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 (SpO2) from 429 childhood OSA patients were analyzed. A CNN-RNN architecture fed with PR and SpO2 signals was developed to automatically classify wake (W), non-Rapid Eye Movement (NREM), and REM sleep stages. This architecture was composed of: (i) a convolutional neural network (CNN), which learns stage-related features from raw PR and SpO2 data; and (ii) a recurrent neural network (RNN), which models the temporal distribution of the sleep stages. The proposed CNN-RNN model showed a high performance for the automated detection of W/NREM/REM sleep stages (86.0% accuracy and 0.743 Cohen's kappa). Furthermore, the total sleep time estimated for each children using the CNN-RNN model showed high agreement with the manually derived from PSG (intra-class correlation coefficient = 0.747). These results were superior to previous works using CNN-based deep-learning models for automatic sleep staging in pediatric OSA patients from pulse oximetry signals. Therefore, the combination of CNN and RNN allows to obtain additional information from raw PR and SpO2 data related to sleep stages, thus being useful to automatically score sleep stages in pulse oximetry tests for children evaluated for suspected OSA.Clinical Relevance-This research establishes the usefulness of a CNN-RNN architecture to automatically score sleep stages in pulse oximetry tests for pediatric OSA diagnosis.

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). Three distinct deep-learning architectures were explored to automatically classify sleep stages from a single-channel EEG data. Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable artificial intelligence (XAI) algorithm, was then applied to provide an interpretation of the singular EEG patterns contributing to each predicted sleep stage. Among the tested architectures, a standard convolutional neural network (CNN) demonstrated the highest performance for automated sleep stage detection in the CHAT test set (accuracy = 86.9% and five-class kappa = 0.827). Furthermore, the CNN-based estimation of total sleep time exhibited strong agreement in the clinical dataset (intra-class correlation coefficient = 0.772). Our XAI approach using Grad-CAM effectively highlighted the EEG features associated with each sleep stage, emphasizing their influence on the CNN's decision-making process in both datasets. Grad-CAM heatmaps also allowed to identify and analyze epochs within a recording with a highly likelihood to be misclassified, revealing mixed features from different sleep stages within these epochs. Finally, Grad-CAM heatmaps unveiled novel features contributing to sleep scoring using a single EEG channel. Consequently, integrating an explainable CNN-based deep-learning model in the clinical environment could enable automatic sleep staging in pediatric sleep apnea tests.

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. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark.

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. Concerning cardiorespiratory comorbidities, several studies have assessed the usefulness of simplified screening tests for OSA in patients with hypertension, COPD, heart failure, atrial fibrillation, stroke, morbid obesity, and in hospitalized elders.The key question is whether there is any benefit in the screening for the existence of OSA in patients with comorbidities. In this regard, there are few studies evaluating the performance of the various diagnostic procedures in patients at high risk for OSA. The purpose of this chapter is to review the existing literature about diagnosis in those diseases with a high risk for OSA, with special reference to artificial intelligence-related methods.

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. In addition, a range of results from selected studies are presented as examples of these methods. Very high diagnostic performances are reported in these results regardless of the approaches taken. This leads us to conclude that conventional machine learning methods are useful techniques to develop new OSA diagnosis simplification proposals and to act as benchmark for other more recent methods such as deep learning.

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 (SpO2) signal is characterized by a near-constant profile in healthy subjects breathing normally, while marked drops (desaturations) are linked to respiratory events. Parameterization of the desaturations has led to a great number of indices of severity assessment commonly used to assist in OSA diagnosis. In this chapter, the main methodologies used to characterize the overnight oximetry profile are reviewed, from visual inspection and simple statistics to complex measures involving signal processing and pattern recognition techniques. We focus on the individual performance of each approach, but also on the complementarity among the great amount of indices existing in the state of the art, looking for the most relevant oximetric feature subset. Finally, a quick overview of SpO2-based deep learning applications for OSA management is carried out, where the raw oximetry signal is analyzed without previous parameterization. Our research allows us to conclude that all the methodologies (conventional, time, frequency, nonlinear, and hypoxemia-based) demonstrate high ability to provide relevant oximetric indices, but only a reduced set provide non-redundant complementary information leading to a significant performance increase. Finally, although oximetry is a robust tool, greater standardization and prospective validation of the measures derived from complex signal processing techniques are still needed to homogenize interpretation and increase generalizability.

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. Then, we divided the cohort in three OSA severity groups (no OSA, mild, and moderate/severe) to characterize the corresponding SO using the spectral maximum in the slow wave sleep (SWS) band δ1: 0.1-2 Hz (Maxs o), as well as the frequency where this maximum is located (FreqMaxso). Spectral entropy (SpecEn) from δ1 was also included in the analyses. A correlation analysis was performed to evaluate associations of these spectral measures with six OSA-related clinical variables and six cognitive scores. Our results indicate that Maxso could be used as a moderate/severe OSA biomarker while providing useful information regarding verbal and visuo-spatial impairments, and that FreqMaxso emerges as an even more robust indicator of visuospatial function. In addition, we uncovered novel insights regarding the ability of SpecEn in δ1 to characterize OSA-related disruption of sleep homeostasis. Our findings suggest that the information from SO may be useful to automatically characterize moderate/severe pediatric OSA and its cognitive consequences. Clinical Relevance- This study contributes towards reaching an objective quantifiable and automated assessment of the potential cognitive consequences of pediatric sleep apnea.

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 (SpO2) 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. The aim of this study was to evaluate a convolutional neural network (CNN) architecture based on these two signals to estimate the severity of pediatric OSA. PSG-derived AF and SpO2 signals from the Childhood Adenotonsillectomy Trial (CHAT) database (1638 recordings), as well as from a clinical database (974 recordings), were analyzed. A 2D CNN fed with AF and SpO2 signals was implemented to estimate the number of apneic events, and the total apnea-hypopnea index (AHI) was estimated. A training-validation-test strategy was used to train the CNN, adjust the hyperparameters, and assess the diagnostic ability of the algorithm, respectively. Classification into four OSA severity levels (no OSA, mild, moderate, or severe) reached 4-class accuracy and Cohen's Kappa of 72.55% and 0.6011 in the CHAT test set, and 61.79% and 0.4469 in the clinical dataset, respectively. Binary classification accuracy using AHI cutoffs 1, 5 and 10 events/h ranged between 84.64% and 94.44% in CHAT, and 84.10%-90.26% in the clinical database. The proposed CNN-based architecture achieved high diagnostic ability in two independent databases, outperforming previous approaches that employed SpO2 signals alone, or other classical feature-engineering approaches. Therefore, analysis of AF and SpO2 signals using deep learning can be useful to deploy reliable computer-aided diagnostic tools for childhood OSA.

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). The impact of N-antigenemia on 90-day mortality was assessed by multivariable Cox regression analysis. RESULTS: Prevalence of N-antigenemia at hospitalization was higher in nonsurvivors (69% (82/118) vs. 52% (250/482); p < 0.001). The patients with N-antigenemia showed more frequently RNAemia (45.7% (148/324) vs. 19.8% (51/257); p < 0.001), absence of anti-SARS-CoV-2 N antibodies (80.7% (264/327) vs. 26.6% (69/259); p < 0.001) and absence of S1 antibodies (73.4% (240/327) vs. 23.6% (61/259); p < 0.001). The patients with antigenemia showed more frequently acute respiratory distress syndrome (30.1% (100/332) vs. 18.7% (50/268); p = 0.001) and nosocomial infections (13.6% (45/331) vs. 7.9% (21/267); p = 0.026). N-antigenemia was a risk factor for increased 90-day mortality in the multivariable analysis (HR, 1.99 (95% CI,1.09-3.61), whereas the presence of anti-SARS-CoV-2 N-antibodies represented a protective factor (HR, 0.47 (95% CI, 0.26-0.85). DISCUSSION: The presence of N-antigenemia or the absence of anti-SARS-CoV-2 N-antibodies after hospitalization is associated to increased 90-day mortality in unvaccinated COVID-19 patients. Detection of N-antigenemia by using lateral flow tests is a quick, widely available tool that could contribute to early identify those COVID-19 patients at risk of deterioration.

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. We hypothesized that changes with treatment in these spectral bands can reliably identify changes in OSA severity and reflect OSA resolution. METHODS: Four hundred and four OSA children (5-9.9 years) from the prospective Childhood Adenotonsillectomy Trial were included; 206 underwent early adenotonsillectomy (eAT), while 198 underwent watchful waiting with supportive care (WWSC). HRV changes from baseline to follow-up were computed for classical and OSA-related frequency bands. Causal mediation analysis was conducted to evaluate how treatment influences HRV through mediators such as OSA resolution and changes in disease severity. Disease resolution was initially assessed by considering only obstructive events, and was followed by adding central apneas to the analyses. RESULTS: Treatment, regardless of eAT or WWSC, affects HRV activity, mainly in the specific frequency band BW2 (0.028-0.074 Hz). Furthermore, only changes in BW2 were specifically attributable to all OSA resolution mediators. HRV activity in BW2 also showed statistically significant differences between resolved and non-resolved OSA. CONCLUSIONS: OSA treatment affects HRV activity in terms of change in severity and disease resolution, especially in OSA-related BW2 frequency band. This band allowed to differentiate HRV activity between children with and without resolution, so we propose BW2 as potential biomarker of pediatric OSA resolution. CLINICAL TRIAL REGISTRATION: Childhood Adenotonsillectomy Trial, NCT00560859, https://sleepdata.org/datasets/chat.

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. DATA SOURCES: Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. ELIGIBILITY CRITERIA: Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard. APPRAISAL AND SYNTHESIS METHODS: Pooled sensitivities and specificities were computed for three apnea-hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random-effect models were assumed. Summary receiver-operating characteristics (SROC) analyses were also conducted. Heterogeneity (I 2 ) was evaluated, and publication bias was corrected (trim and fill). RESULTS: Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.

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. In this study, we use the PPG signal along with convolutional neural networks (CNN), a deep-learning technique, for the automatic identification of the three main levels of sleep: wake (W), rapid eye movement (REM), and non-REM sleep. A database of 366 PPG recordings from pediatric OSA patients is involved in the study. A CNN architecture was trained using 30-s epochs from the PPG signal for three-stage sleep classification. This model showed a promising diagnostic performance in an independent test set, with 78.2% accuracy and 0.57 Cohen's kappa for W/NREM/REM classification. Furthermore, the percentage of time in wake stage obtained for each subject showed no statistically significant differences with the manually scored from PSG. These results were superior to the only state-of-the-art study focused on the analysis of the PPG signal in the automated detection of sleep stages in children suffering from OSA. This suggests that CNN can be used along with PPG recordings for sleep stages scoring in pediatric home sleep apnea tests.

Impact of atmospheric nitrogen dioxide on emergency department visits for asthma in a low air pollution area: a time series and case-crossover study. / Influencia del dióxido de nitrógeno ambiental en las consultas a urgencias por asma en un entorno con baja contaminación: análisis de series temporales y casos cruzados.

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). The association between the mean atmospheric concentration of NO2 and the number of daily visits to the ED for asthma was analyzed with generalized linear regression analysis (Poisson modeling). The impact of exposure on individual risk was assessed by crossover analysis of case periods. We adjusted for confounding meteorologic variables, potential variability due to seasonal changes was corrected by trend analysis, and 3 time lags were assessed (0, 1, and 3 days). RESULTS: We analyzed 2527 asthma emergencies in 1588 patients (70% female) with a mean (SD) age of 51 (21) years. A significant positive association (relative risk, 1.056, 95% CI, 1.006-1.108; P .05) between atmospheric NO2 concentration and greater risk of visiting an ED within 3 days was detected. An increase of 10 µg/m3 of NO2 accounted for 5.3% of the visits (attributable fraction, 5.30, 95% CI, 0.60-9.75; P .05). CONCLUSION: In an urban area with low pollution levels, an elevation in atmospheric NO2 is associated with more hospital ED visits for asthma attacks in adults.

OBJETIVO: Analizar la asociación entre los niveles ambientales de dióxido de nitrógeno (NO2) y el número de consultas a urgencias por un episodio de agudización de asma bronquial en la población adulta de un entorno urbano con bajos niveles de contaminación. METODO: Estudio ecológico retrospectivo de series temporales. Se consideraron las visitas por asma de pacientes mayores de 14 años que acudieron a un servicio de urgencias de forma consecutiva entre 2010 y 2018 (3.287 días). La asociación entre la concentración media de NO2 y el número diario de visitas a urgencias por asma se estudió mediante un modelo lineal generalizado con regresión de Poisson. Se evaluó el impacto en el riesgo individual mediante un análisis de casos cruzados. Se ajustó por las variables confusoras meteorológicas, se corrigió la estacionalidad mediante análisis de tendencias y se evaluaron tres lags temporales (0, 1 y 3 días). RESULTADOS: Se analizaron 2.527 urgencias por asma correspondientes a 1.588 pacientes (edad media 51 ± 21 años, 70% mujeres). Hubo una asociación positiva significativa (riesgo relativo: RR = 1,056, IC 95%: 1,006-1,108; p 0,05) entre la concentración de NO2 y un mayor riesgo de consulta a urgencias por asma a los 3 días. Un incremento de 10 µgr/m3 de NO2 explicó el 5,3% de las consultas (fracción atribuible: FA = 5,30, IC 95%: 0,60-9,75; p 0,05). CONCLUSIONES: El incremento de los niveles ambientales de NO2 se asocia con un mayor número de urgencias hospitalarias por exacerbación de asma en adultos en un entorno con baja contaminación.

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. A two-step analysis involving 294 children (176 controls, 57% males, age range: 5-9 years) was conducted for this purpose. First, the activity and irregularity of overnight EEG spectrum were characterized in the typical frequency bands by means of relative spectral power and spectral entropy, respectively: δ1 (0.1-2 Hz), δ2 (2-4 Hz), θ (4-8 Hz), α (8-13 Hz), σ (10-16 Hz), ß1 (13-19 Hz), ß2 (19-30 Hz), and γ (30-70 Hz). Then, a correlation network analysis was conducted to evaluate relationships between them, six polysomnography variables (apnea-hypopnea index, respiratory arousal index, spontaneous arousal index, overnight minimum blood oxygen saturation, wake time after sleep onset, and sleep efficiency), and six cognitive scores (differential ability scales, Peabody picture vocabulary test, expressive vocabulary test, design copying, phonological processing, and tower test). We found that as the severity of the disease increases, OSA broadly affects sleep EEG to the point that the information from the different frequency bands becomes more similar, regardless of activity or irregularity. EEG activity and irregularity information from the most severely affected children were significantly associated with polysomnographic variables, which were coherent with both micro and macro sleep disruptions. We hypothesize that the EEG changes caused by OSA could be related to the occurrence of respiratory-related arousals, as well as thalamic inhibition in the slow oscillation generation due to increases in arousal levels aimed at recovery from respiratory events. Furthermore, relationships between sleep EEG and cognitive scores emerged regarding language, visual-spatial processing, and executive function with pronounced associations found with EEG irregularity in δ1 (Peabody picture vocabulary test and expressive vocabulary test maximum absolute correlations 0.61 and 0.54) and ß2 (phonological processing, 0.74; design copying, 0.65; and Tow 0.52). Our results show that overnight EEG informs both sleep alterations and cognitive effects of pediatric OSA. Moreover, EEG irregularity provides new information that complements and expands the classic EEG activity analysis. These findings lay the foundation for the use of sleep EEG to assess cognitive changes in pediatric 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. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0-13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0-0.04 Hz; low frequency: 0.04-0.15 Hz; and high frequency: 0.15-0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001-0.005 Hz; BW2: 0.028-0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.

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

Objective.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. However, a large proportion of individuals with COPD are undiagnosed and untreated. Given the high prevalence of COPD and its clinical importance, it is critical to develop new algorithms to identify undiagnosed COPD. This is particularly true in specific disease groups in which the presence of concomitant COPD increases overall morbidity/mortality such as those with sleep-disordered breathing. To our knowledge, no research has looked at the feasibility of automated COPD diagnosis using a data-driven analysis of the nocturnal continuous oximetry time series. We hypothesize that patients with COPD will exert certain patterns and/or dynamics of their overnight oximetry time series that are unique to this condition and that may be captured using a data-driven approach.Approach.We introduce a novel approach to nocturnal COPD diagnosis using 44 oximetry digital biomarkers and five demographic features and assess its performance in a population sample at risk of sleep-disordered breathing. A total ofn=350 unique patients' polysomnography (PSG) recordings were used. A random forest (RF) classifier was trained using these features and evaluated using nested cross-validation.Main results.The RF classifier obtainedF1 = 0.86 ± 0.02 and AUROC = 0.93 ± 0.02 on the test sets. A total of 8 COPD individuals out of 70 were misclassified. No severe cases (GOLD 3-4) were misdiagnosed. Including additional non-oximetry derived PSG biomarkers resulted in minimal performance increase.Significance.We demonstrated for the first time, the feasibility of COPD diagnosis from nocturnal oximetry time series for a population sample at risk of sleep-disordered breathing. We also highlighted what set of digital oximetry biomarkers best reflect how COPD manifests overnight. The results motivate that overnight single channel oximetry can be a valuable modality for COPD diagnosis, in a population sample at risk of sleep-disordered breathing. Further data is needed to validate this approach on other population samples.

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: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.