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Heart rate variability (HRV) is an established indicator of cardiac health. Recent developments have shown the potential of nonlinear metrics for pattern classification of various heart conditions. Evidence indicates that the combination of multiple linear and nonlinear features leads to increased classification accuracy. In our paper, we demonstrate HRV classification using two dynamic nonlinear...
Heart rate variability (HRV) is among important characteristics of general cardiac health. While 24-hour Holter monitoring is well recognized as a comprehensive analytical technique, short-term (up to 30 min.) electrocardiogram (ECG), recorded in presence of controlled environmental stimuli, remains rather unexplored in diagnostic practice. The presented method of change point detection in such class...
Here we present a method of QT interval measurement for Physionetpsilas online QT Challenge ECG database using the wavelets Daubechies 6 and 8 and Symlet 6 and time plane features. Doing so we found that out of these three wavelets Daubechies 6 gives the best output and when averaged with the interval of time plane feature extraction method it gives least percentage of error with respect to the median...
We describe the development of an automated, adaptive method to obtain the time interval between successive heart beats from noisy and highly variable electrocardiography signals. These interbeat time series are critical to the fractal characterization of cardiac health. When the biophysical measurement is severely tainted with noise from multiple sources, there is a need for algorithms to robustly...
We describe the development of an automated, adaptive method to obtain the time interval between successive heart beats from noisy and highly variable electrocardiography signals. These interbeat time series are critical to the fractal characterization of cardiac health. When the biophysical measurement is severely tainted with noise from multiple sources, there is a need for algorithms to robustly...
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