Heart rate asymmetry as a new marker for neonatal stress

Kramarić, Karolina; Šapina, Matej; Garcin, Matthieu; Milas, Krešimir; Pirić, Marko; Brdarić, Dario; Lukić, Gordana; Milas, Vesna; Pušeljić, Silvija

doi:10.1016/j.bspc.2018.08.027

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…This study clearly shows the correlation between HRV and stress (31). Also, Karolina Kramaric et al have discovered that the asymmetry of the neonate heart rate is affected by stressful stimuli, which indicates the importance of detecting stress (32).…”

Section: Discussionsupporting

confidence: 54%

Heart rate variability features for different stress classification

Moridani¹,

Mahabadi²,

Javadi³

2020

BLL

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AIM: Autonomic nervous system (ANS) activities during different types of stress could affect the electrocardiogram (ECG) signal. This study aimed to recognize the types of stress by using different ECG signals in order to prevent its actual physiological effects on the heart signal. METHOD: The ECG signal recorded by portable wrist bracelets from 20 students in during seven phases which incorporated three different types of stress and four relaxation phases. After different forms of windowing the signal, we used linear and non-linear features such as detrended fl uctuation analysis (DFA), Poincaré plot, approximate and sample entropy, correlation dimension, and recurrence plot to extract various features of the heart rate variability (HRV). Then, different classifi ers were used to identify the types of stress. RESULTS: The results showed a decrease in NN50, RMSSD, pNN50, and recurrence plot features, and an increase in the DFA method during stress stages, which show the effect of stress on heart rate. Also, by using the convolutional neural network (CNN), an average classifi cation rate of 98 % was obtained in association with cognitive stress and that of 94.5 % in association with emotional stress. CONCLUSION: This paper showed that features extracted from HRV can detect the stress and non-stress stages with high signifi cance. Also, the accuracy of this paper proved that the proposed method is successful in preventing the dangerous effects of different types of stress on the heart (Tab.

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Section: Discussionsupporting

confidence: 54%

Heart rate variability features for different stress classification

Moridani¹,

Mahabadi²,

Javadi³

2020

BLL

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“…Several studies have examined HRA clinical application in neonates. Index of HRA seems to be a promising tool in neonatal stress examination (Kramaric et al, 2019). Induction of stress led to change in GI in neonatal.…”

Section: Discussionmentioning

confidence: 99%

“…Mathematical methods of HRA calculations are constantly being developed. There are attempts to redefine currently used descriptors (e.g., redefined GI in Karmakar et al, 2012) and to create new ones, as slope index, area index, or SKG index (Karmakar et al, 2015a;Yan et al, 2017;Kramaric et al, 2019).…”

Section: Hrv and Hramentioning

confidence: 99%

Heart Rate Asymmetry Analysis During Head-Up Tilt Test in Healthy Men

et al. 2021

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The purpose of this study is to assess the cardiovascular system response to orthostatic stress in a group of 133 healthy men using heart rate asymmetry (HRA) methods. HRA is a feature of variability in human heart rate which is dependent upon external and internal body conditions. The initial phases of head-up tilt test (HUTT), namely, supine and tilt, were chosen as the external body affecting factors. Various calculation methods of HRA, such as Porta’s index (PI), Guzik’s index (GI), and its variance based components, were used to assess the heart rate variability (HRV) and its asymmetry. We compared 5-min ECG recordings from both supine and tilt phases of HUT test. Short-term HRA was observed in 54.1% of men in supine phase and 65.4% of men in tilt phase. The study revealed significant increase of GI (from 0.50 to 0.52, p < 0.001) in the tilt phase as well as significant changes in HRV descriptors between HUTT phases. Our results showed that the variability of human heart rate and its asymmetry are sensitive to orthostatic stress. The study of short-term HRA is a potential additional tool to increase sensitivity in conditions where HUTT is a diagnostic tool, such as vasovagal syncope.

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“…The physiological interpretation of this behavior is not completely understood, and can currently only be speculated. HRV analysis has been already applied in the neonatal framework, using a multi-lag tone entropy, asymmetric detrended fluctuation, asymmetric Poincaré plot approach, and estimating the Hurst exponent using a mean-reversion approach [22][23][24][25] . The increase of the negative scaling exponent, depicting higher autocorrelation, and the Poincaré plot asymmetry showed a higher contribution of accelerations within the RR interval time series, while the multi-lag tone-entropy showed a decrease of the parasympathetic, and an increase of the sympathetic branch of the autonomic nervous system [22][23][24] .…”

Section: Discussionmentioning

confidence: 99%

Lempel-Ziv complexity of the pNNx statistics – an application to neonatal stress

Šapina¹,

Karmakar²,

Kramarić³

et al. 2021

Chaos, Solitons & Fractals

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Among the existing measures of heart rate variability (HRV), the pNN50 statistics is one of the most commonly reported. However, it is only a single member of a much larger family of HRV measures -the pNNx statistics. In this research pNNx was further extended, combining it with the Lempel-Ziv complexity (LZ76) in a controlled neonatal stress framework. Forty healthy newborns were recorded undergoing two different types of stress stimuli -a routine heel stick blood sampling, and a dull heel pressure stimulation. Instead of relying on a single value, the entire spectrum from pNN1 to pNN100 was calculated, along with LZ76 derived from binarized sequences for each NN. The results of this study show a downward shift of the pNNx curves when newborns are stressed, with reduced LZ76 complexity when stressed.When ROC curves were utilized for the pNNx statistics and LZ76, however, the highest AUC values were observed when both measures were combined, with the highest AUC values of 0.88 (0.80-0.94) and 0.85 (0.74-0.91) for discriminating resting states from stress phases.Combining the widely used pNNx statistics with LZ76 extends the existing HRV toolbox, and shows a promising application in recognizing acute neonatal stress.

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Heart rate asymmetry as a new marker for neonatal stress

Cited by 11 publications

References 24 publications

Heart rate variability features for different stress classification

Heart rate variability features for different stress classification

Heart Rate Asymmetry Analysis During Head-Up Tilt Test in Healthy Men

Lempel-Ziv complexity of the pNNx statistics – an application to neonatal stress

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