Showing papers in "Computer Methods and Programs in Biomedicine in 2014"

TL;DR: Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis that includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection.

TL;DR: This paper presents how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio, a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user.

TL;DR: A computer aided classification method in computed tomography (CT) images of lungs developed using artificial neural network, which shows that the feed forward back propagation network gives better classification.

TL;DR: New supervised feature selection methods based on hybridization of Particle Swarm Optimization, PSO based Relative Reduct andPSO based Quick Reduct are presented for the diseases diagnosis, proving the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques.

TL;DR: It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance and will be used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier.

TL;DR: The results show that the proposed automatic approach for automatic cervical cancer cell segmentation and classification yields very good performance and is better than its counterparts.

TL;DR: Heat transfer and flow analysis for a non-Newtonian third grade nanofluid flow in porous medium of a hollow vessel in presence of magnetic field are simulated analytically and numerically and show that increasing the thermophoresis parameter caused an increase in temperature values in whole domain and a increase in nanoparticles concentration just near the inner wall of vessel.

TL;DR: This work outlines a five-level ECG signal quality classification algorithm that was trained to perform the classification and tested on a simulated dataset and was validated using data from the MIT-BIH arrhythmia database (MITDB).

TL;DR: The objective was to develop an open-source and multi-platform framework to read, write, modify and visualize data from any motion analysis systems using standard (C3D) and proprietary file formats (used by many companies producing motion capture systems).

TL;DR: In this article, a hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection(SBS).

TL;DR: A novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans that significantly reduces the number of false positives in nodule candidates.

TL;DR: A set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views and a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms.

TL;DR: This study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.

TL;DR: The automation of the human body parts motion monitoring, its analysis in relation to the psychomotor exercise indicated to the patient, and the storage of the result of the realization of a set of exercises free the rehabilitation experts of doing such demanding tasks.

TL;DR: New combined methods to classify normal and epileptic seizure EEG signals using wavelet transform (WT), phase-space reconstruction (PSR), and Euclidean distance (ED) based on a neural network with weighted fuzzy membership functions (NEWFM).

TL;DR: A random forest classifier (RFC) approach is proposed to diagnose lymph diseases and it was observed that GA-RFC achieved the highest classification accuracy of 92.2%.

TL;DR: This paper designs a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes and proposes infrastructure level mechanisms to provide dynamic resource elasticity for CBIHCS.

TL;DR: A novel method for QRS detection in electrocardiograms (ECG) based on the S-Transform, a new time frequency representation (TFR), which provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum.

TL;DR: Experimental results show that feature extraction using the FWHVA is faster than that of SampEn and FFT and mean strength feature associated with ictal EEG is significant higherthan that of healthy and inter-ictalEEGs.

TL;DR: The results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.

TL;DR: The results demonstrated that VE platforms were associated with different patterns in emotional responses and task performance, and suggest that different VE systems may be appropriate for different scientific purposes when studying stress reactivity using emotionally evocative tasks.

TL;DR: This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres, and observed that the epileptic focus localization, data sampling frequency, testing duration, number of seizures in testing, type of machine learning, and preictal time influence significantly the prediction performance.

TL;DR: It is demonstrated that versatile texture and color based descriptors of image regions corresponding to various anomalies of the gastrointestinal tract allows their accurate detection of pathologies in a sequence of WCE frames.

TL;DR: An intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease and a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection.

TL;DR: An automated method for the detection of new vessels in retinal images is described, using the standard line operator and a novel modified line operator to reduce false responses to non-vessel edges.

TL;DR: The microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented and several scale-adapted region descriptors are introduced to characterize these blob regions.

TL;DR: The effectiveness and possible use of the VR to recover the proprioception of stroke patients was showed and subjects were significantly improved in conventional behavioral tests after training.

TL;DR: It is found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost and the proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer.

TL;DR: Experimental results demonstrated that hand gesture guidance was able to effectively guide the surgical robot, and the robot-assisted implementation was found to improve the accuracy of needle insertion, and this human-robot cooperative mechanism is a promising approach for precise transcutaneous ablation therapy.

TL;DR: The context, experimental considerations, methods, and preliminary findings of two public datasets created by the CYBHi team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers are described.