Investigation of an automatic sleep stage classification by means of multiscorer hypnogram

Methods of Information in Medicine
V C Figueroa HellandN Wessel

Abstract

Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers. We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and only on epochs where the three experts agree in their sleep stage scoring. We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in app...Continue Reading

Citations

Aug 10, 2011·Biomedizinische Technik. Biomedical Engineering·Andrej GapelyukNiels Wessel
Aug 27, 2015·Journal of Pharmacological and Toxicological Methods·Icilio Cavero, Henry Holzgrefe
Feb 14, 2015·Computers in Biology and Medicine·Teresa SousaUrbano Nunes
Sep 16, 2014·PloS One·Sabrina CamargoNiels Wessel
Jul 12, 2018·IEEE Reviews in Biomedical Engineering·Sirinthip RoomkhamDimitri Perrin
Feb 8, 2020·Journal of Sleep Research·Kristin M GunnarsdottirSridevi V Sarma

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