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Extracted Features (extracted + feature)
Selected AbstractsAnalysis of electrocardiographic changes in partial epileptic patients by combining eigenvector methods and support vector machinesEXPERT SYSTEMS, Issue 3 2009Elif Derya Übeyli Abstract: In the present study, the diagnostic accuracy of support vector machines (SVMs) on electrocardiogram (ECG) signals is evaluated. Two types of ECG beats (normal and partial epilepsy) were obtained from the Physiobank database. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the SVM trained on the extracted features. The present research demonstrates that the power levels of the power spectral densities obtained by eigenvector methods are features which represent the ECG signals well and SVMs trained on these features achieve high classification accuracies. [source] Probabilistic neural networks combined with wavelet coefficients for analysis of electroencephalogram signalsEXPERT SYSTEMS, Issue 2 2009Elif Derya Übeyli Abstract: In this paper, the probabilistic neural network is presented for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction by wavelet transform and classification using the classifiers trained on the extracted features. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrates that the wavelet coefficients obtained by the wavelet transform are features which represent the EEG signals well. The conclusions indicate that the probabilistic neural network trained on the wavelet coefficients achieves high classification accuracies (the total classification accuracy is 97.63%). [source] Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithmsJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 1 2006Jorge L. Martínez The paper reports on mobile robot motion estimation based on matching points from successive two-dimensional (2D) laser scans. This ego-motion approach is well suited to unstructured and dynamic environments because it directly uses raw laser points rather than extracted features. We have analyzed the application of two methods that are very different in essence: (i) A 2D version of iterative closest point (ICP), which is widely used for surface registration; (ii) a genetic algorithm (GA), which is a novel approach for this kind of problem. Their performance in terms of real-time applicability and accuracy has been compared in outdoor experiments with nonstop motion under diverse realistic navigation conditions. Based on this analysis, we propose a hybrid GA-ICP algorithm that combines the best characteristics of these pure methods. The experiments have been carried out with the tracked mobile robot Auriga-, and an on-board 2D laser scanner. © 2006 Wiley Periodicals, Inc. [source] Resolution of structure characteristics of AE signals in multiphase flow system,From data to informationAICHE JOURNAL, Issue 10 2009Yi-Jun He Abstract This investigation was performed to study the underlying structure characteristics of acoustic emission (AE) signals, which could be helpful not only to understand a relatively complete picture of hydrodynamics in multiphase flow systems, but also to extract the most useful information from the original signals with respect to a particular measurement requirement. However, due to AE signals are made up of emission from many acoustic sources at different scales, the resolution of AE signals is often very complicated and appears to be relatively poorly researched. In this study, the structure characteristics of AE signals measured both in gas,solid fluidized bed and liquid,solid stirred tank were researched in detail by resorting to wavelet transform and rescaled range analysis. A general criterion was proposed to resolve AE signals into three physical-related characteristic scales, i.e., microscale, mesoscale, and macroscale. Multiscale resolution of AE signals implied that AE signals in microscale represented totally the dynamics of solid phase and could be applied to measure particle-related properties. Furthermore, based on the structure characteristics of AE signals, useful features related to particles motion were extracted to establish two new prediction models, one for on-line measurements of particle size distribution (PSD) and average particle size in gas,solid fluidized bed and the other for on-line measurement of the suspension height in liquid,solid stirred tank. The prediction results indicated that (1) measurements of PSD and average particle size using AE method showed a fairly good agreement with that using sieve method both for laboratory scale and plant scale fluidized beds, and (2) measurements of the suspension height using AE method showed a fairly good agreement with that using visual method. The results thus validated that the extracted features based on analyses of structure characteristics of AE signals were very useful for establishing effective on-line measurement models with respect to some particular applications. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction techniqueLASERS IN SURGERY AND MEDICINE, Issue 7 2006Changfang Zhu MS Abstract Background and Objective We explored the use of diffuse reflectance spectroscopy in the ultraviolet-visible (UV-VIS) spectrum for the diagnosis of breast cancer. A physical model (Monte Carlo inverse model) and an empirical model (partial least squares analysis) based approach, were compared for extracting diagnostic features from the diffuse reflectance spectra. Study Design/Methods The physical model and the empirical model were employed to extract features from diffuse reflectance spectra measured from freshly excised breast tissues. A subset of extracted features obtained using each method showed statistically significant differences between malignant and non-malignant breast tissues. These features were separately input to a support vector machine (SVM) algorithm to classify each tissue sample as malignant or non-malignant. Results and Conclusions The features extracted from the Monte Carlo based analysis were hemoglobin saturation, total hemoglobin concentration, beta-carotene concentration and the mean (wavelength averaged) reduced scattering coefficient. Beta-carotene concentration was positively correlated and the mean reduced scattering coefficient was negatively correlated with percent adipose tissue content in normal breast tissues. In addition, there was a statistically significant decrease in the beta-carotene concentration and hemoglobin saturation, and a statistically significant increase in the mean reduced scattering coefficient in malignant tissues compared to non-malignant tissues. The features extracted from the partial least squares analysis were a set of principal components. A subset of principal components showed that the diffuse reflectance spectra of malignant breast tissues displayed an increased intensity over wavelength range of 440,510 nm and a decreased intensity over wavelength range of 510,600 nm, relative to that of non-malignant breast tissues. The diagnostic performance of the classification algorithms based on both feature extraction techniques yielded similar sensitivities and specificities of approximately 80% for discriminating between malignant and non-malignant breast tissues. While both methods yielded similar classification accuracies, the model based approach provided insight into the physiological and structural features that discriminate between malignant and non-malignant breast tissues. Lasers Surg. Med. © 2006 Wiley-Liss, Inc. [source] Application of artificial neural networks for the estimation of tumour characteristics in biological tissuesTHE INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY, Issue 3 2007Seyed Mohsen Hosseini Abstract Background Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce ,forward results'. Methods Three feed-forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back-propagation (RP) algorithm with a leave-one-out (LOO) cross-validation approach is used for training purposes. Results The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue. Conclusion There is a non-linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small. Copyright © 2007 John Wiley & Sons, Ltd. [source] | |||||||||||||