AIMC Topic: Sleep Apnea, Obstructive
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Enhanced machine learning approaches for OSA patient screening: model development and validation study.
Scientific reports
Aug 26, 2024
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
Aug 21, 2024
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...
Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.
Sensors (Basel, Switzerland)
Jul 25, 2024
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...
Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.
Journal of sleep research
Jul 18, 2024
Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obst...
ECGAN-Assisted ResT-Net Based on Fuzziness for OSA Detection.
IEEE transactions on bio-medical engineering
Jul 18, 2024
OBJECTIVE: Growing attention has been paid recently to electrocardiogram (ECG) based obstructive sleep apnea (OSA) detection, with some progresses been made on this topic. However, the lack of data, low data quality, and incomplete data labeling hind...
Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks.
IEEE journal of biomedical and health informatics
Jul 2, 2024
OBJECTIVE: wearable sensor technology has progressed significantly in the last decade, but its clinical usability for the assessment of obstructive sleep apnea (OSA) is limited by the lack of large and representative datasets simultaneously acquired ...
Predicting Therapeutic Response to Hypoglossal Nerve Stimulation Using Deep Learning.
The Laryngoscope
Jun 27, 2024
OBJECTIVES: To develop and validate machine learning (ML) and deep learning (DL) models using drug-induced sleep endoscopy (DISE) images to predict the therapeutic efficacy of hypoglossal nerve stimulator (HGNS) implantation.
EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.
Biomedical engineering online
Jun 20, 2024
OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data s...
Machine learning methods for adult OSAHS risk prediction.
BMC health services research
Jun 5, 2024
BACKGROUND: Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk fac...
Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.
Annals of clinical and translational neurology
Feb 23, 2024
OBJECTIVE: This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index ...
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