Editorial: Machine Learning and Wearable Technology in Sleep Medicine
Author:
Publisher
Frontiers Media SA
Subject
General Engineering
Reference4 articles.
1. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis;Benjafield;Lancet Respir Med.,2019
2. Sleep assessment of hospitalised patients: a literature review;Hoey;Int J Nurs Stud.,2014
3. Potential underestimation of sleep apnea severity by at-home kits: rescoring in-laboratory polysomnography without sleep staging;Bianchi;J Clin Sleep Med.,2017
4. A review on wearable photoplethysmography sensors and their potential future applications in health care;Castaneda;Int J Biosens Bioelectron.,2018
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1. Validation of a sleep staging classification model for healthy adults based on two combinations of a single-channel EEG headband and wrist actigraphy;Journal of Clinical Sleep Medicine;2024-06
2. Artificial Intelligence in Sleep Medicine: The Dawn of a New Era;Nature and Science of Sleep;2024-04
3. Machine learning in physical activity, sedentary, and sleep behavior research;Journal of Activity, Sedentary and Sleep Behaviors;2024-01-30
4. A Wearable Device Integrated with Deep Learning‐Based Algorithms for the Analysis of Breath Patterns;Advanced Intelligent Systems;2023-08-18
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