Signal-to-noise ratios for measuring saliency of features extracted by eigenvector methods from ecg signals

Title:

Signal-to-noise ratios for measuring saliency of features extracted by eigenvector methods from ecg signals

Creator:

Übeyli , Elif Derya

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:46ab2732-eca3-4c95-beef-5f087183f881
uuid:46ab2732-eca3-4c95-beef-5f087183f881

Subject:

Feature saliency, signal-to-noise ratio, eigenvector methods, and electrocardiogram (ECG) beats classification

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. Features are used to represent patterns with minimal loss of important information. The feature vector, which is comprised of the set of all features used for describing 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 recurrent neural networks (RNNs) used in the classification of electrocardiogram (ECG) signals. In order to extract features representing four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database, eigenvector methods were used. The RNNs used in the ECG beats classification were trained for the SNR screening method. The results of the application of the SNR screening method to the ECG signals demonstrated that classification accuracies of the RNNs with salient input features are higher than those of the RNNs with salient and non-salient input features.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2008 Volume:18 | Number:5

Harvested from:

CDK

Metadata only:

false