Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals.

The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea 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. 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.

Airflow Analysis in the Context of Sleep Apnea.

The airflow (AF) is a physiological signal involved in the overnight polysomnography (PSG) that reflects the respiratory activity. This signal is able to show the particularities of sleep apnea and is therefore used to define apneic events. In this regard, a growing number of studies have shown the usefulness of employing the overnight airflow as the only or combined information source for diagnosing sleep apnea in both children and adults. Due to its easy acquisition and interpretation, this biosignal has been widely analyzed by means of different signal processing techniques. In this chapter, we review the main methodological approaches applied to characterize and extract relevant information from this signal. In view of the results, we can conclude that the overnight airflow successfully reflects the particularities caused by the occurrence of apneic and hypopneic events and provides useful information for obtaining relevant biomarkers that characterize this disease.

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 [...].

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.

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.

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. Thus, our objective was to characterize AF through bispectrum, and assess its performance to diagnose pediatric OSA. This characterization was conducted using 13 bispectral features from 946 AF signals. The oxygen desaturation index ≥3% (ODI3), a common clinical measure of OSA severity, was also obtained to evaluate its complementarity to the AF bispectral analysis. The fast correlation-based filter (FCBF) and a multi-layer perceptron (MLP) were used for subsequent automatic feature selection and pattern recognition stages. FCBF selected 3 bispectral features and ODI3, which were used to train a MLP model with ability to estimate apnea-hypopnea index (AHI). The model reached 82.16%, 82.49%, and 90.15% accuracies for the common AHI cut-offs 1, 5, and 10 events/h, respectively. The different bispectral approaches used to characterize AF in children provided complementary information. Accordingly, bispectral analysis showed that the occurrence of apneic events decreases the non-gaussianity and non-linear interaction of the AF harmonic components, as well as the regularity of the respiratory patterns. Moreover, the bispectral information from AF also showed complementarity with ODI3. Our findings suggest that AF bispectral analysis may serve as a useful tool to simplify the diagnosis of pediatric OSA, particularly for children with moderate-to-severe OSA.

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.

Assessment of Nocturnal Autonomic Cardiac Imbalance in Positional Obstructive Sleep Apnea. A Multiscale Nonlinear Approach.

Positional obstructive sleep apnea (POSA) is a major phenotype of sleep apnea. Supine-predominant positional patients are frequently characterized by milder symptoms and less comorbidity due to a lower age, body mass index, and overall apnea-hypopnea index. However, the bradycardia-tachycardia pattern during apneic events is known to be more severe in the supine position, which could affect the cardiac regulation of positional patients. This study aims at characterizing nocturnal heart rate modulation in the presence of POSA in order to assess potential differences between positional and non-positional patients. Patients showing clinical symptoms of suffering from a sleep-related breathing disorder performed unsupervised portable polysomnography (PSG) and simultaneous nocturnal pulse oximetry (NPO) at home. Positional patients were identified according to the Amsterdam POSA classification (APOC) criteria. Pulse rate variability (PRV) recordings from the NPO readings were used to assess overnight cardiac modulation. Conventional cardiac indexes in the time and frequency domains were computed. Additionally, multiscale entropy (MSE) was used to investigate the nonlinear dynamics of the PRV recordings in POSA and non-POSA patients. A total of 129 patients (median age 56.0, interquartile range (IQR) 44.8-63.0 years, median body mass index (BMI) 27.7, IQR 26.0-31.3 kg/m2) were classified as POSA (37 APOC I, 77 APOC II, and 15 APOC III), while 104 subjects (median age 57.5, IQR 49.0-67.0 years, median BMI 29.8, IQR 26.6-34.7 kg/m2) comprised the non-POSA group. Overnight PRV recordings from positional patients showed significantly higher disorderliness than non-positional subjects in the smallest biological scales of the MSE profile (τ = 1: 0.25, IQR 0.20-0.31 vs. 0.22, IQR 0.18-0.27, p < 0.01) (τ = 2: 0.41, IQR 0.34-0.48 vs. 0.37, IQR 0.29-0.42, p < 0.01). According to our findings, nocturnal heart rate regulation is severely affected in POSA patients, suggesting increased cardiac imbalance due to predominant positional apneas.

A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow.

