scholarly journals Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines

2021 ◽  
Vol 12 ◽  
Author(s):  
John Morales ◽  
Pascal Borzée ◽  
Dries Testelmans ◽  
Bertien Buyse ◽  
Sabine Van Huffel ◽  
...  
Keyword(s):  
Heart Rate ◽  
Support Vector Machines ◽  
Respiratory Sinus Arrhythmia ◽  
Support Vector ◽  
Regression Problem ◽  
Sinus Arrhythmia ◽  
Linear Interaction ◽  
Obstructive Sleep ◽  
Vector Machines ◽  
Cardiorespiratory Coupling

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.

scholarly journals The effect of adenotonsillectomy for childhood sleep apnoea on cardiorespiratory control

2016 ◽  
Vol 2 (2) ◽  
pp. 00003-2016 ◽  
Author(s):  
Mathias Baumert ◽  
Yvonne Pamula ◽  
James Martin ◽  
Declan Kennedy ◽  
Anand Ganesan ◽  
...  
Keyword(s):  
Heart Rate ◽  
Respiratory Rate ◽  
Obstructive Sleep Apnoea ◽  
Respiratory Sinus Arrhythmia ◽  
Sleep Apnoea ◽  
Obstructive Sleep Apnoea Syndrome ◽  
Sinus Arrhythmia ◽  
Cardiorespiratory Control ◽  
Post Surgery ◽  
Obstructive Sleep

The efficacy of adenotonsillectomy for relieving obstructive sleep apnoea symptoms in children has been firmly established, but its precise effects on cardiorespiratory control are poorly understood.In 375 children enrolled in the Childhood Adenotonsillectomy Trial, randomised to undergo either adenotonsillectomy (n=194) or a strategy of watching waiting (n=181), respiratory rate, respiratory sinus arrhythmia and heart rate were analysed during quiet, non-apnoeic and non-hypopnoeic breathing throughout sleep at baseline and at 7 months using overnight polysomnography.Children who underwent early adenotonsillectomy demonstrated an increase in respiratory rate post-surgery while the watchful waiting group showed no change. Heart rate and respiratory sinus arrhythmia were comparable between both arms. On assessing cardiorespiratory variables with regard to normalisation of clinical polysomnography findings during follow-up, heart rate was reduced in children who had resolution of obstructive sleep apnoea syndrome, while no differences in their respiratory rate or respiratory sinus arrhythmia were observed.Adenotonsillectomy for obstructive sleep apnoea increases baseline respiratory rate during sleep. Normalisation of apnoea–hypopnoea index, spontaneously orviasurgery, lowers heart rate. Considering the small average effect size, the clinical significance is uncertain.

Heart Rate Turbulence Denoising Using Support Vector Machines

2009 ◽  
Vol 56 (2) ◽  
pp. 310-319 ◽  
Author(s):  
Jose Luis Rojo-Alvarez ◽  
Oscar Barquero-Perez ◽  
Inmaculada Mora-Jimenez ◽  
Estrella Everss ◽  
Ana Belen Rodriguez-Gonzalez ◽  
...  
Keyword(s):  
Heart Rate ◽  
Support Vector Machines ◽  
Support Vector ◽  
Heart Rate Turbulence ◽  
Vector Machines

FEATURE EXTRACTION AND CLASSIFICATION OF FETAL HEART RATE USING WAVELET ANALYSIS AND SUPPORT VECTOR MACHINES

2006 ◽  
Vol 15 (03) ◽  
pp. 411-432 ◽  
Author(s):  
GEORGE GEORGOULAS ◽  
CHRYSOSTOMOS STYLIOS ◽  
PETER GROUMPOS
Keyword(s):  
Heart Rate ◽  
Feature Extraction ◽  
Support Vector Machines ◽  
Fetal Heart Rate ◽  
Umbilical Artery ◽  
Fetal Heart ◽  
Support Vector ◽  
Ph Values ◽  
Vector Machines

Since the fetus is not available for direct observations, only indirect information can guide the obstetrician in charge. Electronic Fetal Monitoring (EFM) is widely used for assessing fetal well being. EFM involves detection of the Fetal Heart Rate (FHR) signal and the Uterine Activity (UA) signal. The most serious fetal incident is the hypoxic injury leading to cerebral palsy or even death, which is a condition that must be predicted and avoided. This research work proposes a new integrated method for feature extraction and classification of the FHR signal able to associate FHR with umbilical artery pH values at delivery. The proposed method introduces the use of the Discrete Wavelet Transform (DWT) to extract time-scale dependent features of the FHR signal and the use of Support Vector Machines (SVMs) for the categorization. The proposed methodology is tested on a data set of intrapartum recordings were the FHR categories are associated with umbilical artery pH values, This proposed approach achieved high overall classification performance proving its merits.

Variation of respiratory sinus arrhythmia with age

1976 ◽  
Vol 41 (5) ◽  
pp. 734-738 ◽  
Author(s):  
J. B. Hellman ◽  
R. W. Stacy
Keyword(s):  
Heart Rate ◽  
Control Systems ◽  
Respiratory Sinus Arrhythmia ◽  
Linear Regression Analysis ◽  
Noninvasive Method ◽  
Sinus Arrhythmia ◽  
Cardiorespiratory Control ◽  
Cardiorespiratory Coupling ◽  
The Subject ◽  
Male Subjects

Respiratory sinus arrhythmia was measured on 24 male subjects whose agesranged from 21 to 65 yr, using a technique in which respiration was coupledto heart rate, so that there were 6 heartbeats per inspiration and 6 heartbeats per expiration. This voluntary cardiorespiratory coupling reduced the variance of heart rates measured at various points in the respiratory cycle. Fourier analysis was used to obtain the fundamental of heart rate and respiratory volume. A high correlation coefficient (-0.83) was found betweenpercent of variation of heart rate from the average heart rate (PB) and theage of the subject. Linear regression analysis performed on these data produced the equation: PB = 23.2–0.35 (age). Since sinus arrhythmia at thelow respiratory frequencies used probably reflects the viability of the cardiorespiratory control systems, we believe this provides a simple noninvasive method for the study of the overall competence of the system responsiblefor respiratory sinus arrhythmia.

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