The Inconsistent Nature of Heart Rate Variability During Sleep in Normal Children and Adolescents

Kontos, Anna; Baumert, Mathias; Lushington, Kurt; Kennedy, Declan; Kohler, Mark; Cicua-Navarro, D; Pamula, Yvonne; Martin, James

doi:10.3389/fcvm.2020.00019

Cited by 17 publications

(9 citation statements)

References 57 publications

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“…Notably, we also underscore the individual variability of HRV analyses, finding that group-level differences were largely driven by highly significant changes in a minority of subjects (Table 4). This result highlights the caution that must be taken with HRV interpretation; we suggest future studies on intra-individual HRV analyses account for potentially mediating factors that may affect autonomic regulation, such as time of night [22].…”

Section: Example Application: Hrv In Wakefulness and Sleepmentioning

confidence: 85%

CardioPy: An open-source heart rate variability toolkit for single-lead EKG

Gottshall

Recoder

Schiff

2020

Preprint

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Background and ObjectiveHeart rate variability (HRV) is a promising clinical marker of health and disease. Although HRV methodology is relatively straightforward, accurate detection of R-peaks remains a significant methodological challenge; this is especially true for single-lead EKG signals, which are routinely collected alongside EEG monitoring and for which few software options exist. Most developed algorithms with favorable R-peak detection profiles require significant mathematical and computational proficiency for implementation, providing a significant barrier for clinical research. Our objective was to address these challenges by developing a simple, free, and open-source software package for HRV analysis of single-lead EKG signals.MethodsCardioPy was developed in python and optimized for short-term (5-minute) single-lead EKG recordings. CardioPy’s R-peak detection trades full automation and algorithmic complexity for an adaptive thresholding mechanism, manual artifact removal and parameter adjustment. Standard time and frequency domain analyses are included, such that CardioPy may be used as a stand-alone HRV analysis package. An example use-case of HRV across wakefulness and sleep is presented and results validated against the widely used Kubios HRV software.ResultsHRV analyses were conducted in 66 EKG segments collected from five healthy individuals. Parameter optimization was conducted or each segment, requiring ~1-3 minutes of manual inspection time. With optimization, CardioPy’s R-peak detection algorithm achieved a mean sensitivity of 100.0% (SD 0.05%) and positive predictive value of 99.8% (SD 0.20%). HRV results closely matched those produced by Kubios HRV, both by eye and by quantitative comparison; CardioPy power spectra explained an average of 99.7% (SD 0.50%) of the variance present in Kubios spectra. HRV analyses showed significant group differences between brain states; SDNN, low frequency power, and low frequency-to-high frequency ratio were reduced in slow wave sleep compared to wakefulness.ConclusionsCardioPy provides an accessible and transparent tool for HRV analyses. Manual parameter optimization and artifact removal allow granular control over data quality and a highly reproducible analytic pipeline, despite additional time requirements. Future versions are slated to include automatic parameter optimization and a graphical user interface, further reducing analysis time and improving accessibility.

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Section: Example Application: Hrv In Wakefulness and Sleepmentioning

confidence: 85%

CardioPy: An open-source heart rate variability toolkit for single-lead EKG

Gottshall

Recoder

Schiff

2020

Preprint

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“…Overall, these modifications in children without OSA (mainly snorers) were similar to those observed by Kontos et al . in 75 healthy children and adolescents (Kontos et al., 2020). It is usually stated that there is a shift of cardiac autonomic modulation across sleep stages in the direction of more parasympathetic modulation from wake to NREM sleep, and a reversal to a balance of less parasympathetic modulation entering REM sleep (Elsenbruch et al., 1999), which may seem to contradict our present results.…”

Section: Discussionmentioning

confidence: 99%

“…The minute following an event was also discarded from the analysis (Stein & Pu, 2012). We standardised the epoch of both NREM and REM sleep according to the results of Kontos et al obtained in a large sample of healthy children (n = 75) who showed that maximal differences in HRV indices can be expected from these two epochs (NREM1 and REM1 epochs) (Kontos et al, 2020). Furthermore, children with OSA have normal sleep stage distribution and OSA is predominantly a REM phenomenon in children (Goh et al, 2000).…”

