3 results match your criteria: "Honeynaps Research and Development Center[Affiliation]"
A deep learning algorithm model to automatically score and grade obstructive sleep apnea in adult polysomnography.
Digit Health
October 2024
Honeynaps Research and Development Center, Honeynaps Co. Ltd, Seoul, Republic of Korea.
Objective: Polysomnography (PSG) is unique in diagnosing sleep disorders, notably obstructive sleep apnea (OSA). Despite its advantages, manual PSG data grading is time-consuming and laborious. Thus, this research evaluated a deep learning-based automated scoring system for respiratory events in sleep-disordered breathing patients.
View Article and Find Full Text PDFEfficacy and safety of digital therapeutic application of Sleep Index-Based Treatment for Insomnia (dSIBT-I): a pilot study.
J Sleep Res
February 2024
Department of Otorhinolaryngology-Head and Neck Surgery, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, South Korea.
Article Synopsis
- - This study aimed to analyze the safety and effectiveness of a digital therapy for insomnia called dSIBT-I, comparing it with another digital therapy, dCBT-I, at Asan Medical Center with 50 participants over one month.
- - Both treatments showed significant improvements in sleep scores after four weeks, but there was no major difference between the two therapies in overall effectiveness.
- - Notably, at the two-week mark, dSIBT-I outperformed dCBT-I in reducing insomnia severity and had no reported adverse events, suggesting it's a safe and effective option for quick relief.
Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm.
Medicina (Kaunas)
June 2022
Honeynaps Research and Development Center, Honeynaps Co., Ltd., 4F, 529, Nonhyeon-ro, Gangnam-gu, Seoul 06126, Korea.
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.
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