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Computational and Mathematical Methods in Medicine

Wei Zhang, Viktoria Muravina, Robert Azencott, Zili D Chu, Michael J Paldino
Purpose: Metrics of the brain network architecture derived from resting-state fMRI have been shown to provide physiologically meaningful markers of IQ in children with epilepsy. However, traditional measures of functional connectivity (FC), specifically the Pearson correlation, assume a dominant linear relationship between BOLD time courses; this assumption may not be valid. Mutual information is an alternative measure of FC which has shown promise in the study of complex networks due to its ability to flexibly capture association of diverse forms...
2018: Computational and Mathematical Methods in Medicine
Donghai Liu, Xiaohong Chen, Dan Peng
In this paper, a cosine similarity measure between hybrid intuitionistic fuzzy sets is proposed. The aim of the paper is to investigate the cosine similarity measure with hybrid intuitionistic fuzzy information and apply it to medical diagnosis. Firstly, we construct the cosine similarity measure between hybrid intuitionistic fuzzy sets, and the relevant properties are also discussed. In order to obtain a reasonable evaluation in group decision, the weight of experts under different attributes is determined by the projection of individual decision information on the ideal decision information, where the ideal decision information is the average values of each expert's evaluation...
2018: Computational and Mathematical Methods in Medicine
Hao Liu, Puming Zhang
Epilepsy has been considered as a network-level disorder characterized by recurrent seizures, which result from network reorganization with evolution of synchronization. In this study, the brain networks were established by calculating phase synchronization based on electrocorticogram (ECoG) signals from eleven refractory epilepsy patients. Results showed that there was a significant increase of synchronization prior to seizure termination and no significant difference of the transitions of network states among the preseizure, seizure, and postseizure periods...
2018: Computational and Mathematical Methods in Medicine
Math J J M Candel
If there are no carryover effects, AB/BA crossover designs are more efficient than parallel (A/B) and extended parallel (AA/BB) group designs. This study extends these results in that (a) optimal instead of equal treatment allocation is examined, (b) allowance for treatment-dependent outcome variances is made, and (c) next to treatment effects, also treatment by period interaction effects are examined. Starting from a linear mixed model analysis, the optimal allocation requires knowledge on intraclass correlations in A and B, which typically is rather vague...
2018: Computational and Mathematical Methods in Medicine
Zhihua Huang, Minghong Li, Yuanye Ma
This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors...
2018: Computational and Mathematical Methods in Medicine
Qiang Ye, Yi Xia, Zhiming Yao
A common feature that is typical of the patients with neurodegenerative (ND) disease is the impairment of motor function, which can interrupt the pathway from cerebrum to the muscle and thus cause movement disorders. For patients with amyotrophic lateral sclerosis disease (ALS), the impairment is caused by the loss of motor neurons. While for patients with Parkinson's disease (PD) and Huntington's disease (HD), it is related to the basal ganglia dysfunction. Previously studies have demonstrated the usage of gait analysis in characterizing the ND patients for the purpose of disease management...
2018: Computational and Mathematical Methods in Medicine
Shuo Huang, Le Cheng, Bin Zhu, Ping Zhou, Yu Sun, Bing Zhang, Suiren Wan
The clarity improvement and the noise suppression of digital subtraction angiography (DSA) images are very important. However, the common methods are very complicated. An image time-domain integration method is proposed in this study, which is based on the blood flow periodicity. In this method, the images of the first cardiac cycle after the injection of the contrast agent are integrated to obtain the time-domain integration image. This method can be used independently or as a postprocessing method of the denoising method on the signal image...
2018: Computational and Mathematical Methods in Medicine
Martin Bušík, Martin Slavík, Ivan Cimrák
Modelling of cell flow for biomedical applications relies in many cases on the correct description of fluid-structure interaction between the cell membrane and the surrounding fluid. We analyse the coupling of the lattice-Boltzmann method for the fluid and the spring network model for the cells. We investigate the bare friction parameter of fluid-structure interaction that is mediated via dissipative coupling. Such coupling mimics the no-slip boundary condition at the interface between the fluid and object...
2018: Computational and Mathematical Methods in Medicine
Jinxiang Xi, Mohamed Talaat, Hesham Tanbour, Khaled Talaat
Unique features exist in acinar units such as multiple alveoli, interalveolar septal walls, and pores of Kohn. However, the effects of such features on airflow and particle deposition remain not well quantified due to their structural complexity. This study aims to numerically investigate particle dynamics in acinar models with interalveolar septal walls and pores of Kohn. A simplified 4-alveoli model with well-defined geometries and a physiologically realistic 45-alveoli model was developed. A well-validated Lagrangian tracking model was used to simulate particle trajectories in the acinar models with rhythmically expanding and contracting wall motions...
