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Due to redundancy of over-dimensioned information, observed often in originally recorded biomedical signals, feature extraction and selection has become focus of much researches connected with biomedical signal processing and classification. Mixed new feature vector combined from time-frequency signal representation (obtained after wavelet transform) and(More)
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(More)
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(More)
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(More)
In this paper, we describe the RobinHeart surgery robot development related project in which entire robot operation is supposedly done remotely, using wide area network connection. In such environment, any vision and telemanipulation data packets are subject to delay, limitations, issues and failures, as any network connections do. It has become necessary(More)