Section: Heart Rate Variability Analysesmentioning

confidence: 99%

Cross‐sectional case‐control study of the relationships between pharyngeal compliance and heart rate variability indices in childhood obstructive sleep apnoea

Bokov

Mauvais

Madani

et al. 2021

Journal of Sleep Research

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A combination of noradrenergic and antimuscarinic agents reduces the apneahypopnea index (AHI) in adult patients with obstructive sleep apnoea (OSA) via reduced upper airway collapsibility, suggesting that a shift in the sympathovagal balance improves OSA. The objectives of our present case-control study were to assess heart rate variability (HRV) indices in the stages of sleep in children with and without OSA to evaluate OSA-induced sleep HRV modifications and to assess whether increased collapsibility measured during wakefulness is associated with reduced sympathetic activity during non-rapid eye movement (NREM) sleep. Three groups of 15 children were matched by sex, age, z-score of body mass index and ethnicity: non-OSA (obstructive AHI [OAHI] <2 events/hr), mild (OAHI ≥2 to <5 events/hr) or moderate-severe (OAHI ≥5 events/hr) OSA. Pharyngeal compliance was measured during wakefulness using acoustic pharyngometry. HRV indices (time and frequency domain variables) were calculated on 5-min electrocardiography recordings from polysomnography during wakefulness, NREM and REM sleep in periods free of any event. As compared to children without OSA, those with OSA (n = 30) were characterised by increased compliance and no physiological parasympathetic tone increase in REM sleep. Children with increased pharyngeal compliance (n = 21) had a higher OAHI due to higher AHI in NREM sleep, whereas their sympathetic tone was lower than that of those with normal compliance (n = 24). In conclusion, children with increased pharyngeal compliance exhibit decreased sympathetic tone associated with increased AHI in NREM sleep. Therapeutics directed at sympathovagal balance modifications should be tested in childhood OSA.

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“…During sleep, the respiration rate in toddlers may be 18 min −1 or even lower, but it may be higher than 24 min −1 as well. That is, automatic segmental analysis of a 24 h ECG recording with a narrowly restricted frequency range would most probably fail or at least provide different information compared to SD1 (or RMSSD) [ 39 ]. Furthermore, this may even hold for pre-selected segments based on strict signal preprocessing and healthy adults, where information content may be lost when the HF band according to the recommended definition is used without further consideration.…”

Section: Discussionmentioning

confidence: 99%

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

Lackner

Eglmaier

Hackl-Wimmer

et al. 2020

Sensors

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Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data is a more delicate matter. The aim of this paper is to supply detailed methodological discussion and new data material in order to provide a helpful notice of HRV monitoring issues depending on recording conditions and study populations. Special consideration is given to the monitoring over long periods, across periods with different levels of activity, and in adults versus children. Specifically, the paper aims at making users aware of neglected methodological limitations and at providing substantiated recommendations for the selection of appropriate HRV variables and their interpretation. To this end, 30-h HRV data of 48 healthy adults (18–40 years) and 47 healthy toddlers (16–37 months) were analyzed in detail. Time-domain, frequency-domain, and nonlinear HRV variables were calculated after strict signal preprocessing, using six different high-frequency band definitions including frequency bands dynamically adjusted for the individual respiration rate. The major conclusion of the in-depth analyses is that for most applications that implicate long-term monitoring across varying circumstances and activity levels in healthy individuals, the time-domain variables are adequate to gain an impression of an individual’s HRV and, thus, the dynamic adaptation of an organism’s behavior in response to the ever-changing demands of daily life. The sound selection and interpretation of frequency-domain variables requires considerably more consideration of physiological and mathematical principles. For those who prefer using frequency-domain variables, the paper provides detailed guidance and recommendations for the definition of appropriate frequency bands in compliance with their specific recording conditions and study populations.

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The Inconsistent Nature of Heart Rate Variability During Sleep in Normal Children and Adolescents

Cited by 17 publications

References 57 publications

CardioPy: An open-source heart rate variability toolkit for single-lead EKG

CardioPy: An open-source heart rate variability toolkit for single-lead EKG

Cross‐sectional case‐control study of the relationships between pharyngeal compliance and heart rate variability indices in childhood obstructive sleep apnoea

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

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