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The goal of this paper is to present a novel approach in the automatic diagnosis of ECG abnormalities based on detection of R peaks in the phase space. The features are extracted from detected R peaks using their geometric position on the phase curve. This paper is dealing with classification problem of normal and abnormal ECG signals. The proposed system has been validated with the data from the...
According to the symmetric characteristics of bispectrum, a novel feature extraction scheme, which includes the summation-at-every-column feature vector, the summation-at-every-row feature vector and their combination in a triangle area, one of the 12 symmetric areas of bispectrum, is proposed. By using One-against-One (OAO) method of multi classification of Support Vector Machine (SVM), the mean...
This work is an evaluation, to which degree geological information can be obtained from modern remote sensing systems like the multispectral ASTER or the hyperspectral Hyperion sensor for a volcanic region like Teide Volcano (Tenerife, Canary Islands). To account for the enhanced information content these sensors provide, hyperspectral analysis methods, incorporating for example Minimum Noise Fraction-Transformation...
Objective: To realize the automatic classification between melancholic and healthy persons by extracting the disease features from the melancholic's EEG signals. Methods: 1. Extracting the features from the EEG signals of melancholic and healthy persons; 2. Obtaining the characteristic parameters such as the maximum, minimum, mean and standard deviation of EEG power spectrum amplitude; 3. Training...
Optimizing a classifier is a subject of great interest in the research area. A lot of methods inspired of biological metaphors are proposed for this task. This paper present a new algorithm based on the natural immune metaphors which select a proper subset of features and optimal parameters of a support vector machines (SVM) classifier. The designed optimization method is validated for ERP assessment...
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