Biomedical Engineering: Applications, Basis and Communications

This study focuses on patients with severe obstructive sleep apnea syndrome (OSAS), and clarifies the existence of nonlinear and nonstationary properties in post-apneic snoring sounds. Many researchers have tried to discover intrinsic properties of the snoring sounds in OSAS patients for the past decades using linear frequency analysis, but no one has shown any evidence of the existence of nonlinearity and nonstationarity based on the quantitative evaluation of the post-apneic snoring sounds. In this study, Hilbert–Huang transform (HHT), which is designed for analyzing nonlinear and nonstationary signals, is adopted to generate a time-frequency map and the temporal variation in the spectral density is quantitatively evaluated using the averaged Kullback–Leibler divergence (AKL). As a result, for six OSAS patients, there is a tendency that most of AKL calculated from post-apneic snores are higher than those from non-apneic snores, which indicates that post-apneic snores are more nonstationary. In addition, we also evaluated the difference between the HHT time-frequency maps and spectrograms using short-time fourier transforms (STFT). Such analyses revealed that frequency fluctuations inherent to snoring can be adequately represented through HHT, but not with STFT. These nonlinear and nonstationary properties seem to be highly related to the physiological phenomena of OSAS, two of which are the explosive airflow after reopening of the closed airway and the collision vibration of the soft tissues.

The control of assistive robotic walkers are usually designed without considering the coupled dynamics of the user and the walker. This paper proposes a coupled dynamic modeling for human walking with a wheeled robotic walker and then control of this model. The 2D linear inverted pendulum mode (LIPM) is used to model human walking. The wheeled robotic walker is modeled as a simple wheeled subsystem moving on an even ground. To simulate the human arm, a parallel linear spring and damper set is then used to indicate the interaction forces. An optimal fixed final-state controller is used to control the walking model and to keep it balanced. To check whether the LIPM model walking is balanced, the zero-moment point position is calculated and checked to be in the support polygon. The results are validated with the experimental data of human walking along with a wheeled walker for three healthy subjects. The relative positions of center of mass, the absolute positions of the human and the walker, and the velocities are considered for comparison and discussion. The results for each subject is compared with the experiments through $L_2$ norm of the error between human and walker positions in simulation and experiments. The proposed model can be utilized in developing controllers for robotic walkers that consider the user’s dynamics.

This paper explores a novel clustering approach for multimodal Glioblastomas (GBM) characterization using the magnetic resonance image (MRI) modality. We define our segmentation problem as a linear mixture model (LMM). In every segmentation process, we generate a non-negative matrix with GLCM features from every MRI slice and a rank-two NMF (Non Negative Matrix Factorization) is applied.

Our method process in four levels of segmentation. In the first one, the LMM matrix for the whole brain was generated from FLAIR modality to extract whole tumor region, which considered as the region of Interest (ROI). In the second level, we extract the ROI from T1c modality and the LMM matrix was generated from only this ROI to extract necrosis region. The principle will be the same for the other two levels to extract the enhanced and the non-enhanced region. Quantitative and qualitative assessment over the publicly dataset from MICCAI 2015 challenge (BRATS 2015) demonstrated that the proposed method could generate a competitive efficiency for high grade Glioblastomas characterization among several competing method.

In order to highlight the performance of our method, we propose a comparative study with unsupervised segmentation methodologies (K-means, fuzzy C-means (FCM), gaussian mixture model (GMM) and hierarchical non-negative factorization (hNMF)) over the publicly BRATS 2015 dataset by computing validation metrics (the sensitivity, the dice and the specificity). The obtained results could attest the performance of the proposed algorithm compared to the unsupervised segmentation methodologies.

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.

