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- Title
Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm †.
- Authors
Cho, Jae Hoon; Choi, Ji Ho; Moon, Ji Eun; Lee, Young Jun; Lee, Ho Dong; Ha, Tae Kyoung
- Abstract
Background and Objectives: Polysomnography is manually scored by sleep experts. However, manual scoring is a time-consuming and labor-intensive task. The goal of this study was to verify the accuracy of automated sleep-stage scoring based on a deep learning algorithm compared to manual sleep-stage scoring. Materials and Methods: A total of 602 polysomnography datasets from subjects (Male:Female = 397:205) aged 19 to 65 years (mean age, 43.8, standard deviation = 12.2) were included in the study. The performance of the proposed model was evaluated based on kappa value and bootstrapped point-estimate of median percent agreement with a 95% bootstrap confidence interval and R = 1000. The proposed model was trained using 482 datasets and validated using 48 datasets. For testing, 72 datasets were selected randomly. Results: The proposed model exhibited good concordance rates with manual scoring for stages W (94%), N1 (83.9%), N2 (89%), N3 (92%), and R (93%). The average kappa value was 0.84. For the bootstrap method, high overall agreement between the automated deep learning algorithm and manual scoring was observed in stages W (98%), N1 (94%), N2 (92%), N3 (99%), and R (98%) and total (96%). Conclusions: Automated sleep-stage scoring using the proposed model may be a reliable method for sleep-stage classification.
- Subjects
MACHINE learning; DEEP learning; SLEEP stages; STANDARD deviations; CONFIDENCE intervals
- Publication
Medicina (1010660X), 2022, Vol 58, Issue 6, p779
- ISSN
1010-660X
- Publication type
Article
- DOI
10.3390/medicina58060779