Hybrid feature vector extraction in unsupervised learning neural classifier.

Feature extraction and selection method as a preliminary stage of heart rate variability (HRV) signals unsupervised learning neural classifier is presented. Multi-domain, mixed new feature vector is created from time, frequency and time-frequency parameters of HRV analysis. The optimal feature set for given classification task was chosen as a result of feature ranking, obtained after computing the class separability measure for every independent feature. Such prepared a new signal representation in reduced feature space is the input to neural classifier based on introduced by Grosberg Adaptive Resonance Theory (ART2) structure. Test of proposed method carried out on the base of 62 patients with coronary artery disease divided into learning and verifying set allowed to chose these features, which gave the best results. Classifier performance measures obtained for unsupervised learning ART2 neural network was comparable with these reached for multiplayer perceptron structures.

An improvement of unsupervised hybrid biomedical signal classifiers by optimal feature extraction in system preliminary layer.

In this paper we try to place emphasis especially on the feature extraction stage of classification procedure, where new feature vectors obtained from a high-dimensional data space, which the best match the analysed classification task are proposed. Based on multilevel Mallat wavelet decomposition, parameters obtained directly from the wavelet component as well as feature resulting from energy and entropy analysis are tested. In classifier part of proposed hybrid systems, unsupervised learning systems with self organizing maps (SOM) and adaptive resonance networks (ART2) are verified. T-F methods and particularly wavelet analysis was chosen as feature extraction tool because of its ability to deal with non-stationary signals. It is important to take into consideration, that heart rate variability (HRV) signals, which were classified in elaborated systems are nonstationary and have important parameters included both in time and frequency domain. Proposed structures were tested using the set of clinically characterized heart rate variability (HRV) signals of 62 patients, as cases with a coronary artery disease of different level. Additionally similar control group of healthy patients was analyzed. Whole database was divided into learning and verifying set. Results showed, that the new HRV signal representation obtained in the space created by the feature vector based on Shannon entropy of Mallat component energy distribution gave the best classifier performance with ART2 neural structure used in classifier part of described hybrid system.