When repositioning fractured bones in orthopedic surgery, correctness and accuracy are vital to allow the bone to regain the function and facial aesthetics of native uninjured bone. Various improvements to repositioning techniques have been proposed using points, curves, and surfaces to find correspondence between the fracture fragments. The aim of this study was to investigate the appropriate registration constraints for fractured bone reduction. One paired-point and three assisted-constraints registration methods were tested based on contralateral, landmark, and fracture line markers. The fractured proximal femur of a patient was used to compare the performance of these registration methods. Semi-automatic repositioning based on a singular value decomposition algorithm was performed to solve the problem of matching the data from two fragments. The repositioning results show that the proposed registration methods have great promise in assisting the user in defining the paired points, which is often difficult due to visibility limitations on images of fractured bone. Each of the proposed approaches was shown to yield different benefits. In terms of repositioning correctness, the use of contralateral constraints produced the smallest RMS error (1.853mm). The contralateral template method yielded the lowest fragment deviation error, but was not significantly superior to the other approaches. Fracture line-based constraints may potentially enable the relocation of fragments closest to pre-injured conditions.

In this study, antibacterial activity and long-lasting release of silver for clinical applications were achieved by utilizing silver nanoparticles on the Titania nanotubes (TNTs) through in situ polymerization of polydopamine (PDA). TNTs were synthesized with the hydrothermal process from Titania nanoparticles. Then the surface modification of TNTs was accomplished by in situ polymerization of PDA and silver ions were reduced on the PDA surface. The feature of obtained samples was characterized using transmission electron microscope (TEM), field emission scanning electron microscope (FESEM), X-ray diffraction spectroscopy (XRD), Fourier-transform infrared spectroscopy (FTIR) and atomic absorption spectrophotometry (AAS). The results showed that PDA layer formed on the synthesized anatase TNTs surface. This used as reduction agent for silver ions as well as an adhesive layer to tethering the silver nanoparticles on TNTs surface. AAS results indicated that silver ions reduction to silver nanoparticles on the TNTs surface increased from 3.1 wt.% to 9.6 wt.% in presence of PDA. Also, the results of silver release revealed that PDA worked as an adhesive layer by chelating silver nanoparticles on TNTs and slowing silver ions release rate which implying the possible long-term antibacterial activity of PDA coated TNTs. Besides that, TNTs showed 33% antibacterial activity which is half than silver loaded TNTs-PDA samples. This confirms that PDA have extraordinary effect on the antibacterial activity. This work offers a facile process for the preparation of long-lasting silver based antibacterial activity and facilitates their clinical application in the modern biomedical fields.

In this study, blood flow associated with oxygen transport in the human carotid bifurcation was investigated numerically to assess the effects of bifurcation geometry on distribution and magnitude of the wall shear stress (WSS) and Sherwood number (Sh: dimensionless oxygen wall flux) at the favourable site of atherosclerotic lesion. Three-dimensional average models of the rigid-walled carotid bifurcation were constructed to perform simulations of steady blood flow under the wall boundary condition of a constant oxygen tension. The results demonstrated that changes in the bifurcation angle significantly altered the distribution of both the WSS and the Sh, even though the pattern of the axial flow was not very sensitive to the change in bifurcation angle. Flow with large inertia bifurcated at the flow divider and created a flow recirculation zone with low WSS and Sh on the outer wall of the internal carotid artery (ICA) sinus, where atherosclerotic lesions tend to develop. A wider bifurcation angle made the area of low Sh in the ICA sinus smaller, but the level of Sh along the outer wall of the ICA sinus extremely low. Another finding was that low Sh was associated with high WSS at the region distal to the ICA sinus. The Sh distribution did not readjust as fast as the WSS in this region, as reflected by the different rates of recovery of the WSS and Sh, thus uncoupling the transport process of oxygen transport from WSS.

In this paper, a novel approach is adopted to model and predict the transmission of the viral disease dengue. We have studied the dynamics of the dengue spread from vector to human and human to vector for a homogeneous population. An authentic technique has been employed to obtain the equilibria and thus the basic reproduction number, which is defined as the average of secondary infections produced when a single infected individual is introduced into a host population where each individual is a susceptible. The nonlinear extended model is solved numerically. The stability analysis is discussed in detail in the light of the numerical results and important axioms. The epidemiological dynamics are discussed and a graphical analysis is presented for different values of the constraints.