Supervised and Unsupervised Learning Systems as a Part of Hybrid Structures Applied in EGG Signals Classifiers

This paper aims at investigating an unsupervised learnt neural networks in classifier applications and comparing them to supervised perceptron type nets. The proposed solutions focus on combing the time-frequency preliminary analysis by means of wavelet transform with application of self organizing maps. Using wavelet transform as a feature extraction tool allowed to reveal important parameters included both in time and frequency domain of non-stationary electrogastrographic signals, which were classified in elaborated systems. Proposed structures were tested using the set of clinically characterized EGG signals of 62 patients, as cases with different level rhythm disturbances from bradygastria up to tachygastria together with some artifacts of non-stationary character such as muscle thrill etc. Additionally similar control group of healthy patients was analyzed. The results of the proposed methodology are illustrated in the measure of sensitivity and specificity, where the best classifier based on Kohonen maps with preliminary wavelet processing reached the performance above 90%.