Detecting premature ventricular contractions in ECG signals with Gaussian processes

The aim of this work is twofold. First, we propose to investigate the capabilities of a new Bayesian approach for detecting premature ventricular contractions (PVCs), namely the Gaussian process (GP) approach. Second, we report an experimental comparison of different kinds of ECG signal representations, which are the standard temporal signal morphology, the discrete wavelet transform domain, the S-transform characteristics and the high-order statistics. In general, the obtained classification results show that the GP detector can compete seriously with state-of-the-art methods since it allows to yield better overall accuracy as well as better sensitivity. In addition, among the different kinds of features explored, those based on high-order statistics appear to be the best compromise between accuracy and computational time for PVC detection. 1

Pavement Design and Materials Research 2003-2005 Activities

This report documents the workthat has been completed under the NDOT-UNR cooperative agreement for Pavement Design and Materials Research for the 2003-2005 time period

Segmentation of Fluorescence Microscopy Cell Images Using Unsupervised Mining

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu’s threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu’s threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications

Improved building roof type classification using correlation-based feature selection and gain ratio algorithms

Of late, application of data mining for pattern recognition and feature classification is fast becoming an essential technique in remote sensing research. Accurate feature selection is a necessary step to improve the accuracy of classification. This process depends on the number of feature attributes available for interactive synthesis of common characteristics that discriminate different features. Geographic object-based image analysis (GEOBIA) has made it possible to derive varieties of object attribute for this purpose; however, the analysis is more computationally intensive. The aim of this study is to develop feature selection technique that will provide the most suitable attributes to identify different roofing materials and their conditions. First, the feature importance was evaluated using gain ratio algorithm, and the result was ranked, leading to selection of the optimal feature subset. Then, the quality of the selected features was assessed using correlation-based feature selection (CFS). The classification results using SVM classifier produced an overall accuracy of 83.16%. The study has shown that the ability to exploit rich image feature attribute through optimization process improves accurate extraction of roof material with greater reliability

RVM-based multi-class classification of remotely sensed data

The relevance vector machine (RVM), a Bayesian extension of the support vector machine (SVM), has considerable potential for the analysis of remotely sensed data. Here, the RVM is introduced and used to derive a multi-class classification of land cover with an accuracy of 91.25%, a level comparable to that achieved by a suite of popular image classifiers including the SVM. Critically, however, the output of the RVM includes an estimate of the posterior probability of class membership. This output may be used to illustrate the uncertainty of the class allocations on a per-case basis and help to identify possible routes to further enhance classification accuracy

Editorial to special issue "multispectral image acquisition, processing, and analysis"

International audience[No abstract available