The most appropriate physiological signals to develop simplified as well as accurate screening tests for obstructive sleep apnoea (OSA) remain unknown. This study aimed at assessing whether joint analysis of at-home oximetry and airflow recordings by means of machine-learning algorithms leads to a significant diagnostic performance increase compared to single-channel approaches. Consecutive patients showing moderate-to-high clinical suspicion of OSA were involved. The apnoea-hypopnoea index (AHI) from unsupervised polysomnography was the gold standard. Oximetry and airflow from at-home polysomnography were parameterised by means of 38 time, frequency, and non-linear variables. Complementarity between both signals was exhaustively inspected via automated feature selection. Regression support vector machines were used to estimate the AHI from single-channel and dual-channel approaches. A total of 239 patients successfully completed at-home polysomnography. The optimum joint model reached 0.93 (95%CI 0.90-0.95) intra-class correlation coefficient between estimated and actual AHI. Overall performance of the dual-channel approach (kappa: 0.71; 4-class accuracy: 81.3%) significantly outperformed individual oximetry (kappa: 0.61; 4-class accuracy: 75.0%) and airflow (kappa: 0.42; 4-class accuracy: 61.5%). According to our findings, oximetry alone was able to reach notably high accuracy, particularly to confirm severe cases of the disease. Nevertheless, oximetry and airflow showed high complementarity leading to a remarkable performance increase compared to single-channel approaches. Consequently, their joint analysis via machine learning enables accurate abbreviated screening of OSA at home.

Characterization of the dynamic behavior of neural activity in Alzheimer's disease: exploring the non-stationarity and recurrence structure of EEG resting-state activity.

OBJECTIVE: Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to induce perturbations to normal neuronal behavior and disrupt neuronal networks. Recent work suggests that the dynamic properties of resting-state neuronal activity could be affected by MCI and AD-induced neurodegeneration. The aim of the study was to characterize these properties from different perspectives: (i) using the Kullback-Leibler divergence (KLD), a measure of non-stationarity derived from the continuous wavelet transform; and (ii) using the entropy of the recurrence point density ([Formula: see text]) and the median of the recurrence point density ([Formula: see text]), two novel metrics based on recurrence quantification analysis. APPROACH: KLD, [Formula: see text] and [Formula: see text] were computed for 49 patients with dementia due to AD, 66 patients with MCI due to AD and 43 cognitively healthy controls from 60 s electroencephalographic (EEG) recordings with a 10 s sliding window with no overlap. Afterwards, we tested whether the measures reflected alterations to normal neuronal activity induced by MCI and AD. MAIN RESULTS: Our results showed that frequency-dependent alterations to normal dynamic behavior can be found in patients with MCI and AD, both in non-stationarity and recurrence structure. Patients with MCI showed signs of patterns of abnormal state recurrence in the theta (4-8 Hz) and beta (13-30 Hz) frequency bands that became more marked in AD. Moreover, abnormal non-stationarity patterns were found in MCI patients, but not in patients with AD in delta (1-4 Hz), alpha (8-13 Hz), and gamma (30-70 Hz). SIGNIFICANCE: The alterations in normal levels of non-stationarity in patients with MCI suggest an initial increase in cortical activity during the development of AD. This increase could possibly be due to an impairment in neuronal inhibition that is not present during later stages. MCI and AD induce alterations to the recurrence structure of cortical activity, suggesting that normal state switching during rest may be affected by these pathologies.

Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis.

BACKGROUND AND OBJECTIVE: In-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). METHODS: 946 AF and blood oxygen saturation (SpO2) recordings from children ages 0-13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea-hypopnoea index (AHI). RESULTS: 8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. CONCLUSIONS: RP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.

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.

Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis.

The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI <; 1 events/h), mild (1 events/h ≤ AHI <; 5 events/h), moderate (5 events/h ≤ AHI <; 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09-0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value <; 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.

Characterization of EEG Resting-state Activity in Alzheimer's Disease by Means of Recurrence Plot Analyses.

The main objective of this study was to characterize EEG resting-state activity in 55 Alzheimer's disease (AD) patients and 29 healthy controls by means of TREND, a measure based on recurrence quantification analysis. TREND was computed from 60-second recordings of consecutive EEG activity, divided into non-overlapping windows of length 1, 2, 3, 5, 10, 15, 20 and 60 seconds. This measure was computed in the conventional EEG frequency bands (delta, theta, alpha, beta-1, beta-2 and gamma). The parameters delay (τ) and embedding dimension (m) were first optimized for every window size and frequency band under study. These embedding parameters proved to be frequency-dependent. Furthermore, 10 s epochs were set as the minimum length required to avoid spurious results. Statistically significant differences between both groups were found (p <; 0.05, Mann-Whitney U-test). The groups showed differences in TREND in the theta (4-8 Hz), beta1 (13-19 Hz) and beta-2 (19-30 Hz) frequency bands. Our results using TREND suggest that AD disrupts resting-state neural dynamics. Furthermore, these findings indicate that AD induces a frequency-dependent pattern of alterations in the non-stationarity levels of resting-state neural activity.

Influence of Chronic Obstructive Pulmonary Disease and Moderate-To-Severe Sleep Apnoea in Overnight Cardiac Autonomic Modulation: Time, Frequency and Non-Linear Analyses.

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent lung diseases worldwide. COPD patients show major dysfunction in cardiac autonomic modulation due to sustained hypoxaemia, which has been significantly related to higher risk of cardiovascular disease. Obstructive sleep apnoea syndrome (OSAS) is a frequent comorbidity in COPD patients. It has been found that patients suffering from both COPD and OSAS simultaneously, the so-called overlap syndrome, have notably higher morbidity and mortality. Heart rate variability (HRV) has demonstrated to be useful to assess changes in autonomic functioning in different clinical conditions. However, there is still little scientific evidence on the magnitude of changes in cardiovascular dynamics elicited by the combined effect of both respiratory diseases, particularly during sleep, when apnoeic events occur. In this regard, we hypothesised that a non-linear analysis is able to provide further insight into long-term dynamics of overnight cardiovascular modulation. Accordingly, this study is aimed at assessing the usefulness of sample entropy (SampEn) to distinguish changes in overnight pulse rate variability (PRV) recordings among three patient groups while sleeping: COPD, moderate-to-severe OSAS, and overlap syndrome. In order to achieve this goal, a population composed of 297 patients were studied: 22 with COPD alone, 213 showing moderate-to-severe OSAS, and 62 with COPD and moderate-to-severe OSAS simultaneously (COPD+OSAS). Cardiovascular dynamics were analysed using pulse rate (PR) recordings from unattended pulse oximetry carried out at patients' home. Conventional time- and frequency- domain analyses were performed to characterise sympathetic and parasympathetic activation of the nervous system, while SampEn was applied to quantify long-term changes in irregularity. Our analyses revealed that overnight PRV recordings from COPD+OSAS patients were significantly more irregular (higher SampEn) than those from patients with COPD alone (0.267 [0.210-0.407] vs. 0.212 [0.151-0.267]; p < 0.05) due to recurrent apnoeic events during the night. Similarly, COPD + OSAS patients also showed significantly higher irregularity in PRV during the night than subjects with OSAS alone (0.267 [0.210-0.407] vs. 0.241 [0.189-0.325]; p = 0.05), which suggests that the cumulative effect of both diseases increases disorganization of pulse rate while sleeping. On the other hand, no statistical significant differences were found between COPD and COPD + OSAS patients when traditional frequency bands (LF and HF) were analysed. We conclude that SampEn is able to properly quantify changes in overnight cardiovascular dynamics of patients with overlap syndrome, which could be useful to assess cardiovascular impairment in COPD patients due to the presence of concomitant OSAS.

Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome.

