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The main goal of this paper is to give the basis for creating a computer-based clinical decision support (CDS) system for laryngopathies. One of approaches which can be used in the proposed CDS is based on the speech signal analysis using recurrent neural networks (RNNs). RNNs can be used for pattern recognition in time series data due to their ability of(More)
The research concerns computer-based clinical decision support for laryngopathies. The proposed computer tool is based on a speech signal analysis in the time domain using recurrent neural networks. Such networks have the ability of time series prediction because of their memory nodes as well as local recurrent connections. In our experiments we use the(More)
Classification of voice signals in a time domain for detecting some disturbances can be made through mining unique episodes in temporal information systems. In ideal case, a voice signal is periodic and signal shapes in each period are the same. In case of larynx diseases, some disturbances in a voice signal can be distinguished. Selected time windows(More)
—In the paper, we present a new computer tool supporting a non-invasive diagnosis of selected larynx diseases. The tool is created for the Java platform. The computer-aided diagnosis of laryngopathies, in the presented tool, is based on analysis of a patient's voice signal in time and frequency domains. A number of classification ways proposed for the(More)
In the paper, we are interested in classification of disturbed periodic biosignals. An ant based clustering algorithm is used to group episodes into which examined biosignals are divided. Disturbances in periodicity of such signals cause some difficulties in formation of coherent clusters of similar episodes. A quality of a clustering process result can be(More)
In the paper, we present a new computer tool supporting a non-invasive diagnosis of selected larynx diseases. The tool is created for the Java platform. The computer-aided diagnosis of laryngopathies, in the presented tool, is based on analysis of a patient's voice signal in time and frequency domains. A number of classification ways proposed for the(More)