Computational and Mathematical Methods in Medicine
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Open Access journal
ISSN (Print) 1748-670X - ISSN (Online) 1748-6718
Published by Hindawi [333 journals]
- Femoral Neck Strain during Maximal Contraction of Isolated Hip-Spanning
Abstract: The aim of the study was to investigate femoral neck strain during maximal isometric contraction of the hip-spanning muscles. The musculoskeletal and the femur finite-element models from an elderly white woman were taken from earlier studies. The hip-spanning muscles were grouped by function in six hip-spanning muscle groups. The peak hip and knee moments in the model were matched to corresponding published measurements of the hip and knee moments during maximal isometric exercises about the hip and the knee in elderly participants. The femoral neck strain was calculated using full activation of the agonist muscles at fourteen physiological joint angles. The of the femoral neck volume exceeded the 90th percentile of the strain distribution across the 84 studied scenarios. Hip extensors, flexors, and abductors generated the highest tension in the proximal neck (2727 με), tension (986 με) and compression (−2818 με) in the anterior and posterior neck, and compression (−2069 με) in the distal neck, respectively. Hip extensors and flexors generated the highest neck strain per unit of joint moment (63–67 με·m·N−1) at extreme hip angles. Therefore, femoral neck strain is heterogeneous and muscle contraction and posture dependent.
PubDate: Wed, 22 Mar 2017 07:29:34 +000
- Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary
Abstract: Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.
PubDate: Tue, 21 Mar 2017 06:28:06 +000
- Using Agent-Based Models to Develop Public Policy about Food Behaviours:
Future Directions and Recommendations
Abstract: Most adults are overweight or obese in many western countries. Several population-level interventions on the physical, economical, political, or sociocultural environment have thus attempted to achieve a healthier weight. These interventions have involved different weight-related behaviours, such as food behaviours. Agent-based models (ABMs) have the potential to help policymakers evaluate food behaviour interventions from a systems perspective. However, fully realizing this potential involves a complex procedure starting with obtaining and analyzing data to populate the model and eventually identifying more efficient cross-sectoral policies. Current procedures for ABMs of food behaviours are mostly rooted in one technique, often ignore the food environment beyond home and work, and underutilize rich datasets. In this paper, we address some of these limitations to better support policymakers through two contributions. First, via a scoping review, we highlight readily available datasets and techniques to deal with these limitations independently. Second, we propose a three steps’ process to tackle all limitations together and discuss its use to develop future models for food behaviours. We acknowledge that this integrated process is a leap forward in ABMs. However, this long-term objective is well-worth addressing as it can generate robust findings to effectively inform the design of food behaviour interventions.
PubDate: Tue, 21 Mar 2017 00:00:00 +000
- Computational and Mathematical Methods in Cardiovascular Diseases
PubDate: Sun, 19 Mar 2017 00:00:00 +000
- Comparison of Baseline Wander Removal Techniques considering the
Preservation of ST Changes in the Ischemic ECG: A Simulation Study
Abstract: The most important ECG marker for the diagnosis of ischemia or infarction is a change in the ST segment. Baseline wander is a typical artifact that corrupts the recorded ECG and can hinder the correct diagnosis of such diseases. For the purpose of finding the best suited filter for the removal of baseline wander, the ground truth about the ST change prior to the corrupting artifact and the subsequent filtering process is needed. In order to create the desired reference, we used a large simulation study that allowed us to represent the ischemic heart at a multiscale level from the cardiac myocyte to the surface ECG. We also created a realistic model of baseline wander to evaluate five filtering techniques commonly used in literature. In the simulation study, we included a total of 5.5 million signals coming from 765 electrophysiological setups. We found that the best performing method was the wavelet-based baseline cancellation. However, for medical applications, the Butterworth high-pass filter is the better choice because it is computationally cheap and almost as accurate. Even though all methods modify the ST segment up to some extent, they were all proved to be better than leaving baseline wander unfiltered.
PubDate: Wed, 08 Mar 2017 06:49:36 +000
- Topological Measurements of DWI Tractography for Alzheimer’s
Abstract: Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimer’s disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%–99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.
PubDate: Thu, 02 Mar 2017 07:28:18 +000
- Cardioprotection Effects of Sevoflurane by Regulating the Pathway of
Neuroactive Ligand-Receptor Interaction in Patients Undergoing Coronary
Artery Bypass Graft Surgery
Abstract: This study was designed to identify attractor modules and further reveal the potential biological processes involving in sevoflurane-induced anesthesia in patients treated with coronary artery bypass graft (CABG) surgery. Microarray profile data (ID: E-GEOD-4386) on atrial samples obtained from patients receiving anesthetic gas sevoflurane prior to and following CABG procedure were downloaded from EMBL-EBI database for further analysis. Protein-protein interaction (PPI) networks of baseline and sevoflurane groups were inferred and reweighted according to Spearman correlation coefficient (SCC), followed by systematic modules inference using clique-merging approach. Subsequently, attract method was utilized to explore attractor modules. Finally, pathway enrichment analyses for genes in the attractor modules were implemented to illuminate the biological processes in sevoflurane group. Using clique-merging approach, 27 and 36 modules were obtained from the PPI networks of baseline and sevoflurane-treated samples, respectively. By comparing with the baseline condition, 5 module pairs with the same gene composition were identified. Subsequently, 1 out of 5 modules was identified as an attractor based on attract method. Additionally, pathway analysis indicated that genes in the attractor module were associated with neuroactive ligand-receptor interaction. Accordingly, sevoflurane might exert important functions in cardioprotection in patients following CABG, partially through regulating the pathway of neuroactive ligand-receptor interaction.
PubDate: Tue, 28 Feb 2017 14:22:05 +000
- Fast Parabola Detection Using Estimation of Distribution Algorithms
Abstract: This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about on synthetic images and on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
PubDate: Tue, 21 Feb 2017 00:00:00 +000
- Feature Extraction and Classification of EHG between Pregnancy and Labour
Group Using Hilbert-Huang Transform and Extreme Learning Machine
Abstract: Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.
PubDate: Sun, 19 Feb 2017 13:53:23 +000
- Automated Classification of Severity in Cardiac Dyssynchrony Merging
Clinical Data and Mechanical Descriptors
Abstract: Cardiac resynchronization therapy (CRT) improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders. However, a high percentage of subjects under CRT (20%–30%) do not show any improvement. Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response. This work proposes an automated classification model of severity in ventricular contraction dyssynchrony. The model includes clinical data such as left ventricular ejection fraction (LVEF), QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction. A control group of 33 normal volunteers ( years, LVEF of ) and a HF group of 42 subjects ( years, LVEF < 35%) were studied. The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively. For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively. These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.
PubDate: Sun, 19 Feb 2017 07:15:47 +000
- Noise Attenuation Estimation for Maximum Length Sequences in Deconvolution
Process of Auditory Evoked Potentials
Abstract: The use of maximum length sequence (m-sequence) has been found beneficial for recovering both linear and nonlinear components at rapid stimulation. Since m-sequence is fully characterized by a primitive polynomial of different orders, the selection of polynomial order can be problematic in practice. Usually, the m-sequence is repetitively delivered in a looped fashion. Ensemble averaging is carried out as the first step and followed by the cross-correlation analysis to deconvolve linear/nonlinear responses. According to the classical noise reduction property based on additive noise model, theoretical equations have been derived in measuring noise attenuation ratios (NARs) after the averaging and correlation processes in the present study. A computer simulation experiment was conducted to test the derived equations, and a nonlinear deconvolution experiment was also conducted using order 7 and 9 m-sequences to address this issue with real data. Both theoretical and experimental results show that the NAR is essentially independent of the m-sequence order and is decided by the total length of valid data, as well as stimulation rate. The present study offers a guideline for m-sequence selections, which can be used to estimate required recording time and signal-to-noise ratio in designing m-sequence experiments.
PubDate: Sun, 19 Feb 2017 00:00:00 +000
- Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach
Abstract: In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an -measure of 81.24%.
PubDate: Sun, 19 Feb 2017 00:00:00 +000
- Mathematical Modelling of Immune Parameters in the Evolution of Severe
Abstract: Aims. Predicting the risk of severity at an early stage in an individual patient will be invaluable in preventing morbidity and mortality caused by dengue. We hypothesized that such predictions are possible by analyzing multiple parameters using mathematical modeling. Methodology. Data from 11 adult patients with dengue fever (DF) and 25 patients with dengue hemorrhagic fever (DHF) were analyzed. Multivariate statistical analysis was performed to study the characteristics and interactions of parameters using dengue NS1 antigen levels, dengue IgG antibody levels, platelet counts, and lymphocyte counts. Fuzzy logic fundamentals were used to map the risk of developing severe forms of dengue. The cumulative effects of the parameters were incorporated using the Hamacher and the OWA operators. Results. The operator classified the patients according to the severity level during the time period of 96 hours to 120 hours after the onset of fever. The accuracy ranged from 53% to 89%. Conclusion. The results show a robust mathematical model that explains the evolution from dengue to its serious forms in individual patients. The model allows prediction of severe cases of dengue which could be useful for optimal management of patients during a dengue outbreak. Further analysis of the model may also deepen our understanding of the pathways towards severe illness.
