Measuring saliency of features using signal-to-noise ratios for detection of electrocardiographic changes in partial epileptic patients.

Abstract:

:Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of two types of electrocardiogram (ECG) beats (normal and partial epilepsy). In order to extract features representing the ECG signals, discrete wavelet transform was used. The PNNs used in the ECG signals classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and non-salient input features.

journal_name

J Med Syst

authors

Ubeyli ED

doi

10.1007/s10916-008-9152-x

subject

Has Abstract

pub_date

2008-12-01 00:00:00

pages

463-70

issue

6

eissn

0148-5598

issn

1573-689X

journal_volume

32

pub_type

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