Showing papers in "Computer Methods and Programs in Biomedicine in 2013"

TL;DR: In this paper, a nonlinear analysis of EEG signal for discriminating depression patients and normal controls was performed. And the proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced.

TL;DR: A dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features and 94.08% accuracy is achieved, which is higher than the known approaches in the literature.

TL;DR: The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.

TL;DR: The results indicate that the proposed methodology is able to detect FoG events with 81.94% sensitivity, 98.74% specificity, 96.11% accuracy and 98.6% area under curve (AUC) using the signals from all sensors and the Random Forests classification algorithm.

TL;DR: Three database approaches--NoSQL, XML-enabled and native XML--are investigated to evaluate their suitability for structured clinical data and the results show that NoSQL database is the best choice for query speed, whereas XML databases are advantageous in terms of scalability, flexibility and extensibility, which are essential to cope with the characteristics of clinical data.

TL;DR: A cloud-based system for clients with mobile devices or web browsers that addresses the issues regarding the usefulness of the ECG data collected from patients themselves and has been proven to be functional, accurate and efficient.

TL;DR: The performance of cognitive load level prediction was found to be close to that of a reaction time measure, showing the feasibility of eye activity features for near-real time CLM.

TL;DR: The results indicate that the Windkessel method provides accurate estimates of wave reflection in subjects with preserved ejection fraction and the comparison with waveforms derived from Doppler ultrasound as well as recently proposed simple triangular and averaged flow waves showed that this approach may reduce variability and provide realistic results.

TL;DR: A SAS macro and R package are provided here to estimate the concordance correlation coefficient (CCC) where the design of the data involves repeated measurements by subject and observer.

TL;DR: The proposed scheme combines lossless data compression and encryption technique to embed electronic health record (EHR)/DICOM metadata, image hash, indexing keyword, doctor identification code and tamper localization information in the medical images.

TL;DR: A novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an ArtificialBee Colony for improving its performance is proposed, in order to enhance the diversity of ABC, without compromising with the solution quality.

TL;DR: It was proved that SVM classification offered significantly increased classification performance compared to the reference methods, and may be used as an auxiliary tool to differentiate between benign and malignant SPNs of CT images in future.

TL;DR: Noise is an essential part of biomedical systems and often plays a fundamental role in the performance of these systems, and in preliminary work, noise has demonstrated therapeutic potential to alleviate the effects of various diseases.

TL;DR: A new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection, which uses dynamic reject thresholds following the cost of misclassifying a sample and thecost of rejecting a sample.

TL;DR: A novel, highly discriminative HeartIndex is developed, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes of normal and CAD affected cases.

TL;DR: The results of the experiments demonstrate that the highest precision, recall and F-measure are achieved by the combination of the rank correlation coefficient for dimensionality reduction, HBGF consensus function and the SMO classifier with the polynomial kernel.

TL;DR: KmL3D is an R package that implements a version of k-means dedicated to clustering joint-trajectories that provides facilities for the management of missing values, offers several quality criteria and its graphic interface helps the user to select the best partition.

TL;DR: A combination of linear/nonlinear features from HRV signals is effective in automatic sleep staging and time-frequency features are more informative than others.

TL;DR: A comparison between hard and fuzzy clustering algorithms for thyroid diseases data set in order to find the optimal number of clusters and some recommendations are formulated to improve determining the actual number of cluster present in the data set.

TL;DR: In this article, an open-source image processing toolkit dedicated to fetal brain MR images is described and various tools are included such as: denoising, image reconstruction, super-resolution and tractography.

TL;DR: A computer-aided diagnosis system (Atheromatic) that analyzes ultrasound images and classifies them into symptomatic and asymptomatic plaques, based on a combination of discrete wavelet transform, higher order spectra (HOS) and textural features.

TL;DR: This paper presents a stroke rehabilitation (SR) system for the upper limbs, developed as an interactive virtual environment (IVE) based on a commercial 3D vision system, a humanoid robot, and devices producing ergonometric signals.

TL;DR: A new segmentation algorithm, breast mass contour segmentation, based on classical seed region growing algorithm to enhance contour of a mass from a given region of interest with ability to adjust threshold value adaptively is introduced.

TL;DR: The architectural aspects of a framework supporting real-time vision-based gesture recognition and virtual environments for fast prototyping of customized exercises for rehabilitation purposes are described.

TL;DR: A conscious effort has been made to avoid deviating into the area of automated breast cancer detection by providing a comprehensive review of research papers in this area of pectoral muscle segmentation.

TL;DR: The Survival Kit is a Fortran 90 Software intended for survival analysis using proportional hazards models and their extension to frailty models with a single response time.

TL;DR: An accurate and efficient method based on Fuzzy Neighborhood Discriminant Analysis (FNDA) is proposed for discriminant feature extraction and then extended to the channel selection problem, aiming to preserve the local geometrical and discriminant structures while taking into account the contribution of the samples to the different classes.

TL;DR: This article introduces a Bayesian analysis of the four-parameter generalized modified Weibull (GMW) distribution in presence of cure fraction, censored data and covariates and demonstrates the ability of using this model in the analysis of real data.

TL;DR: The results of this study showed that the 2D FIR filters designed based on ABC optimization can eliminate speckle noise quite well on noise added test images and intrinsically noisy ultrasound images.

TL;DR: Results indicate that anisotropy has a profound influence on the strength of current density as it causes current flow to deviate from its isotropically defined path along with diffused distribution patterns across the gray and WM.