A deep learning algorithm model to automatically score and grade obstructive sleep apnea in adult polysomnography DOI Creative Commons
Marn Joon Park, Ji Ho Choi, Shin Young Kim

et al.

Digital Health, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 1, 2024

Polysomnography (PSG) is unique in diagnosing sleep disorders, notably obstructive apnea (OSA). Despite its advantages, manual PSG data grading time-consuming and laborious. Thus, this research evaluated a deep learning-based automated scoring system for respiratory events sleep-disordered breathing patients.

Language: Английский

Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals DOI Creative Commons
Verónica Barroso-García,

Marta Fernández-Poyatos,

Benjamín Sahelices

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(20), P. 3187 - 3187

Published: Oct. 12, 2023

The high prevalence of sleep apnea and the limitations polysomnography have prompted investigation strategies aimed at automated diagnosis using a restricted number physiological measures. This study to demonstrate that thoracic (THO) abdominal (ABD) movement signals are useful for accurately estimating severity apnea, even if central respiratory events present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly THO ABD automatically estimate evaluate event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 root mean square error (RMSE) 10.33 events/h when apnea-hypopnea index, ICC 0.83 RMSE 0.95 index. CNN obtained accuracies 94.98%, 79.82%, 81.60% 5, 15, 30 evaluating complete hypopnea model improved nature was central: 98.72% 99.74% accuracy 5 15 events/h. Hence, information extracted from these CNNs could be powerful tool diagnose especially in subjects with density events.

Language: Английский

Citations

4
Discrete learning-based intelligent methodology for heart disease diagnosis DOI
Mehdi Khashei, Negar Bakhtiarvand

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 84, P. 104700 - 104700

Published: Feb. 27, 2023

Language: Английский

Citations

3
SSMDA: Semi-supervised multi-source domain adaptive autism prediction model using neuroimaging DOI

Mehak Mengi,

Deepti Malhotra

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 95, P. 106337 - 106337

Published: April 18, 2024

Language: Английский

Citations

0
Precise detection of diabetic retinopathy using adaptive remora optimization algorithm with deep adversarial approach DOI

Sambit S Mondal,

Nirupama Mandal, Krishna Kant Singh

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: July 17, 2024

Language: Английский

Citations

0
A deep learning algorithm model to automatically score and grade obstructive sleep apnea in adult polysomnography DOI Creative Commons
Marn Joon Park, Ji Ho Choi, Shin Young Kim

et al.

Digital Health, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 1, 2024

Polysomnography (PSG) is unique in diagnosing sleep disorders, notably obstructive apnea (OSA). Despite its advantages, manual PSG data grading time-consuming and laborious. Thus, this research evaluated a deep learning-based automated scoring system for respiratory events sleep-disordered breathing patients.

Language: Английский

Citations

0