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

Martín-Montero, Adrián; Armañac-Julián, Pablo; Gil, Eduardo; Kheirandish‐Gozal, Leila; Álvarez, Daniel; Lázaro, Jesús; Bailón, Raquel; Gozal, David; Laguna, Pablo; Hornero, Roberto; Gutiérrez‐Tobal, Gonzalo C.

doi:10.1016/j.compbiomed.2023.106549

Cited by 11 publications

(5 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secondly, heart rate variability (HRV) has been recently described as an OSA-specific biomarker, with promising results in evaluating treatment efficacy in children [ 70 , 71 , 72 , 73 ]. HRV corresponds to variations in the heart rate (HR) or beat-beat time interval due to the modulation of the autonomic nervous system’s activity.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and Treatment of Sleep Apnea in Children: A Future Perspective Is Needed

Solano-Pérez,

Coso,

Castillo-García

et al. 2023

Biomedicines

View full text Add to dashboard Cite

Obstructive sleep apnea (OSA) in children is a prevalent, but still, today, underdiagnosed illness, which consists of repetitive episodes of upper airway obstruction during sleep with important repercussions for sleep quality. OSA has relevant consequences in the pediatric population, mainly in the metabolic, cardiovascular (CV), and neurological spheres. However, contrary to adults, advances in diagnostic and therapeutic management have been scarce in the last few years despite the increasing scientific evidence of the deleterious consequences of pediatric OSA. The problem of underdiagnosis and the lack of response to treatment in some groups make an update to the management of OSA in children necessary. Probably, the heterogeneity of OSA is not well represented by the classical clinical presentation and severity parameters (apnea/hypopnea index (AHI)), and new strategies are required. A specific and consensus definition should be established. Additionally, the role of simplified methods in the diagnosis algorithm should be considered. Finally, the search for new biomarkers for risk stratification is needed in this population. In conclusion, new paradigms based on personalized medicine should be implemented in this population.

show abstract

Section: Discussionmentioning

confidence: 99%

Diagnosis and Treatment of Sleep Apnea in Children: A Future Perspective Is Needed

Solano-Pérez,

Coso,

Castillo-García

et al. 2023

Biomedicines

View full text Add to dashboard Cite

show abstract

“…Obstructive sleep apnea has a detrimental impact on health sequelae such as cognitive behavioural deficits, growth retardation, and CVD, particularly in untreated childhood OSA (Beebe, 2006;Ehsan et al, 2017;Gozal, 2008). Abnormal autonomic control has been reported in children with OSA (Aljadeff et al, 1997;Liu et al, 2018;Martín-Montero et al, 2023;Teo & Mitchell, 2013). A crosssectional case-control study showed that compared to non-OSA controls, children with OSA showed a larger pharyngeal compliance associated with an increased AHI, possibly as consequence of decreased sympathetic tone (reduced LF and LF/HF) in NREM sleep but decreased parasympathetic tone (reduced HF) in REM sleep (Bokov et al, 2021).…”

Section: Upper Airway Surgical Proceduresmentioning

confidence: 99%

“…The circadian rhythm leads to cyclical changes in autonomic regulation during the NREM and REM sleep stages, which is independent of OSA (Ucak et al, 2021). Meanwhile, the REM stage seems to cover up the impact of OSA on HRV (Martín‐Montero et al, 2023). In addition, N3 sleep is commonly reduced or even absent in OSA.…”

Section: The Technology and Limitations Of Hrv Analysis In Osamentioning

confidence: 99%

Obstructive sleep apnea heterogeneity and autonomic function: a role for heart rate variability in therapy selection and efficacy monitoring

Qin,

Fietze,

Mazzotti

et al. 2023

Journal of Sleep Research

View full text Add to dashboard Cite

SUMMARYObstructive sleep apnea is a highly prevalent sleep‐related breathing disorder, resulting in a disturbed breathing pattern, changes in blood gases, abnormal autonomic regulation, metabolic fluctuation, poor neurocognitive performance, and increased cardiovascular risk. With broad inter‐individual differences recognised in risk factors, clinical symptoms, gene expression, physiological characteristics, and health outcomes, various obstructive sleep apnea subtypes have been identified. Therapeutic efficacy and its impact on outcomes, particularly for cardiovascular consequences, may also vary depending on these features in obstructive sleep apnea. A number of interventions such as positive airway pressure therapies, oral appliance, surgical treatment, and pharmaceutical options are available in clinical practice. Selecting an effective obstructive sleep apnea treatment and therapy is a challenging medical decision due to obstructive sleep apnea heterogeneity and numerous treatment modalities. Thus, an objective marker for clinical evaluation is warranted to estimate the treatment response in patients with obstructive sleep apnea. Currently, while the Apnea–Hypopnea Index is used for severity assessment of obstructive sleep apnea and still considered a major guide to diagnosis and managements of obstructive sleep apnea, the Apnea–Hypopnea Index is not a robust marker of symptoms, function, or outcome improvement. Abnormal cardiac autonomic modulation can provide additional insight to better understand obstructive sleep apnea phenotyping. Heart rate variability is a reliable neurocardiac tool to assess altered autonomic function and can also provide cardiovascular information in obstructive sleep apnea. Beyond the Apnea–Hypopnea Index, this review aims to discuss the role of heart rate variability as an indicator and predictor of therapeutic efficacy to different modalities in order to optimise tailored treatment for obstructive sleep apnea.

show abstract

“…There has been a lot of valuable work on automatic sleep state classification in recent years, automatic sleep staging mainly uses traditional machine learning methods in the early stage, such as Support Vector Machine (SVM) (Alickovic and Subasi, 2018 ) or Random Forest (RF) (Memar and Faradji, 2018 ), which have high requirements for handcrafted features. Since traditional machine learning methods require complex feature engineering, researchers began to use deep learning for automatic sleep staging and achieved high accuracy (Supratak et al, 2017 ; Phan et al, 2019 ; Bakker et al, 2022 ; Li et al, 2022 ; Martín-Montero et al, 2023 ; Zhang et al, 2023 ). Although deep learning methods have achieved high accuracy, they have not fully exploited the topology of functional connections in different brain regions.…”

Section: Introductionmentioning

confidence: 99%

STDP-based adaptive graph convolutional networks for automatic sleep staging

et al. 2023

View full text Add to dashboard Cite

Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.

show abstract

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

Cited by 11 publications

References 54 publications

Diagnosis and Treatment of Sleep Apnea in Children: A Future Perspective Is Needed

Diagnosis and Treatment of Sleep Apnea in Children: A Future Perspective Is Needed

Obstructive sleep apnea heterogeneity and autonomic function: a role for heart rate variability in therapy selection and efficacy monitoring

STDP-based adaptive graph convolutional networks for automatic sleep staging

Contact Info

Product

Resources

About