Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach.

ABSTRACT: Sleep apnea (SLA) is a respiratory-related sleep disorder that affects a major proportion of the population. The gold standard in sleep testing, polysomnography, is costly, inconvenient, and unpleasant, and it requires a skilled professional to score. Multiple researchers have suggested and developed automated scoring processes with less detectors and automated classification algorithms to resolve these problems. An automatic detection system will allow for a high diagnosis rate and the analysis of additional patients. Deep learning (DL) is achieving high priority due to the availability of databases and recently developed methods. As the most up-and-coming technique for classification and generative tasks, DL has shown its significant potential in 2-dimensional clinical image processing s

SUBMITTER: Tyagi PK 

PROVIDER: S-EPMC10382448 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature