Verónica Barroso-García

Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home

Untreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO2) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.

Multi-class adaboost to detect Sleep Apnea-Hypopnea Syndrome severity from oximetry recordings obtained at home

This paper aims at evaluating a novel multi-class methodology to establish Sleep Apnea-Hypopnea Syndrome (SAHS) severity by the use of single-channel at-home oximetry recordings. The study involved 320 participants derived to a specialized sleep unit due to SAHS suspicion. These were assigned to one out of the four SAHS severity degrees according to the apnea-hypopnea index (AHI): no-SAHS (AHI<;5 events/hour), mild-SAHS (5≤AHI<;15 e/h), moderate-SAHS (15≤AHI<;30 e/h), and severe-SAHS (AHI≥30 e/h). A set of statistical, spectral, and non-linear features were extracted from blood oxygen saturation (SpO2) signals to characterize SAHS. Then, an optimum set among these features were automatically selected based on relevancy and redundancy analyses. Finally, a multi-class AdaBoost model, built with the optimum set of features, was obtained from a training set (60%) and evaluated in an independent test set (40%). Our AdaBoost model reached 0.386 Cohens kappa in the four-class classification task. Additionally, it reached accuracies of 89.8%, 85.8%, and 74.8% when evaluating the AHI thresholds 5 e/h, 15 e/h, and 30 e/h, respectively, outperforming the classic oxygen desaturation index. Our results suggest that SpO2 obtained at home, along with multi-class AdaBoost, are useful to detect SAHS severity.

Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea

Sleep apnea-hypopnea syndrome (SAHS) is a chronic sleep-related breathing disorder, which is currently considered a major health problem. In-lab nocturnal polysomnography (NPSG) is the gold standard diagnostic technique though it is complex and relatively unavailable. On the other hand, the analysis of blood oxygen saturation (SpO2) from nocturnal pulse oximetry (NPO) is a simple, noninvasive, highly available and effective alternative. This study focused on the design and assessment of a neural network (NN) aimed at detecting SAHS using information from at-home unsupervised portable SpO2 recordings. A Bayesian multilayer perceptron NN (MLP-NN) was proposed, fed with complementary oximetric features properly selected. A dataset composed of 320 unattended SpO2 recordings was analyzed (60% for training and 40% for validation). The proposed Bayesian MLP-NN achieved 94.2% sensitivity, 69.6% specificity, and 89.8% accuracy in the test set. Our results suggest that automated analysis of at-home portable NPO recordings by means of Bayesian MLP-NN could be an effective and highly available technique in the context of SAHS diagnosis.

Pulse Rate Variability Analysis to Enhance Oximetry as at-Home Alternative for Sleep Apnea Diagnosing

This study focuses on the at-home Sleep apnea-hypopnea syndrome (SAHS) severity estimation. Three percent oxygen desaturation index \( (ODI_{3} ) \) from nocturnal pulse-oximetry has been commonly evaluated as simplified alternative to polysomnography (PSG), the standard in-hospital diagnostic test. However, \( ODI_{3} \) has shown limited ability to detect SAHS as it only sums up information from desaturation events. Other physiological signs of SAHS can be found in respiratory and cardiac signals, providing additional helpful data to establish SAHS and its severity. Pulse rate variability time series (PRV), also derived from nocturnal oximetry, is considered a surrogate for heart rate variability, which provides both cardiac and respiratory information. In this study, 200 oximetric recordings obtained at patients home were involved, divided into training (50%) and test (50%) groups. \( ODI_{3} \) and PRV were obtained from them, the latter being characterized by the extraction of statistical features in time domain, as well as the spectral entropy from the commonly used very low (0–0.04 Hz.), low (0.04–0.15 Hz.), and high (0.15–0.4 Hz.) frequency bands. The \( ODI_{3} \) and PRV features were joined in a multi-layer perceptron artificial neural network (MLP), trained to estimate the apnea-hypopnea index (AHI), which is the PSG-derived parameter used to diagnose SAHS. Our results showed that single \( ODI_{3} \) rightly assigned 62.0% of the subjects from the test group into one out the four SAHS severity degrees, reaching 0.470 Cohens kappa, and 0.840 intra-class correlation coefficient (ICC) with the actual AHI (accuracies of 90.0, 88.0 and 82.0% in the increasing AHI cutoffs used to define SAHS severity). By contrast, our MLP model rightly assigned 75.0% of the subjects into their corresponding SAHS severity level, reaching 0.614 \( \kappa \) and 0.904 ICC (accuracies of 93.0, 88.0 and 90.0%). These results suggest that SAHS diagnosis could be accurately conducted at-patients home by combining \( ODI_{3} \) and PRV from nocturnal oximetry

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.

Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea

OBJECTIVE This study is aimed at assessing symbolic dynamics as a reliable technique to characterise complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). APPROACH Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ≥5 events/h from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. MAIN RESULTS The histogram of 3-symbol words from symbolic dynamics showed significant differences (p <0.01) between children with AHI <5 events/h and moderate-to-severe patients (AHI ≥5 events/h). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p-value <0.01) outperforming the other models. SIGNIFICANCE Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry.

Usefulness of discrete wavelet transform in the analysis of oximetry signals to assist in childhood sleep apnea-hypopnea syndrome diagnosis

Sleep apnea hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may cause many negative consequences for the health and development of children. The gold standard for diagnosis is the overnight polysomnography (PSG), which is a high cost, complex, intrusive, and time-demanding technique. To improve the early detection of pediatric SAHS, we propose an automated analysis of the SpO2 signal from nocturnal oximetry. A database composed of 298 SpO2 recordings from children ranging from 0 to 13 years old was used for this purpose. Due to the abrupt changes caused by respiratory events in the SpO2 signal, our goal was to evaluate the diagnostic ability of this by means of the discrete wavelet transform (DWT). To achieve this objective, we conducted a signal processing approach divided into two main stages: (i) feature extraction, where features from the DWT detail coefficients were computed, and (ii) feature classification, where a logistic regression (LR) model was used to classify children into SAHS negative or SAHS positive. Our results showed that respiratory events introduced more variability in two detail levels of the DWT from SpO2: 0.024–0.049 Hz and 0.012–0.024 Hz. Moreover, the LR classifier achieved an 81.9% accuracy (79.1% sensitivity and 84.1% specificity) in an independent test set for a clinical cutoff point of 5 events/h, as derived from PSG. These results suggest that DWT analysis may be a useful tool to analyze SpO2 recordings in the context of childhood SAHS.

A Bayesian neural network approach to compare the spectral information from nasal pressure and thermistor airflow in the automatic sleep apnea severity estimation

In the sleep apnea-hypopnea syndrome (SAHS) context, airflow signal plays a key role for the simplification of the diagnostic process. It is measured during the standard diagnostic test by the acquisition of two simultaneous sensors: a nasal prong pressure (NPP) and a thermistor (TH). The current study focuses on the comparison of their spectral content to help in the automatic SAHS-severity estimation. The spectral analysis of 315 NPP and corresponding TH recordings is firstly proposed to characterize the conventional band of interest for SAHS (0.025–0.050 Hz.). A magnitude squared coherence analysis is also conducted to quantify possible differences in the frequency components of airflow from both sensors. Then, a feature selection stage is implemented to assess the relevance and redundancy of the information extracted from the spectrum of NPP and TH airflow. Finally, a multiclass Bayesian multi-layer perceptron (BY-MLP) was used to perform an automatic estimation of SAHS severity (no-SAHS, mild, moderate, and severe), by the use of the selected spectral features from: airflow NPP alone, airflow TH alone, and both sensors jointly. The highest diagnostic performance was reached by BY-MLP only trained with NPP spectral features, reaching Cohens к = 0.498 in the overall four-class classification task. It also achieved 91.3%, 84.9%, and 83.3% of accuracy in the binary evaluation of the 3 apnea-hypopnea index cut-offs (5, 15, and 30 events/hour) that define the four SAHS degrees. Our results suggest that TH sensor might be not necessary for SAHS severity estimation if an automatic comprehensive characterization approach is adopted to simplify the diagnostic process.

Irregularity and Variability Analysis of Airflow Recordings to Facilitate the Diagnosis of Paediatric Sleep Apnoea-Hypopnoea Syndrome

The aim of this paper is to evaluate the evolution of irregularity and variability of airflow (AF) signals as sleep apnoea-hypopnoea syndrome (SAHS) severity increases in children. We analyzed 501 AF recordings from children 6.2 ± 3.4 years old. The respiratory rate variability (RRV) signal, which is obtained from AF, was also estimated. The proposed methodology consisted of three phases: (i) extraction of spectral entropy (SE1), quadratic spectral entropy (SE2), cubic spectral entropy (SE3), and central tendency measure (CTM) to quantify irregularity and variability of AF and RRV; (ii) feature selection with forward stepwise logistic regression (FSLR), and (iii) classification of subjects using logistic regression (LR). SE1, SE2, SE3, and CTM were used to conduct exploratory analyses that showed increasing irregularity and decreasing variability in AF, and increasing variability in RRV as apnoea-hypopnoea index (AHI) was higher. These tendencies were clearer in children with a higher severity degree (from AHI ≥ 5 events/hour). Binary LR models achieved 60%, 76%, and 80% accuracy for the AHI cutoff points 1, 5, and 10 e/h, respectively. These results suggest that irregularity and variability measures are able to characterize paediatric SAHS in AF recordings. Hence, the use of these approaches could be helpful in automatically detecting SAHS in children.