MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis
Mar 3, 2023
Automatic sleep stage classification with deep residual networks in a mixed-cohort setting
Manual annotation of polysomnography studies is subject to human bias with multiple studies showing variations in how sleep experts score sleep. Most research in automatic sleep stage classification models use small-scale data from a single origin, and it is unknown how these models generalize to new data. We developed an algorithm for automatic scoring of sleep stages using raw polysomnography data and obtain state-of-the-art classification performance on a large number of test subjects. Our algorithm was tested under different conditions to compare generalizability. We found that using data from many different sources improves classification performance, and that models trained on single-origin data generalize inconsistently to new data. Future researchers should take multiple datasets into account when developing sleep scoring models.
Jan 1, 2021
Proteomic biomarkers of sleep apnea
Nov 12, 2020
Automatic Detection of Cortical Arousals in Sleep and Their Contribution to Daytime Sleepiness
Jun 1, 2020
Design of a Deep Learning Model for Automatic Scoring of Periodic and Non-Periodic Leg Movements during Sleep Validated against Multiple Human Experts
May 1, 2020
Neural Network Analysis of Sleep Stages Enables Efficient Diagnosis of Narcolepsy
Dec 6, 2018
Comparison of computerized methods for rapid eye movement sleep without atonia detection
Jul 13, 2018
A comparative study of methods for automatic detection of rapid eye movement abnormal muscular activity in narcolepsy
Feb 27, 2018