Nonlinear dynamic analysis of mitral valve doppler signals: surrogate data analysis

In our study, the nonlinear dynamics of initial valve Doppler signals from 32 healthy and 28 patients with initial valve stenosis was evaluated using by the computation of Lyapunov exponents, correlation dimension values and surrogate data analysis Two chaotic features are compared for healthy and patient subjects It was found that the hugest Lyapunov exponent and correlation dimension values derived from patient subjects were larger than that of healthy subjects (rho In our study, the nonlinear dynamics of mitral valve Doppler signals from 32 healthy and 28 patients withmitral valve stenosis was evaluated using by the computation of Lyapunov exponents, correlation dimensionvalues and surrogate data analysis. Two chaotic features are compared for healthy and patient subjects. Itwas found that the largest Lyapunov exponent and correlation dimension values derived from patient subjectswere larger than that of healthy subjects (&rho; &lt;0.005). Surrogate data analysis was performed to determinethe chaotic dynamics of Doppler signals. It was observed that the original and surrogate data have similarspectral features. Receiver operating characteristic (ROC) curves were used to evaluate the nonlinearitycharacter Area Under the curve (AUC) values were obtained as 0.99 and 0.978 for the largest Lyapunovexponent and correlation dimension values, respectively. According to these results, Doppler signals have anonlinear dynamic property and the largest Lyapunov exponent. Correlation dimension features can be usedto detect the change in blood flow velocity of patients with mitral valve stenosis.</div

APPLICATION OF NULLATOR-NORATOR AND NULLOR MODEL TO ELECTRONIC CIRCUITS

The aim of this paper is to introduce the nullor model which is developed for active electronic circuit components. Therefore, nullor models of some electronic devices such as transistor, ideal and nonideal operational amplifiers are given. Applications of this model to negative impedance converters, gyrator, various controlled sources and the active RC circuit synthesis are presented and also the advantages of using this model are discussed

A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

Ultrasound imaging is an imaging technique for early detection of breast cancer. Breast Imaging Reporting and Data System (BI-RADS) lexicon, developed by The American College of Radiology, provides a standard for expert doctors to interpret the ultrasound images of breast cancer. This standard describes the features to classify the tumour as benign or malignant and it also categorizes the biopsy requirement as a percentage. Biopsy is an invasive method that doctors use for diagnosis of breast cancer. Computer-aided detection (CAD)/diagnosis systems that are designed to include the feature standards used in benign/malignant classification help the doctors in diagnosis but they do not provide enough information about the BI-RADS category of the mass. These systems classify the benign tumours with 90% biopsy possibility (BI-RADS-4) and with 2% biopsy possibility (BI-RADS-2) in the same category. There are some studies in the literature that make category classification via commonly used classifier methods but their success rates are low. In this study, a two-layer, high-success-rate classifier model based on Type-2 fuzzy inference is developed, which classifies the tumour as benign or malignant with its BI-RADS category by incorporating the opinions of the expert doctors. A 99.34% success rate in benign/malignant classification and a 92% success rate in category classification (BI-RADS 2, 3, 4, 5) were obtained in the accuracy tests. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out mass diagnosis