A model to identify high crash road segments with the dynamic segmentation method.

Currently, high social and economic costs in addition to physical and mental consequences put road safety among most important issues. This paper aims at presenting a novel approach, capable of identifying the location as well as the length of high crash road segments. It focuses on the location of accidents occurred along the road and their effective regions.

Predicting termination of paroxysmal atrial fibrillation using empirical mode decomposition of the atrial activity and statistical features of the heart rate variability.

This paper presents an algorithm for predicting termination of paroxysmal atrial fibrillation (AF) attacks using features extracted from the atrial activity (AA) and heart rate variability (HRV) signals. First, AA signal was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition method. Then, power spectrums of the AA and its IMFs (second, third, and forth components) were obtained, and the peak frequency of the power spectral densities were extracted.

Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation.

This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. This method consists of four steps: Preprocessing, feature extraction, feature reduction, and classification.

Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders.

Heart murmurs are pathological sounds produced by turbulent blood flow due to certain cardiac defects such as valves disorders. Detection of murmurs via auscultation is a task that depends on the proficiency of physician. There are many cases in which the accuracy of detection is questionable.

Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal.

Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal.

In this paper, an effective paroxysmal atrial fibrillation (PAF) prediction algorithm is presented, which is based on analysis of the heart rate variability (HRV) signal. The proposed method consists of a preprocessing step for QRS detection and HRV signal extraction. In the next step, several features which can be used as markers for the prediction of PAF are extracted from the HRV signal.

A feedback retina model for improving medical images fusion.

Image fusion has become a powerful technique for increasing the interpretation quality of images in medical applications. The diagnostic potential of brain positron emission tomography (PET) imaging is limited by low spatial resolution. For solving this problem, the high-frequency part of the MRI, which would be unrecoverable by the set PET acquisition system, is extracted and added to the PET image.

Detection of atrial fibrillation episodes using SVM.

This paper explains an atrial fibrillation (AF) detection algorithm, which consists of a linear discriminant analysis (LDA) based feature reduction scheme and a support vector machine (SVM) based classifier. Initially nine features were extracted from the input episodes each containing 32 RR intervals by linear and nonlinear methods. Next, to improve the learning efficiency of the classifier and to reduce the learning time, these features are reduced to 4 features by LDA.

Content-based medical image classification using a new hierarchical merging scheme.

Automatic medical image classification is a technique for assigning a medical image to a class among a number of image categories. Due to computational complexity, it is an important task in the content-based image retrieval (CBIR). In this paper, we propose a hierarchical medical image classification method including two levels using a perfect set of various shape and texture features.

A transformation based method to design ultrasound array.

In medical ultrasound imaging, the desired lateral field distribution at each focal distance can be obtained by optimal apodization. On the other hand, the lateral field is a function of focal distance. Hence, finding the optimal apodization is a very arduous process.

A hybrid algorithm for medical image registration.

Registration is a process to align different acquired images of the same subject. A major problem in this field is to register images captured by different imaging systems. These images have different gray values so simple methods like correlation are not applicable.