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Sleep Apnea, Obstructive

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Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sensors (Basel, Switzerland)
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are c...

Construction and evaluation of a predictive model for the types of sleep respiratory events in patients with OSA based on hypoxic parameters.

Sleep & breathing = Schlaf & Atmung
OBJECTIVE: To explore the differences and associations of hypoxic parameters among distinct types of respiratory events in patients with obstructive sleep apnea (OSA) and to construct prediction models for the types of respiratory events based on hyp...

Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model.

ACS applied materials & interfaces
Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is execute...

Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.

Sleep & breathing = Schlaf & Atmung
PURPOSE: This study aims to develop sleep apnea screening models with overnight SpO2 data, and to investigate the impact of the SpO2 data granularity on model performance.

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Scientific reports
Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluat...

Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning.

Scientific reports
Obstructive sleep apnea (OSA) is closely associated with the development and chronicity of temporomandibular disorder (TMD). Given the intricate pathophysiology of both OSA and TMD, comprehensive diagnostic approaches are crucial. This study aimed to...

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.

Sleep medicine
OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including p...

Navigating ChatGPT's alignment with expert consensus on pediatric OSA management.

International journal of pediatric otorhinolaryngology
OBJECTIVE: This study aimed to evaluate the potential integration of artificial intelligence (AI), specifically ChatGPT, into healthcare decision-making, focusing on its alignment with expert consensus statements regarding the management of persisten...

IPCT-Net: Parallel information bottleneck modality fusion network for obstructive sleep apnea diagnosis.

Neural networks : the official journal of the International Neural Network Society
Obstructive sleep apnea (OSA) is a common sleep breathing disorder and timely diagnosis helps to avoid the serious medical expenses caused by related complications. Existing deep learning (DL)-based methods primarily focus on single-modal models, whi...

SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.

IEEE journal of biomedical and health informatics
Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagno...