Erratum: Martín-Montero et al. Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea. Entropy 2021, 23, 1016.

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

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 (SpO2) signals from children were used for this purpose. A convolutional neural network (CNN) architecture was trained using 20-min SpO2 segments from the training set (859 subjects) to estimate the number of apneic events. CNN hyperparameters were tuned using Bayesian optimization in the validation set (1402 subjects). This model was applied to three test sets composed of 312, 392, and 231 subjects from three independent databases, in which the apnea-hypopnea index (AHI) estimated for each subject (AHICNN) was obtained by aggregating the output of the CNN for each 20-min SpO2 segment. AHICNN outperformed the 3% oxygen desaturation index (ODI3), a clinical approach, as well as the AHI estimated by a conventional feature-engineering approach based on multi-layer perceptron (AHIMLP). Specifically, AHICNN reached higher four-class Cohen's kappa in the three test databases than ODI3 (0.515 vs 0.417, 0.422 vs 0.372, and 0.423 vs 0.369) and AHIMLP (0.515 vs 0.377, 0.422 vs 0.381, and 0.423 vs 0.306). In addition, our proposal outperformed state-of-the-art studies, particularly for the AHI severity cutoffs of 5 e/h and 10 e/h. This suggests that the information automatically learned from the SpO2 signal by deep-learning techniques helps to enhance the diagnostic ability of oximetry in the context of pediatric OSA.

Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry.

Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO2) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO2 raw data. CHAT-baseline dataset, composed of 453 SpO2 recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO2 signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.

Bispectral Analysis to Enhance Oximetry as a Simplified Alternative for Pediatric Sleep Apnea Diagnosis.

This study aims at assessing the bispectral analysis of blood oxygen saturation (SpO2) from nocturnal oximetry to help in pediatric sleep apnea-hypopnea syndrome (SAHS) diagnosis. Recent studies have found excessive redundancy in the SAHS-related information usually extracted from SpO2, while proposing only two features as a reduced set to be used. On the other hand, it has been suggested that SpO2 bispectral analysis is able to provide complementary information to common anthropometric, spectral, and clinical variables. We address these novel findings to assess whether bispectrum provides new non-redundant information to help in SAHS diagnosis. Thus, we use 981 pediatric SpO2 recordings to extract both the reduced set of features recently proposed as well as 9 bispectral features. Then, a feature selection method based on the fast correlationbased filter and bootstrapping is used to assess redundancy among all the features. Finally, the non-redundant ones are used to train a Bayesian multi-layer perceptron neural network (BYMLP) that estimate the apnea-hypopnea index (AHI), which is the diagnostic reference variable. Bispectral phase entropy was found complementary to the two previously recommended features and a BY-MLP model trained with the three of them reached high agreement with actual AHI (intra-class correlation coefficient = 0.889). Estimated AHI also showed high diagnostic ability, reaching 82.1%, 81.9%, and 90.3% accuracies and 0.814, 0.880, and 0.922 area under the receiver-operating characteristics curve for three common AHI thresholds: 1 e/h, 5 e/h, and 10 e/h, respectively. These results suggest that the information extracted from the bispectrum of SpO2 can improve the diagnostic performance of the oximetry test.

Association between obstructive sleep apnea and cancer incidence in a large multicenter Spanish cohort.

RATIONALE: Obstructive sleep apnea (OSA) has been associated with increased cancer mortality, but whether it is also associated with cancer incidence is unknown. OBJECTIVES: To investigate whether OSA is associated with increased cancer incidence in a large clinical cohort. METHODS: A multicenter, clinical cohort study including consecutive patients investigated for suspected OSA between 2003 and 2007 in seven Spanish teaching hospitals. Apnea-hypopnea index (AHI) and percent nighttime with oxygen saturation less than 90% (TSat(90)) were used as surrogates of OSA severity, both as continuous variables and categorized by tertiles. Cox proportional hazards regression analyses were used to calculate hazard ratio (HR) and 95% confidence interval (CI) for cancer incidence after adjusting for confounding variables. MEASUREMENTS AND MAIN RESULTS: A total of 4,910 patients were analyzed (median follow-up, 4.5 yr; interquartile range, 3.4-5.2). Compared with the lower TSat(90) category (<1.2%), the adjusted hazards (95% CI) of cancer incidence for increasing categories were 1.58 (1.07-2.34) for TSat(90) 1.2-12% and 2.33 (1.57-3.46) for TSat(90) greater than 12%. Continuous TSat(90) was also associated with cancer incidence (adjusted HR, 1.07 [1.02-1.13] per 10-unit increase in TSat(90)). In stratified analyses, TSat(90) was associated with cancer incidence in patients younger than 65 years (adjusted HR, 1.13 [95% CI, 1.06-1.21] per 10-unit increase in TSat(90)) and males (adjusted HR, 1.11 [95% CI, 1.04-1.17] per 10-unit increase in TSat(90)). AHI was not associated with cancer incidence in the adjusted analyses, except for patients younger than 65 years (adjusted HR for AHI >43 vs. <18.7, 1.66; 95% CI, 1.04-2.64). CONCLUSIONS: Increased overnight hypoxia as a surrogate of OSA severity was associated with increased cancer incidence. This association seems to be limited to men and patients younger than 65 years of age.