Heart rate variability spectrum characteristics in children with sleep apnea

Diagnostic accuracy of portable sleep monitors in pediatric sleep apnea: A systematic review

In recent years, a plethora of new type III and IV portable sleep monitors (PSM) have been developed, although evidence regarding their diagnostic accuracy for use in children remains heterogeneous. This study systematically reviews the literature addressing the diagnostic accuracies of type III and IV PSM for pediatric sleep apnea. Publications indexed in Medline, Embase, or Web of Science were reviewed using the PRISMA framework. Of 1054 studies, 62 fulfilled the inclusion criteria. Of the studies evaluating oximetry-based type IV PSM, one (6.25 %) demonstrated a balanced set of high (≥80 %) sensitivities and specificities for the diagnosis of any pediatric sleep apnea, while five studies (27.8 %) showed similar accuracies for moderate-to-severe sleep apnea. For non-oximetry-based type IV PSM, two studies (40 %) reported a balanced set of high diagnostic accuracies for moderate-to-severe sleep apnea. Type III PSM repeatedly demonstrated higher diagnostic accuracies, with six studies (66.7 %) reporting a balanced set of high diagnostic accuracies for moderate-to-severe sleep apnea. This review highlights the potential of type III PSM to detect moderate-to-severe pediatric sleep apnea, although current evidence is limited to support the stand-alone use of type IV PSM for the diagnosis of sleep apnea in most children.

Heart rate variability in school-age children born moderate-to-late preterm

BACKGROUND

Prematurity is associated with reduced cardiac autonomic function. This study aimed to investigate the heart rate variability (HRV) in school-age children born moderately to late preterm (MLPT).

METHODS

This cross-sectional study investigated school-age children, aged 5 to 10 years, born moderate-to-late preterm. Electrocardiograms recordings were performed during fifteen-minutes. Time and frequency domain parameters were calculated, corrected for heart rate and compared between the groups.

RESULTS

A total of 123 children were evaluated and 119 were included in this study. HRV measures, studied in the time and frequency domains, were similar in both groups. Corrected values of root mean square of successive differences between normal cycles (RMSSD), percentage of successive cycles with a duration difference >50 ms (pNN50%), and high frequency (HF), indices that predominantly represent the parasympathetic activity of the autonomic nervous system, were 1.6E-7 and 1.8E-7 (p=0.226); 1.6E-13 and 1.6E-13 (p=0.506); 6.9E-12 and 7.4E-12 (p=0.968) in the preterm and control groups, respectively.

CONCLUSION

This study did not find differences in heart rate variability between school-age children born MLPT and those born at term, suggesting that plasticity of cardiac autonomic modulation continues to occur in children up to school age or there is less impairment of the autonomic system in MLPT.

ECG-based convolutional neural network in pediatric obstructive sleep apnea diagnosis

Obstructive sleep apnea (OSA) is a prevalent respiratory condition in children and is characterized by partial or complete obstruction of the upper airway during sleep. The respiratory events in OSA induce transient alterations of the cardiovascular system that ultimately can lead to increased cardiovascular risk in affected children. Therefore, a timely and accurate diagnosis is of utmost importance. However, polysomnography (PSG), the standard diagnostic test for pediatric OSA, is complex, uncomfortable, costly, and relatively inaccessible, particularly in low-resource environments, thereby resulting in substantial underdiagnosis. Here, we propose a novel deep-learning approach to simplify the diagnosis of pediatric OSA using raw electrocardiogram tracing (ECG). Specifically, a new convolutional neural network (CNN)-based regression model was implemented to automatically predict pediatric OSA by estimating its severity based on the apnea-hypopnea index (AHI) and deriving 4 OSA severity categories. For this purpose, overnight ECGs from 1,610 PSG recordings obtained from the Childhood Adenotonsillectomy Trial (CHAT) database were used. The database was randomly divided into approximately 60%, 20%, and 20% for training, validation, and testing, respectively. The diagnostic performance of the proposed CNN model largely outperformed the most accurate previous algorithms that relied on ECG-derived features (4-class Cohen's kappa coefficient of 0.373 versus 0.166). Specifically, for AHI cutoff values of 1, 5, and 10 events/hour, the binary classification achieved sensitivities of 84.19%, 76.67%, and 53.66%; specificities of 46.15%, 91.39%, and 98.06%; and accuracies of 75.92%, 86.96%, and 91.97%, respectively. Therefore, pediatric OSA can be readily identified by our proposed CNN model, which provides a simpler, faster, and more accessible diagnostic test that can be implemented in clinical practice.

Restless Legs Syndrome and Restless Sleep Disorder in Children

Restless legs syndrome (RLS) affects 2% of children presenting with symptoms of insomnia, restless sleep, decreased quality of life, and effects on cognition and behavior. The International RLS Study Group and the American Academy of Sleep Medicine have published guidelines for the diagnosis and treatment of RLS in children. Restless sleep disorder has been recently identified in children and presents with frequent movements during sleep and daytime symptoms with polysomnography findings of at least 5 large muscle movements at night. Treatment options for both disorders include iron supplementation, either oral or intravenous with improvement in nighttime and daytime symptoms.

Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability

Heart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-min segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1-5 e/s; 5-10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA.

