Summary
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.
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Extract
Signal-to-Noise Ratios for Measuring Saliency of Features Extracted by Eigenvector Methods From Ecg Signals
1. Introduction
Electrocardiography is an important tool in diagnosing the condition of the heart. The electrocardiogram (ECG) is the record of variation of bioelectric potential with respect to time as the human heart beats. It provides valuable information about the functional aspects of the heart and cardiovascular system. Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. Therefore, extensive research and work analyzing the ECG signals have been reported [1-7]. The state of cardiac health is generally reflected in the shape of ECG waveform and heart rate. It may contain important pointers to the nature of diseases afflicting the heart. However, biosignals are nonstationary signals, the reflection of which may occur at random in the time scale. In this situation, the disease symptoms may not show up all the time, but they would manifest at certain irregular intervals during the day. Therefore, for effective diagnostics, the study of ECG pattern and heart rate variability signal may have to be carried out over several hours. The volume of the data is thus enormous and the study is tedious and time consuming. Naturally, the possibility of the analyst missing (or misreading) vital information is high. Therefore, computerbased analysis and classification of diseases can be very helpful in diagnostics [1-7] .The entire process of methodologies developed for automated diagnosis can generally be subdivided into a number of disjoint proc...See the full content of this document
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