AIMC Topic: Apnea

Clear Filters Showing 1 to 4 of 4 articles

Automated hypoxia and apnea identification for neonates via enhanced respiratory signal modeling with deep learning.

Scientific reports
Neonatal respiratory monitoring is crucial for assessing breathing patterns, but the lack of real-time clinical data limits the development of machine learning (ML) models. This study provides a synthetic signal generation framework to replicate infa...

SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography.

IEEE journal of biomedical and health informatics
Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pr...

Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals.

PloS one
Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography an...

Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

Artificial intelligence in medicine
OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health Organization, India has the highest number of pre-term births [1], with over 3.5 million babies born...