A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea.

Characterization of sleep stages is essential in the diagnosis of sleep-related disorders but relies on manual scoring of overnight polysomnography (PSG) recordings, which is onerous and labor-intensive. Accordingly, we aimed to develop an accurate deep-learning model for sleep staging in children suffering from pediatric obstructive sleep apnea (OSA) using pulse oximetry signals. For this purpose, pulse rate (PR) and blood oxygen saturation (SpO) from 429 childhood OSA patients were analyzed.

An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea.

Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings. Accordingly, we aimed to develop an accurate and interpretable deep-learning model for sleep staging in children using single-channel electroencephalogram (EEG) recordings. We used EEG signals from the Childhood Adenotonsillectomy Trial (CHAT) dataset (n = 1637) and a clinical sleep database (n = 980).

Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.

Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal.

Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures.

Obstructive sleep apnea (OSA) is a multidimensional disease often underdiagnosed due to the complexity and unavailability of its standard diagnostic method: the polysomnography. Among the alternative abbreviated tests searching for a compromise between simplicity and accurateness, oximetry is probably the most popular. The blood oxygen saturation (SpO) signal is characterized by a near-constant profile in healthy subjects breathing normally, while marked drops (desaturations) are linked to respiratory events.

Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea.

The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful and large easily available databases, and suitable methods to assess performances.

Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity.

Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to significantly negative implications on morbidity and mortality. In these patients, underdiagnosis of OSA is high.

Slow EEG Oscillation to Characterize Pediatric Sleep Apnea and Associated Cognitive Impairments.

Previous studies have suggested that the typical slow oscillations (SO) characteristics during sleep could be modified in the presence of pediatric obstructive sleep apnea (OSA). Here, we evaluate whether these modifications are significant and if they may reflect cognitive deficits. We recorded the overnight electroencephalogram (EEG) of 294 pediatric subjects (5-9 years old) using eight channels.

A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry.

The gold standard approach to diagnose obstructive sleep apnea (OSA) in children is overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous to healthcare systems and families. Simplification of PSG should enhance availability and comfort, and reduce complexity and waitlists. Airflow (AF) and oximetry (SpO) signals summarize most of the information needed to detect apneas and hypopneas, but automatic analysis of these signals using deep-learning algorithms has not been extensively investigated in the pediatric context.

N-antigenemia detection by a rapid lateral flow test predicts 90-day mortality in COVID-19: A prospective cohort study.

Objectives: To evaluate if the detection of N antigen of SARS-CoV-2 in plasma by a rapid lateral flow test predicts 90-day mortality in COVID-19 patients hospitalized at the wards.

Methods: The presence of N-antigenemia was evaluated in the first 36 hours after hospitalization in 600 unvaccinated COVID-19 patients, by using the Panbio COVID-19 Ag Rapid Test Device from Abbott (Abbott Laboratories Inc., Chicago, IL, USA).

Automatic Sleep Staging in Children with Sleep Apnea using Photoplethysmography and Convolutional Neural Networks.

Sleep staging is of paramount importance in children with suspicion of pediatric obstructive sleep apnea (OSA). Complexity, cost, and intrusiveness of overnight polysomnography (PSG), the gold standard, have led to the search for alternative tests. In this sense, the photoplethysmography signal (PPG) carries useful information about the autonomous nervous activity associated to sleep stages and can be easily acquired in pediatric sleep apnea home tests with a pulse oximeter.

Erratum: Martín-Montero et al. Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea. 2021, , 1016.

The author wishes to make the following correction to this paper [...

Impact of atmospheric nitrogen dioxide on emergency department visits for asthma in a low air pollution area: a time series and case-crossover study.

Objectives: To analyze the association between atmospheric levels of nitrogen dioxide (NO2) and the number of visits by adults to an emergency department (ED) for exacerbated asthma in an urban area with low levels of air pollution.

Material And Methods: Retrospective ecological time-series study. We quantified ED visits for asthma by consecutive patients over the age of 14 years between 2010 and 2018 (3287 days).

Pediatric Sleep Apnea: The Overnight Electroencephalogram as a Phenotypic Biomarker.

Pediatric obstructive sleep apnea (OSA) is a prevalent disorder that disrupts sleep and is associated with neurocognitive and behavioral negative consequences, potentially hampering the development of children for years. However, its relationships with sleep electroencephalogram (EEG) have been scarcely investigated. Here, our main objective was to characterize the overnight EEG of OSA-affected children and its putative relationships with polysomnographic measures and cognitive functions.

Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults.

Overnight pulse oximetry has shown usefulness to simplify obstructive sleep apnea (OSA) diagnosis when combined with machine-learning approaches. However, the development and evaluation of a single model with ability to reach high diagnostic performance in both community-based non-referral and clinical referral cohorts are still pending. Since ensemble-learning algorithms are known for their generalization ability, we propose a least-squares boosting (LSBoost) model aimed at estimating the apnea-hypopnea index (AHI), as the correlate clinical measure of disease severity.

Heart rate variability as a potential biomarker of pediatric obstructive sleep apnea resolution.

Study Objectives: Pediatric obstructive sleep apnea (OSA) affects cardiac autonomic regulation, altering heart rate variability (HRV). Although changes in classical HRV parameters occur after OSA treatment, they have not been evaluated as reporters of OSA resolution. Specific frequency bands (named BW1, BW2, and BWRes) have been recently identified in OSA.

Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea.

Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA.

Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta-analysis.

Background: Machine-learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice.

Objective: To assess the reliability of machine-learning-based methods to detect pediatric OSA.

Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers.

Chronic obstructive pulmonary disease (COPD) is a highly prevalent chronic condition. COPD is a major cause of morbidity, mortality and healthcare costs globally. Spirometry is the gold standard test for a definitive diagnosis and severity grading of COPD.

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children.

This study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: () to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, () to evaluate its diagnostic utility, and () to assess its complementarity with the 3% oxygen desaturation index (3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: () DWT-derived feature extraction, () feature selection, and () pattern recognition.

A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea.

This study aims at assessing the usefulness of deep learning to enhance the diagnostic ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea (OSA). A total of 3196 blood oxygen saturation (SpO) signals from children were used for this purpose. A convolutional neural network (CNN) architecture was trained using 20-min SpO segments from the training set (859 subjects) to estimate the number of apneic events.

Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use.

Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis.

Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis.

Pediatric Obstructive Sleep Apnea (OSA) is a respiratory disease whose diagnosis is performed through overnight polysomnography (PSG). Since it is a complex, time-consuming, expensive, and labor-intensive test, simpler alternatives are being intensively sought. In this study, bispectral analysis of overnight airflow (AF) signal is proposed as a potential approach to replace PSG when indicated.