Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis

Lavanga, Mario; Heremans, Elisabeth; Moeyersons, Jonathan; Bollen, Bieke; Jansen, Katrien; Ortibus, Els; Naulaers, Gunnar; Huffel, Sabine Van; Caicedo, Alexander

doi:10.3389/fphys.2020.581250

Cited by 10 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, although DistEn showed a different changing direction compared with the other five entropy measures, they were not contradictory to each other. In addition, there are studies to quantify complexity from another perspective, for example, multifractality [54]. Multifractality has a different theoretical basis, which may also have important implications for studying the dynamic of RR interval time series during sleep.…”

Section: Discussionmentioning

confidence: 99%

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Yan

Yang

et al. 2022

Entropy

View full text Add to dashboard Cite

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

show abstract

Section: Discussionmentioning

confidence: 99%

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Yan

Yang

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Furthermore, already before the LISA procedure significant differences in HRV were observed between the CPAP-F and CPAP-S groups. HR, HRV and respiration rate all vary with GA (Lavanga et al 2021, Iyer et al 2023, as these features are important for the prediction it may reflect a large range dependency. However, in post hoc analysis of our current data set the dependency of each feature itself with GA did not display significant correlations.…”

Section: Discussionmentioning

confidence: 99%

Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study

Kloonen,

Varisco,

de Kort

et al. 2023

Physiol. Meas.

View full text Add to dashboard Cite

Objective. Less invasive surfactant administration (LISA) has been introduced to preterm infants with respiratory distress syndrome on continuous positive airway pressure (CPAP) support in order to avoid intubation and mechanical ventilation. However, after this LISA procedure, a significant part of infants fails CPAP treatment (CPAP-F) and requires intubation in the first 72 h of life, which is associated with worse complication free survival chances. The aim of this study was to predict CPAP-F after LISA, based on machine learning (ML) analysis of high resolution vital parameter monitoring data surrounding the LISA procedure. Approach. Patients with a gestational age (GA) <32 weeks receiving LISA were included. Vital parameter data was obtained from a data warehouse. Physiological features (HR, RR, peripheral oxygen saturation (SpO2) and body temperature) were calculated in eight 0.5 h windows throughout a period 1.5 h before to 2.5 h after LISA. First, physiological data was analyzed to investigate differences between the CPAP-F and CPAP-Success (CPAP-S) groups. Next, the performance of two types of ML models (logistic regression: LR, support vector machine: SVM) for the prediction of CPAP-F were evaluated. Main results. Of 51 included patients, 18 (35%) had CPAP-F. Univariate analysis showed lower SpO2, temperature and heart rate variability (HRV) before and after the LISA procedure. The best performing ML model showed an area under the curve of 0.90 and 0.93 for LR and SVM respectively in the 0.5 h window directly after LISA, with GA, HRV, respiration rate and SpO2 as most important features. Excluding GA decreased performance in both models. Significance. In this pilot study we were able to predict CPAP-F with a ML model of patient monitor signals, with best performance in the first 0.5 h after LISA. Using ML to predict CPAP-F based on vital signals gains insight in (possibly modifiable) factors that are associated with LISA failure and can help to guide personalized clinical decisions in early respiratory management.

show abstract

“…In addition, we kept HRV analysis rather simple, considering the wide range of HRV metrics [44]. Future studies might benefit from more sophisticated and time-resolved analysis of heart rate variations (e.g., [45]).…”

Section: Limitationsmentioning

confidence: 99%

Estimating Resting HRV during fMRI: A Comparison between Laboratory and Scanner Environment

Schumann

Suttkus

Bär

2021

Sensors

View full text Add to dashboard Cite

Heart rate variability (HRV) is regularly assessed in neuroimaging studies as an indicator of autonomic, emotional or cognitive processes. In this study, we investigated the influence of a loud and cramped environment during magnetic resonance imaging (MRI) on resting HRV measures. We compared recordings during functional MRI sessions with recordings in our autonomic laboratory (LAB) in 101 healthy subjects. In the LAB, we recorded an electrocardiogram (ECG) and a photoplethysmogram (PPG) over 15 min. During resting state functional MRI, we acquired a PPG for 15 min. We assessed anxiety levels before the scanning in each subject. In 27 participants, we performed follow-up sessions to investigate a possible effect of habituation. We found a high intra-class correlation ranging between 0.775 and 0.996, indicating high consistency across conditions. We observed no systematic influence of the MRI environment on any HRV index when PPG signals were analyzed. However, SDNN and RMSSD were significantly higher when extracted from the PPG compared to the ECG. Although we found a significant correlation of anxiety and the decrease in HRV from LAB to MRI, a familiarization session did not change the HRV outcome. Our results suggest that psychological factors are less influential on the HRV outcome during MRI than the methodological choice of the cardiac signal to analyze.

show abstract

Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis

Cited by 10 publications

References 43 publications

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study

Estimating Resting HRV during fMRI: A Comparison between Laboratory and Scanner Environment

Contact Info

Product

Resources

About