Review of Deep Learning Methods for Automated Sleep Staging
Masoud Malekzadeh, Parisa Hajibabaee, Maryam Heidari, Brett Berlin
Abstract
In order to diagnose sleep problems, it is critical to correctly identify sleep stages which is a labor-intensive task. Due to rising data volumes, advanced algorithms, and improvements in computational power and storage, artificial intelligence has been more popular in recent years. Automated sleep staging through cardiac rhythm is one of the active research areas that has gained attention over the last decade. In this study, we review four recent state-of-the-art deep learning methods for automated sleep staging, datasets developed in recent years, and discuss their performance evaluations.
Topics & Concepts
Computer scienceSleep (system call)Deep learningArtificial intelligenceTask (project management)Machine learningData scienceEngineeringSystems engineeringOperating systemEEG and Brain-Computer InterfacesObstructive Sleep Apnea ResearchSleep and Work-Related Fatigue