PubDate: Wed, 15 Feb 2017 00:00:00 +000
- Optimally Repeatable Kinetic Model Variant for Myocardial Blood Flow
Measurements with 82Rb PET
Abstract: Purpose. Myocardial blood flow (MBF) quantification with positron emission tomography (PET) is gaining clinical adoption, but improvements in precision are desired. This study aims to identify analysis variants producing the most repeatable MBF measures. Methods. 12 volunteers underwent same-day test-retest rest and dipyridamole stress imaging with dynamic PET, from which MBF was quantified using 1-tissue-compartment kinetic model variants: () blood-pool versus uptake region sampled input function (Blood/Uptake-ROI), () dual spillover correction (SOC-On/Off), () right blood correction (RBC-On/Off), () arterial blood transit delay (Delay-On/Off), and () distribution volume (DV) constraint (Global/Regional-DV). Repeatability of MBF, stress/rest myocardial flow reserve (MFR), and stress/rest MBF difference (ΔMBF) was assessed using nonparametric reproducibility coefficients ( = 1.45 × interquartile range). Results. MBF using SOC-On, RVBC-Off, Blood-ROI, Global-DV, and Delay-Off was most repeatable for combined rest and stress: = 0.21 mL/min/g (15.8%). Corresponding MFR and ΔMBF were 0.42 (20.2%) and 0.24 mL/min/g (23.5%). MBF repeatability improved with SOC-On at stress () and tended to improve with RBC-Off at both rest and stress (). DV and ROI did not significantly influence repeatability. The Delay-On model was overdetermined and did not reliably converge. Conclusion. MBF and MFR test-retest repeatability were the best with dual spillover correction, left atrium blood input function, and global DV.
PubDate: Mon, 13 Feb 2017 06:39:52 +000
- A Psychometric Tool for a Virtual Reality Rehabilitation Approach for
Abstract: Dyslexia is a chronic problem that affects the life of subjects and often influences their life choices. The standard rehabilitation methods all use a classic paper and pencil training format but these exercises are boring and demanding for children who may have difficulty in completing the treatments. It is important to develop a new rehabilitation program that would help children in a funny and engaging way. A Wii-based game was developed to demonstrate that a short treatment with an action video game, rather than phonological or orthographic training, may improve the reading abilities in dyslexic children. According to the results, an approach using cues in the context of a virtual environment may represent a promising tool to improve attentional skills. On the other hand, our results do not demonstrate an immediate effect on reading performance, suggesting that a more prolonged protocol may be a future direction.
PubDate: Mon, 13 Feb 2017 00:00:00 +000
- Mixed Total Variation and Regularization Method for Optical Tomography
Based on Radiative Transfer Equation
Abstract: Optical tomography is an emerging and important molecular imaging modality. The aim of optical tomography is to reconstruct optical properties of human tissues. In this paper, we focus on reconstructing the absorption coefficient based on the radiative transfer equation (RTE). It is an ill-posed parameter identification problem. Regularization methods have been broadly applied to reconstruct the optical coefficients, such as the total variation (TV) regularization and the regularization. In order to better reconstruct the piecewise constant and sparse coefficient distributions, TV and norms are combined as the regularization. The forward problem is discretized with the discontinuous Galerkin method on the spatial space and the finite element method on the angular space. The minimization problem is solved by a Jacobian-based Levenberg-Marquardt type method which is equipped with a split Bregman algorithms for the regularization. We use the adjoint method to compute the Jacobian matrix which dramatically improves the computation efficiency. By comparing with the other imaging reconstruction methods based on TV and regularizations, the simulation results show the validity and efficiency of the proposed method.
PubDate: Thu, 09 Feb 2017 14:15:16 +000
- 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance
Abstract: Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images’ inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels’ appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.
