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In this paper an improvement of wavelet based methods for detection and classification of power quality disturbances is presented. In the feature extraction process wavelet analysis is also used as in the comparing methods. However, the feature vector is extended with three other coefficients in order to improve the accuracy of the algorithm. In order to evaluate the proposed method, large number...
This paper addresses the problem of automatic wavelet feature extraction for signal classication. We propose to jointly learn wavelet-based features (including scale and translation of the wavelet as well as its shape) and a decision function by casting the problem as a Multi-Kernel Learning problem. A novel active constraints algorithm is then proposed. Our method has been tested on a toy dataset...
Presented paper describes a system of biomedical signal classifiers with preliminary feature extraction stage based on matched wavelets analysis, where two structures of classifier using Neural Networks (NN) and Support Vector Machine (SVM) are applied. As a pilot study the rules extraction algorithm applied for two of mentioned machine learning approaches (NN & SVM) was used. This was made to...
Single trial electroencephalogram (EEG) classification is essential in developing brain-computer interfaces (BCIs). However, popular classification algorithms, e.g., common spatial patterns (CSP), usually highly depend on the prior neurophysiologic knowledge for noise removing, although this knowledge is not always known in practical applications. In this paper, a novel tensor-based scheme is proposed...
Traditional Chinese Pulse Diagnosis (TCPD), one of the four diagnostic methods of Traditional Chinese Medicine (TCM), had been proved to be clinically valid in Chinese Medicine history. Different from most previous work which focused on the diagnosis of cardiovascular diseases, this paper further investigated the possibility of diagnosing cholecystitis and nephrotic syndrome using the pulse waveform...
Since EEG is one of the most important sources of information in diagnosis of epilepsy, several researchers tried to address the issue of decision support for such a data. We present a method for classifying epilepsy of full spectrum EEG recordings. In the proposed method, autoregressive (AR) model is used to acquire power spectrum of EEG signals, then dimension of the extracted feature vectors is...
We expand the idea to develop new bio-signal processing tools that could predict possibility of future risk of abnormalities in ECG signals. The goal is to detect an inherent defect hidden in an ECG signal using wavelet analysis and support vector machines. We apply singular value decomposition analysis of spectral energy distribution in time-frequency plane to extract features, which is essentially...
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