scholarly journals Are Inter-Beat Intervals from Blood Pressure a Valid Alternative to R-R Intervals for the Multiscale Entropy Analysis of Heart Rate Variability?

Author(s):  
Andrea Faini ◽  
Gianfranco Parati ◽  
Paolo Castiglioni
2015 ◽  
Vol 192 ◽  
pp. 78
Author(s):  
L.E.V. Silva ◽  
J.A. Castania ◽  
H.C. Salgado ◽  
R. Fazan

Entropy ◽  
2015 ◽  
Vol 17 (1) ◽  
pp. 231-243 ◽  
Author(s):  
Wen-Yao Pan ◽  
Mao-Chang Su ◽  
Hsien-Tsai Wu ◽  
Meng-Chih Lin ◽  
I-Ting Tsai ◽  
...  

Fractals ◽  
2008 ◽  
Vol 16 (03) ◽  
pp. 199-208 ◽  
Author(s):  
A. SARKAR ◽  
P. BARAT

The heart beat data recorded from samples before and during meditation are analyzed using two different scaling analysis methods. These analyses revealed that meditation severely affects the long range correlation of heart beat of a normal heart. Moreover, it is found that meditation induces periodic behavior in the heart beat. The complexity of the heart rate variability is quantified using multiscale entropy analysis and recurrence analysis. The heart beat during meditation is found to be more complex.


2021 ◽  
Vol 8 (9) ◽  
pp. 122
Author(s):  
Lorenzo Frassineti ◽  
Antonio Lanatà ◽  
Benedetta Olmi ◽  
Claudia Manfredi

The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.


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