Biomedical subjects

B Sankur

Publications and source records attributed to B Sankur.

3 recordsLinked to original sources

PubMed1998

Visual classification of medical data using MLP mapping.

In this work we discuss the design of a novel non-linear mapping method for visual classification based on multilayer perceptrons (MLP) and assigned class target values. In training the perceptron, one or more target output values for each class in a 2-dimensional space are used. In other words, class membership information is interpreted visually as closeness to target values in a 2D feature space. This mapping is obtained by training the multilayer perceptron (MLP) using class membership information, input data and judiciously chosen target values. Weights are estimated in such a way that each training feature of the corresponding class is forced to be mapped onto the corresponding 2-dimensional target value.

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PubMed1996

Multiresolution biological transient extraction applied to respiratory crackles.

A method is proposed for the detection of transients in biological signals. The method is based on enhancing the transient-to-background ratio by a series of operations such as background whitening, wavelet-based multiresolution decomposition and application of Teager's energy operator. The transients are extracted by judiciously thresholding this processed signal. The proposed detector is applied to the discrimination of crackles in pathological respiratory sounds. It is shown that both the crackle detection performance and ability to extract the transient waveforms correctly are superior to existing detectors in the literature.

Adolescent↗

PubMed1994

Comparison of AR-based algorithms for respiratory sounds classification.

Respiratory sounds of pathological and healthy subjects were analyzed via autoregressive (AR) models with a view to construct a diagnostic aid based on auscultation. Using the AR vectors, two reference libraries, pathological and healthy, were built. Two classifiers, k-nearest neighbour (k-NN) classifier and a quadratic classifier, were designed and compared. Performances of the classifiers were tested for different model orders. The best classification results were obtained for model order 6.

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