrected 4.8 and 3.7% of the automatically assigned epochs, resulting in a reliability between 2 Somnolyzer-assisted scorings of 99% (Cohen's kappa: 0.99). In contrast, the reliability between the 2 manual scorings was 82% (kappa: 0.76). The agreement between the 2 Somnolyzer-assisted and the 2 visual scorings was between 81% (kappa: 0.75) and 82% (kappa: 0.76).
CONCLUSION: The AASM version of the Somnolyzer revealed an agreement between semi-automated and human expert scoring comparable to that published for the R&K version with a validity comparable to that of human experts, but with a reliability close to 1, thereby reducing interrater variability as well as scoring time to a minimum.
Copyright © 2010 S. Karger AG, Basel.

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Mesh:

Year:  2010        PMID: 20829636     DOI: 10.1159/000320864

Source DB:  PubMed          Journal:  Neuropsychobiology        ISSN: 0302-282X            Impact factor:   2.328


  29 in total

1.  Computer-Assisted Automated Scoring of Polysomnograms Using the Somnolyzer System.

Authors:  Naresh M Punjabi; Naima Shifa; Georg Dorffner; Susheel Patil; Grace Pien; Rashmi N Aurora
Journal:  Sleep       Date:  2015-10-01       Impact factor: 5.849

2.  Effects of a Workplace-Based Sleep Health Program on Sleep in Members of the German Armed Forces.

Authors:  Cornelia Sauter; Jens T Kowalski; Michael Stein; Stefan Röttger; Heidi Danker-Hopfe
Journal:  J Clin Sleep Med       Date:  2019-03-15       Impact factor: 4.062

Review 3.  Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity.

Authors:  Diego R Mazzotti; Diane C Lim; Kate Sutherland; Lia Bittencourt; Jesse W Mindel; Ulysses Magalang; Allan I Pack; Philip de Chazal; Thomas Penzel
Journal:  Physiol Meas       Date:  2018-09-13       Impact factor: 2.833

4.  Performance of an automated polysomnography scoring system versus computer-assisted manual scoring.

Authors:  Atul Malhotra; Magdy Younes; Samuel T Kuna; Ruth Benca; Clete A Kushida; James Walsh; Alexandra Hanlon; Bethany Staley; Allan I Pack; Grace W Pien
Journal:  Sleep       Date:  2013-04-01       Impact factor: 5.849

5.  Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

Authors:  Linda Zhang; Daniel Fabbri; Raghu Upender; David Kent
Journal:  Sleep       Date:  2019-10-21       Impact factor: 5.849

6.  A novel sleep stage scoring system: Combining expert-based features with the generalized linear model.

Authors:  Kristin M Gunnarsdottir; Charlene Gamaldo; Rachel Marie Salas; Joshua B Ewen; Richard P Allen; Katherine Hu; Sridevi V Sarma
Journal:  J Sleep Res       Date:  2020-02-07       Impact factor: 3.981

7.  Validation of the System One RemStar Auto A-Flex for Obstructive Sleep Apnea Treatment and Detection of Residual Apnea-Hypopnea Index: A European Randomized Trial.

Authors:  Frédéric Gagnadoux; Dirk Pevernagie; Poul Jennum; Nina Lon; Corinne Loiodice; Renaud Tamisier; Petra van Mierlo; Wojciech Trzepizur; Martina Neddermann; Annika Machleit; Jeffrey Jasko; Jean Louis Pépin
Journal:  J Clin Sleep Med       Date:  2017-02-15       Impact factor: 4.062

8.  Artificial intelligence in sleep medicine: background and implications for clinicians.

Authors:  Cathy A Goldstein; Richard B Berry; David T Kent; David A Kristo; Azizi A Seixas; Susan Redline; M Brandon Westover
Journal:  J Clin Sleep Med       Date:  2020-04-15       Impact factor: 4.062

Review 9.  Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review.

Authors:  Diego Alvarez-Estevez; Vicente Moret-Bonillo
Journal:  Sleep Disord       Date:  2015-07-21

10.  Large-Scale Automated Sleep Staging.

Authors:  Haoqi Sun; Jian Jia; Balaji Goparaju; Guang-Bin Huang; Olga Sourina; Matt Travis Bianchi; M Brandon Westover
Journal:  Sleep       Date:  2017-10-01       Impact factor: 5.849

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