Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal.
about
Mathematical biomarkers for the autonomic regulation of cardiovascular systemBayesian quantitative electrophysiology and its multiple applications in bioengineering.Heart rate variability dynamics for the prognosis of cardiovascular risk.Machine learning techniques for arterial pressure waveform analysis.A novel automatic detection system for ECG arrhythmias using maximum margin clustering with immune evolutionary algorithm.Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarctionSystematic mapping study of data mining-based empirical studies in cardiology.Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG.Implementation of a portable device for real-time ECG signal analysis.Prediction of p38 map kinase inhibitory activity of 3, 4-dihydropyrido [3, 2-d] pyrimidone derivatives using an expert system based on principal component analysis and least square support vector machine.Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndromeInter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO.Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation.Arrhythmia Detection based on Morphological and Time-frequency Features of T-wave in Electrocardiogram.Accurate prediction of coronary artery disease using reliable diagnosis system.Geometric patterns of time-delay plots from different cardiac rhythms and arrhythmias using short-term EKG signals.Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier.Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances.Automated diagnosis of coronary artery diseased patients by heart rate variability analysis using linear and non-linear methods.A classification scheme for ventricular arrhythmias using wavelets analysis.High efficient system for automatic classification of the electrocardiogram beats.Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning.Detection of coronary artery disease by reduced features and extreme learning machine.
P2860
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P2860
Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal.
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年学术文章
@wuu
2008年学术文章
@zh
2008年学术文章
@zh-cn
2008年学术文章
@zh-hans
2008年学术文章
@zh-my
2008年学术文章
@zh-sg
2008年學術文章
@yue
2008年學術文章
@zh-hant
name
Support vector machine-based a ...... heart rate variability signal.
@en
Support vector machine-based a ...... heart rate variability signal.
@nl
type
label
Support vector machine-based a ...... heart rate variability signal.
@en
Support vector machine-based a ...... heart rate variability signal.
@nl
prefLabel
Support vector machine-based a ...... heart rate variability signal.
@en
Support vector machine-based a ...... heart rate variability signal.
@nl
P2093
P1476
Support vector machine-based a ...... heart rate variability signal.
@en
P2093
Babak Mohammadzadeh Asl
Maryam Mohebbi
Seyed Kamaledin Setarehdan
P356
10.1016/J.ARTMED.2008.04.007
P577
2008-06-27T00:00:00Z