Assessment of the classification capability of prediction and approximation methods for HRV analysis.
The goal of this paper is to examine the classification capabilities of various prediction and approximation methods and suggest which are most likely to be suitable for the clinical setting. Various prediction and approximation methods are applied in order to detect and extract those which provide the better differentiation between control and patient data, as well as members of different age groups. The prediction methods are local linear prediction, local exponential prediction, the delay times method, autoregressive prediction and neural networks. Approximation is computed with local linear approximation, least squares approximation, neural networks and the wavelet transform. These methods are chosen since each has a different physical basis and thus extracts and uses time series information in a different way.
Manis
George
G
Department of Computer Science, University of Ioannina, Ioannina 45110, Greece. manis@cs.uoi.gr
Nikolopoulos
Stavros
S
Alexandridi
Anastasia
A
Davos
Constantinos
C
D000328:Adult; D000367:Age Factors; D000368:Aged; D003327:Coronary Disease / Q000503:physiopathology; D004562:Electrocardiography / Q000145:classification / Q000706:statistics & numerical data / Q000639:trends; D015716:Electrocardiography, Ambulatory / Q000145:classification / Q000706:statistics & numerical data / Q000639:trends; D005260:Female; D005544:Forecasting; D005583:Fourier Analysis; D006333:Heart Failure / Q000503:physiopathology; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D013997:Time Factors