BACKGROUND: The gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary. OBJECTIVE: The aim of this study is twofold: (i) to analyze the blood oxygen saturation (SpO2) signal from nocturnal oximetry by means of features from the wavelet transform in order to characterize pediatric SAHS; (ii) to evaluate the usefulness of the extracted features to assist in the detection of pediatric SAHS. METHODS: 981 SpO2 signals from children ranging 2-13 years of age were used. Discrete wavelet transform (DWT) was employed due to its suitability to deal with non-stationary signals as well as the ability to analyze the SAHS-related low frequency components of the SpO2 signal with high resolution. In addition, 3% oxygen desaturation index (ODI3), statistical moments and power spectral density (PSD) features were computed. Fast correlation-based filter was applied to select a feature subset. This subset fed three classifiers (logistic regression, support vector machines (SVM), and multilayer perceptron) trained to determine the presence of moderate-to-severe pediatric SAHS (apnea-hypopnea index cutoff ≥ 5 events per hour). RESULTS: The wavelet entropy and features computed in the D9 detail level of the DWT reached significant differences associated with the presence of SAHS. All the proposed classifiers fed with a selected feature subset composed of ODI3, statistical moments, PSD, and DWT features outperformed every single feature. SVM reached the highest performance. It achieved 84.0% accuracy (71.9% sensitivity, 91.1% specificity), outperforming state-of-the-art studies in the detection of moderate-to-severe SAHS using the SpO2 signal alone. CONCLUSION: Wavelet analysis could be a reliable tool to analyze the oximetry signal in order to assist in the automated detection of moderate-to-severe pediatric SAHS. Hence, pediatric subjects suffering from moderate-to-severe SAHS could benefit from an accurate simplified screening test only using the SpO2 signal.

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.

Improving the Diagnostic Ability of Oximetry Recordings in Pediatric Sleep Apnea-Hypopnea Syndrome by Means of Multi-Class AdaBoost.

Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may impose many negative effects on the health and development of children. Due to the drawbacks of overnight polysomnography (PSG), the gold standard diagnosis technique, automated analysis of nocturnal oximetry has emerged as a simplified alternative. In order to improve diagnosis ability of oximetry, we propose to evaluate the usefulness of AdaBoost, a classification boosting algorithm, in the context of pediatric SAHS. A database composed of 981 SpO2 recordings from pediatric subjects was used. For this purpose, a signal processing approach divided into two main stages was conducted: (i) feature extraction, where 3% oxygen desaturation index (ODI3), spectral, and nonlinear features were computed from the oximetry signal, and (ii) AdaBoost classification, where an AdaBoost.M2 model was trained with these features in order to determine the severity of pediatric SAHS according to the apnea-hypopnea index (AHI): AHI<1 events per hour (e/h), 1≤AHI<5 e/h, and AHI≥5 e/h. Our AdaBoost.M2 model achieved a Cohen's kappa of 0.474 in an independent test set in the 3-class classification task. In addition, high accuracies were obtained when using the AHI cutoffs for diagnosis of mild (AHI=1 e/h) and moderate-to-severe (AHI=5 e/h) SAHS: 80.9% and 82.9%, respectively. These results achieved slightly higher diagnostic accuracies than ODI3 as well as state-of-the-art studies. Therefore, AdaBoost could help to enhance the diagnostic ability of the oximetry signal to assess pediatric SAHS severity.

Detrended fluctuation analysis of the oximetry signal to assist in paediatric sleep apnoea-hypopnoea syndrome diagnosis.

OBJECTIVE: To evaluate whether detrended fluctuation analysis (DFA) provides information that improves the diagnostic ability of the oximetry signal in the diagnosis of paediatric sleep apnoea-hypopnoea syndrome (SAHS). APPROACH: A database composed of 981 blood oxygen saturation (SpO2) recordings in children was used to extract DFA-derived features in order to quantify the scaling behaviour and the fluctuations of the SpO2 signal. The 3% oxygen desaturation index (ODI3) was also computed for each subject. Fast correlation-based filter (FCBF) was then applied to select an optimum subset of relevant and non-redundant features. This subset fed a multi-layer perceptron (MLP) neural network to estimate the apnoea-hypopnoea index (AHI). MAIN RESULTS: ODI3 and four features from the DFA reached significant differences associated with the severity of SAHS. An optimum subset composed of the slope in the first scaling region of the DFA profile and the ODI3 was selected using FCBF applied to the training set (60% of samples). The MLP model trained with this feature subset showed good agreement with the actual AHI, reaching an intra-class correlation coefficient of 0.891 in the test set (40% of samples). Furthermore, the estimated AHI showed high diagnostic ability, reaching an accuracy of 82.7%, 81.9%, and 91.1% using three common AHI cut-offs of 1, 5, and 10 events per hour (e h-1), respectively. These results outperformed the overall performance of ODI3. SIGNIFICANCE: DFA may serve as a reliable tool to improve the diagnostic performance of oximetry recordings in the evaluation of paediatric patients with symptoms suggestive of SAHS.