PubDate: Thu, 09 Feb 2017 00:00:00 +000
- Modeling the Parasitic Filariasis Spread by Mosquito in Periodic
Abstract: In this paper a mosquito-borne parasitic infection model in periodic environment is considered. Threshold parameter is given by linear next infection operator, which determined the dynamic behaviors of system. We obtain that when , the disease-free periodic solution is globally asymptotically stable and when by Poincaré map we obtain that disease is uniformly persistent. Numerical simulations support the results and sensitivity analysis shows effects of parameters on , which provided references to seek optimal measures to control the transmission of lymphatic filariasis.
PubDate: Wed, 08 Feb 2017 09:20:13 +000
- Complementary Keratoconus Indices Based on Topographical Interpretation of
Biomechanical Waveform Parameters: A Supplement to Established Keratoconus
Abstract: Purpose. To build new models with the Ocular Response Analyzer (ORA) waveform parameters to create new indices analogous to established topographic keratoconus indices. Method. Biomechanical, tomographic, and topographic measurements of 505 eyes from the Homburger Keratoconus Centre were included. Thirty-seven waveform parameters (WF) were derived from the biomechanical measurement with the ORA. Area under curve (ROC, receiver operating characteristic) was used to quantify the screening performance. A logistic regression analysis was used to create two new keratoconus prediction models based on these waveform parameters to resample the clinically established keratoconus indices from Pentacam and TMS-5. Results. ROC curves show the best results for the waveform parameters p1area, p2area, , , dive1, mslew1, aspect1, aplhf, and dslope1. The new keratoconus prediction model to resample the Pentacam topographic keratoconus index (TKC) was = −4.068 + 0.002 × p2area − 0.005 × dive1 − 0.01 × h1 − 2.501 × aplhf, which achieves a sensitivity of 90.3% and specificity of 89.4%; to resample the TMS-5 keratoconus classification index (KCI) it was = −3.606 + 0.002 × p2area, which achieves a sensitivity of 75.4% and a specificity of 81.8%. Conclusion. In addition to the biomechanically provided Keratoconus Index two new indices which were based on the topographic gold standards (either Pentacam or TMS-5) were created. Of course, these do not replace the original topographic measurement.
PubDate: Tue, 07 Feb 2017 09:10:30 +000
- Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography
Images Using Level Set
Abstract: Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was .
PubDate: Tue, 07 Feb 2017 00:00:00 +000
- Retinal Image Denoising via Bilateral Filter with a Spatial Kernel of
Optimally Oriented Line Spread Function
Abstract: Filtering belongs to the most fundamental operations of retinal image processing and for which the value of the filtered image at a given location is a function of the values in a local window centered at this location. However, preserving thin retinal vessels during the filtering process is challenging due to vessels’ small area and weak contrast compared to background, caused by the limited resolution of imaging and less blood flow in the vessel. In this paper, we present a novel retinal image denoising approach which is able to preserve the details of retinal vessels while effectively eliminating image noise. Specifically, our approach is carried out by determining an optimal spatial kernel for the bilateral filter, which is represented by a line spread function with an orientation and scale adjusted adaptively to the local vessel structure. Moreover, this approach can also be served as a preprocessing tool for improving the accuracy of the vessel detection technique. Experimental results show the superiority of our approach over state-of-the-art image denoising techniques such as the bilateral filter.
PubDate: Sun, 05 Feb 2017 14:22:45 +000
- Development of a Patient-Specific Finite Element Model for Predicting
Implant Failure in Pelvic Ring Fracture Fixation
Abstract: Introduction. The main purpose of this study is to develop an efficient technique for generating FE models of pelvic ring fractures that is capable of predicting possible failure regions of osteosynthesis with acceptable accuracy. Methods. Patient-specific FE models of two patients with osteoporotic pelvic fractures were generated. A validated FE model of an uninjured pelvis from our previous study was used as a master model. Then, fracture morphologies and implant positions defined by a trauma surgeon in the preoperative CT were manually introduced as 3D splines to the master model. Four loading cases were used as boundary conditions. Regions of high stresses in the models were compared with actual locations of implant breakages and loosening identified from follow-up X-rays. Results. Model predictions and the actual clinical outcomes matched well. For Patient A, zones of increased tension and maximum stress coincided well with the actual locations of implant loosening. For Patient B, the model predicted accurately the loosening of the implant in the anterior region. Conclusion. Since a significant reduction in time and labour was achieved in our mesh generation technique, it can be considered as a viable option to be implemented as a part of the clinical routine to aid presurgical planning and postsurgical management of pelvic ring fracture patients.
PubDate: Wed, 01 Feb 2017 00:00:00 +000
- Comparison of Different Features and Classifiers for Driver Fatigue
Detection Based on a Single EEG Channel
Abstract: Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different.