2018: Computational and Mathematical Methods in Medicine
Liyuan Zhang, Jiashi Zhao, Huamin Yang, Zhengang Jiang, Qingliang Li
To solve the problem of scoliosis recognition without a labeled dataset, an unsupervised method is proposed by combining the cascade gentle AdaBoost (CGAdaBoost) classifier and distance regularized level set evolution (DRLSE). The main idea of the proposed method is to establish the relationship between individual vertebrae and the whole spine with vertebral centroids. Scoliosis recognition can be transferred into automatic vertebral detection and segmentation processes, which can avoid the manual data-labeling processing...
2018: Computational and Mathematical Methods in Medicine
Ioannis Tsougos, Alexandros Vamvakas, Constantin Kappas, Ioannis Fezoulidis, Katerina Vassiou
Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype...
2018: Computational and Mathematical Methods in Medicine
Kazeem Oyeyemi Oyebode, Shengzhi Du, Barend Jacobus van Wyk, Karim Djouani
Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation fields of application, such as the medical field. The graph cut energy function has a parameter that is tuned to ensure that the output is neither oversegmented (shrink bias) nor undersegmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation...
2018: Computational and Mathematical Methods in Medicine
Haihua Jiang, Bin Hu, Zhenyu Liu, Gang Wang, Lan Zhang, Xiaoyu Li, Huanyu Kang
Early intervention for depression is very important to ease the disease burden, but current diagnostic methods are still limited. This study investigated automatic depressed speech classification in a sample of 170 native Chinese subjects (85 healthy controls and 85 depressed patients). The classification performances of prosodic, spectral, and glottal speech features were analyzed in recognition of depression. We proposed an ensemble logistic regression model for detecting depression (ELRDD) in speech. The logistic regression, which was superior in recognition of depression, was selected as the base classifier...
2018: Computational and Mathematical Methods in Medicine
Qianqian Wang, Fang'ai Liu, Shuning Xing, Xiaohui Zhao
Click-through rate prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, takes advantage of the attention mechanism in neural network modelling, and improves FM to make feature interactions contribute differently to the prediction...
2018: Computational and Mathematical Methods in Medicine
Ralph Brinks
Chronic diseases impose a huge burden for mankind. Recently, a mathematical relation between the incidence and prevalence of a chronic disease in terms of a differential equation has been described. In this article, we study the characteristics of this differential equation. Furthermore, we prove the ill-posedness of a related inverse problem arising in chronic disease epidemiology. An example application for the inverse problem about type 1 diabetes in German women aged up to 35 years is given.
2018: Computational and Mathematical Methods in Medicine
Ming Liu, Zhenze Wang, Anqiang Sun, Xiaoyan Deng
The typical helical flow within the human arterial system is widely used when designing cardiovascular devices, as this helical flow can be generated using the "crossed limbs" strategy of the bifurcated stent graft (BSG) and enhanced by the tapered structure of arteries. Here, we propose the use of a deflected blended bifurcated stent graft (BBSG) with various tapers, using conventional blended BSGs with the same degree of taper as a comparison. Hemodynamic performances, including helical strength and wall shear stress- (WSS-) based indicators, were assessed...
2018: Computational and Mathematical Methods in Medicine
Bingtao Zhang, Tao Lei, Hong Liu, Hanshu Cai
Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed...
2018: Computational and Mathematical Methods in Medicine
Yifu Xu, Bin Yan, Jingfang Zhang, Jian Chen, Lei Zeng, Linyuang Wang
Background: Dual-energy computed tomography (DECT) has been widely used due to improved substances identification from additional spectral information. The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method. Objective: The aim of this work is to develop and validate a data-driven algorithm for the image-based decomposition problem. Methods: A deep neural net, consisting of a fully convolutional net (FCN) and a fully connected net, is proposed to solve the material decomposition problem...
2018: Computational and Mathematical Methods in Medicine
Michael D Vasilakakis, Dimitris K Iakovidis, Evaggelos Spyrou, Anastasios Koulaouzidis
Wireless Capsule Endoscopy (WCE) is a noninvasive diagnostic technique enabling the inspection of the whole gastrointestinal (GI) tract by capturing and wirelessly transmitting thousands of color images. Proprietary software "stitches" the images into videos for examination by accredited readers. However, the videos produced are of large length and consequently the reading task becomes harder and more prone to human errors. Automating the WCE reading process could contribute in both the reduction of the examination time and the improvement of its diagnostic accuracy...
2018: Computational and Mathematical Methods in Medicine
Myung Hun Jang, Se Jin Ahn, Jun Woo Lee, Min-Hyung Rhee, Dasom Chae, Jinmi Kim, Myung Jun Shin
Objective: The purpose of this study was to establish the validity and reliability of the newly developed surface electromyography (sEMG) device (PSL-EMG-Tr1) compared with a conventional sEMG device (BTS-FREEEMG1000). Methods: In total, 20 healthy participants (10 males, age 30.3 ± 2.9 years; 10 females, age 22.3 ± 2.7 years) were recruited. EMG signals were recorded simultaneously on two devices during three different isometric contractions (maximal voluntary isometric contraction (MVIC, 40% MVIC, 80% MVIC))...
2018: Computational and Mathematical Methods in Medicine
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