Association between Short-Term Exposure to Ozone and Heart Rate Variability: A Systematic Review and Meta-Analysis

At present, ambient air pollution poses a significant threat to patients with cardiovascular disease (CVD). The heart rate variability (HRV) is a marker of the cardiac autonomic nervous system, and it is related to air pollution and cardiovascular disease. There is, however, considerable disagreement in the literature regarding the association between ozone (O₃) and HRV. To further investigate the effects of short-term exposure to O₃ on HRV, we conducted the first meta-analysis of relevant studies. The percentage change of HRV indicator(s) is the effect estimate extracted for the quantitative analysis in this study. In our meta-analysis, per 10 ppb increase in O₃ was significantly associated with decreases in the time-domain measurements, for standard deviation of the normal-to-normal (NN) interval (SDNN) -1.11% (95%CI: -1.35%, -0.87%) and for root mean square of successive differences (RMSSD) -3.26% (95%CI: -5.42%, -1.09%); in the frequency-domain measurements, for high frequency (HF) -3.01% (95%CI: -4.66%, -1.35%) and for low frequency (LF) -2.14% (95%CI: -3.83%, -0.45%). This study showed short-term exposure to O₃ was associated with reduced HRV indicators in adults, which suggested that the cardiac autonomic nervous system might be affected after O₃ exposure, contributing to the association between O₃ exposure and CVD risk.

Pierre Robin and breathing: What to do and when?

Robin sequence is characterized by mandibular retrognathia, airway obstruction, and glossoptosis; 80%-90% also have a cleft palate. Various treatment approaches exist, and although controlled studies are rare, objective assessment of treatment outcomes that address the leading clinical issues, namely obstructive sleep apnea and failure to thrive, are essential. Sleep-disordered breathing may be detected using cardiorespiratory polygraphy or polysomnography. Pulse oximetry alone may miss infants with frequent obstructive apneas, yet no intermittent hypoxia. Among conservative treatment options, the Tubingen Palatal Plate with a velar extension shifting the tongue base forward is the only approach that corrects the underlying anatomy and that has undergone appropriate evaluation. Of the surgical treatment options, which are not necessarily the first line of therapy, mandibular distraction osteogenesis (MDO) is effective and has been most extensively adopted. Notwithstanding, it is puzzling that MDO is frequently used in some countries, yet hardly ever in others, despite similar tracheostomy rates. Thus, prospective multicenter studies with side-by-side comparisons aimed at identifying an optimal treatment paradigm for this potentially life-threatening condition are urgently needed.

Restless Sleep Disorder (RSD): a New Sleep Disorder in Children. A Rapid Review

PURPOSE OF REVIEW

Restless sleep disorder (RSD) is a recently identified pediatric sleep disorder characterized by frequent movements during sleep associated with daytime symptoms. In this review we summarize the expanding evidence of the clinical presentation of RSD, potential pathophysiology, associated comorbidities, and current treatment options that will help the pediatrician identify children with RSD in a timely manner.

RECENT FINDINGS

RSD is diagnosed in 7.7% of children referred evaluated in a pediatric sleep center. Children with RSD present with frequent nightly movements during sleep for at least 3 months, and have daytime symptoms related to poor sleep quality including excessive sleepiness, hyperactivity, irritability among other symptoms. Current evidence shows an increased sympathetic predominance, increased NREM sleep instability, and iron deficiency, as well as increased prevalence in parasomnias and attention deficit hyperactivity disorder. Consensus diagnostic criteria were recently published to diagnose RSD and emergent evidence suggests that iron supplementation improves its nighttime and daytime symptoms.

Secondary Analyses of the Childhood Adenotonsillectomy Trial: A Narrative Review

Importance

Adenotonsillectomy, performed for approximately 500 000 children annually in the US alone, is the first line of treatment of pediatric obstructive sleep apnea (OSA). The Childhood Adenotonsillectomy Trial (CHAT), the first randomized clinical trial to test the efficacy of adenotonsillectomy, compared the management of pediatric nonsevere OSA by early adenotonsillectomy (eAT) vs watchful waiting with supportive care. Since the publication of the primary article in 2013, the CHAT study data set were made available via the National Sleep Research Resource, which allowed researchers to address a range of additional clinical questions relevant to the care of children with OSA. This review focuses on secondary analyses associated with the CHAT data set as grouped by the outcome of interest.

Observations

The results of most secondary analyses suggest that children who underwent eAT experienced the greatest improvements in symptom burden, sleepiness, parent-reported behavior, and quality of life. Changes in other domains, such as cognition, cardiovascular physiology, and metabolic indicators, were modest and selective. The associations between most treatment outcomes and polysomnographic parameters were weak. Symptoms were poor predictors of OSA severity. The results from these secondary analyses benefitted from the rigor of multicenter design and centralized polysomnography interpretation in CHAT. However, the exclusion of younger preschool-aged children and children with primary snoring limited the generalizability of findings. In addition, because caregivers were not masked, some of the parent-reported outcomes may have been inflated.

Conclusions and Relevance

The results of this narrative review suggest that CHAT provides a model for future OSA-related studies in children for design, conduct, and subsequent reuse of the study data set, and its findings have advanced our understanding of the pathophysiology and management of pediatric nonsevere OSA. Directions for future research include whether the findings from this landmark study are generalizable to younger children and children with primary snoring and severe OSA. Similar studies may help address practice variability associated with pediatric OSA and help identify children who are most likely to benefit from undergoing eAT.

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.

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

404 OSA children (5-9.9 years) from the prospective Childhood Adenotonsillectomy Trial (CHAT) 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.

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.