PubDate: Tue, 31 Jan 2017 13:33:03 +000
- Detection of Impaired Cerebral Autoregulation Using Selected Correlation
Analysis: A Validation Study
Abstract: Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.
PubDate: Tue, 31 Jan 2017 00:00:00 +000
- Predictive Behavior of a Computational Foot/Ankle Model through Artificial
Abstract: Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.
PubDate: Mon, 30 Jan 2017 00:00:00 +000
- An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel
Extreme Learning Machine for Medical Diagnosis
Abstract: In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, -mean, -measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.
PubDate: Thu, 26 Jan 2017 14:19:30 +000
- Are Health Videos from Hospitals, Health Organizations, and Active Users
Available to Health Consumers' An Analysis of Diabetes Health Video
Ranking in YouTube
Abstract: Health consumers are increasingly using the Internet to search for health information. The existence of overloaded, inaccurate, obsolete, or simply incorrect health information available on the Internet is a serious obstacle for finding relevant and good-quality data that actually helps patients. Search engines of multimedia Internet platforms are thought to help users to find relevant information according to their search. But, is the information recovered by those search engines from quality sources' Is the health information uploaded from reliable sources, such as hospitals and health organizations, easily available to patients' The availability of videos is directly related to the ranking position in YouTube search. The higher the ranking of the information is, the more accessible it is. The aim of this study is to analyze the ranking evolution of diabetes health videos on YouTube in order to discover how videos from reliable channels, such as hospitals and health organizations, are evolving in the ranking. The analysis was done by tracking the ranking of 2372 videos on a daily basis during a 30-day period using 20 diabetes-related queries. Our conclusions are that the current YouTube algorithm favors the presence of reliable videos in upper rank positions in diabetes-related searches.
PubDate: Tue, 24 Jan 2017 00:00:00 +000
- Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi
Abstract: Small interfering RNAs (siRNAs) induce posttranscriptional gene silencing in various organisms. siRNAs targeted to different positions of the same gene show different effectiveness; hence, predicting siRNA activity is a crucial step. In this paper, we developed and evaluated a powerful tool named “siRNApred” with a new mixed feature set to predict siRNA activity. To improve the prediction accuracy, we proposed 2-3NTs as our new features. A Random Forest siRNA activity prediction model was constructed using the feature set selected by our proposed Binary Search Feature Selection (BSFS) algorithm. Experimental data demonstrated that the binding site of the Argonaute protein correlates with siRNA activity. “siRNApred” is effective for selecting active siRNAs, and the prediction results demonstrate that our method can outperform other current siRNA activity prediction methods in terms of prediction accuracy.
PubDate: Tue, 24 Jan 2017 00:00:00 +000
- A Novel Computer-Aided Approach for Parametric Investigation of Custom
Design of Fracture Fixation Plates
Abstract: The present study proposes an integrated computer-aided approach combining femur surface modeling, fracture evidence recover plate creation, and plate modification in order to conduct a parametric investigation of the design of custom plate for a specific patient. The study allows for improving the design efficiency of specific plates on the patients’ femur parameters and the fracture information. Furthermore, the present approach will lead to exploration of plate modification and optimization. The three-dimensional (3D) surface model of a detailed femur and the corresponding fixation plate were represented with high-level feature parameters, and the shape of the specific plate was recursively modified in order to obtain the optimal plate for a specific patient. The proposed approach was tested and verified on a case study, and it could be helpful for orthopedic surgeons to design and modify the plate in order to fit the specific femur anatomy and the fracture information.
PubDate: Thu, 19 Jan 2017 14:14:02 +000
- Bionic Vision-Based Intelligent Power Line Inspection System
Abstract: Detecting the threats of the external obstacles to the power lines can ensure the stability of the power system. Inspired by the attention mechanism and binocular vision of human visual system, an intelligent power line inspection system is presented in this paper. Human visual attention mechanism in this intelligent inspection system is used to detect and track power lines in image sequences according to the shape information of power lines, and the binocular visual model is used to calculate the 3D coordinate information of obstacles and power lines. In order to improve the real time and accuracy of the system, we propose a new matching strategy based on the traditional SURF algorithm. The experimental results show that the system is able to accurately locate the position of the obstacles around power lines automatically, and the designed power line inspection system is effective in complex backgrounds, and there are no missing detection instances under different conditions.
PubDate: Thu, 19 Jan 2017 10:58:04 +000