Encontrados 7 documentos, a visualizar página 1 de 1

Phonocardiogram segmentation by using Hidden Markov Models

This paper is concerned to the segmentation of heart sounds by using state of art Hidden Markov Models technology. Concerning to several heart pathologies the analysis of the intervals between the first and second heart sounds is of utmost importance. Such intervals are silent for a normal subject and the presence of murmurs indicate certain cardiovascular defects and diseases. While the first heart sound can e...

Phonocardiogram segmentation by using an hybrid RBF-HMM model

This paper is concerned to the segmentation of heart sounds by using Radial-Basis Functions for acoustical modelling, combined with a Hidden Markov Model for heart sounds sequence modelling. The idea behind the use of RBF’s is to take advantage of the local approximations using exponentially decaying localized nonlinearities achieved by the Gaussian function, which increases the clustering power relatively to M...

Hidden Markov tree model applied to the detection of micro-calcification cluste...

This paper is concerned to the application of a relatively new image texture segmentation algorithm named Hidden Markov Tree (HMT) to the detection of micro-calcification clusters in mammograms. The HMT is a wavelet-based tree-structured probabilistic graph that can capture the statistical properties of the coefficients of the wavelet transform. The aim of this approach is, on the one hand, to take advantage of...

Selective MMIE training of hidden Markov models for cardiac arrhythmia classifi...

Centre Algoritmi ; This paper is concerned to the cardiac arrhythmia classification by using Hidden Markov Models. The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, and two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF) and atrial flutter (AFL). The approach followed in this pa...

Cardiac arrhythmia detection by parameters sharing and MMIE training of hidden ...

This paper is concerned to the cardiac arrhythmia classification by using hidden Markov models and maximum mutual information estimation (MMIE) theory. The types of beat being selected are normal (N), premature ventricular contraction (V), and the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF). The approach followed in this paper is based on the supposition that atrial fib...

Object-based spatial segmentation of video guided by depth and motion information

Time limit and VO2 slow components at intensities corresponding to VO2max in sw...