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 Systjournal_title
Journal of medical systemsauthors
Ubeyli EDdoi
10.1007/s10916-008-9152-xsubject
Has Abstractpub_date
2008-12-01 00:00:00pages
463-70issue
6eissn
0148-5598issn
1573-689Xjournal_volume
32pub_type
杂志文章abstract::A study was conducted on the use of radiographic studies by 138 medical residents. They ordered an initial chest film on 88% of their patients and averaged a 34% yield. Though the range of other radiographic studies ordered per patient varied by a factor of 15, more than half the resident physicians ordered an average...
journal_title:Journal of medical systems
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pub_type: 历史文章,杂志文章,评审
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journal_title:Journal of medical systems
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journal_title:Journal of medical systems
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