Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea
Obstructive sleep apnea (OSA) represents a significant health concern. While polysomnography (PSG) remains the gold standard, its resource-intensive nature has encouraged the exploration of further alternative approaches. Most of these were based on the heart rate variability (HRV) analysis, but onl...
Saved in:
Main Authors: | Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/1/433 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Diagnosis and treatment of obstructive sleep apnea
by: Natalia Myśliwiec, et al.
Published: (2025-01-01) -
The role of artificial intelligence in the treatment of obstructive sleep apnea
by: Hannah L. Brennan, et al.
Published: (2023-02-01) -
Myofunctional Therapy for the Treatment of Obstructive Sleep Apnea: Systematic Review and Meta-Analysis
by: Lucas Gabriel dos Anjos Ferreira, et al.
Published: (2025-01-01) -
Obesity and polysomnographic variables on patients with obstructive sleep apnea
by: Chaiane Facco Piccin, et al.
Published: (2015-07-01) -
Differences in patients derived from otolaryngology and other specialties with sleep apnea
by: Constanza Salas, et al.
Published: (2019-10-01)