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.

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.

Reducing false arrhythmia alarm rates using robust heart rate estimation and cost-sensitive support vector machines

2017 ◽  
Vol 38 (2) ◽  
pp. 259-271 ◽  
Author(s):  
Qiang Zhang ◽  
Xianxiang Chen ◽  
Zhen Fang ◽  
Qingyuan Zhan ◽  
Ting Yang ◽  
...  
Keyword(s):  
Heart Rate ◽  
Support Vector Machines ◽  
Support Vector ◽  
Rate Estimation ◽  
Vector Machines ◽  
Heart Rate Estimation
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