فصلنامه مهندسی پزشکی زیستی
سال هفدهم شماره 1 (بهار 1402)

Today, usage of brain-computer interface systems based on steady-state visual evoked potentials (SSVEPs) has been increased due to some advantages such as acceptable accuracy and minimal need for user training. Steady-state visual potentials are one of the most important patterns used in BCI systems, which are generated in the occipital region of the brain by visual stimulation between 6 and 60 Hz. One of the effective methods for extracting the SSVEP frequency in BCI systems is called the Multiway Correlation Coefficient Analysis (MCCA) method, which is a tensorized version of the classical Correlation Coefficient Analysis (CCA) method and is based on multidimensional data.In this paper, inspired by the MCCA method, two new algorithms (PARAFAC-CCA and C-PARAFAC-CCA) have been proposed using the combination of CCA and PARAFAC decomposition. The purpose of the proposed algorithms is to improve the initial reference signal and achieve higher accuracy in SSVEP frequency detection in BCI systems. In the PARAFAC-CCA algorithm, after performing the PARAFAC decomposition on the multidimensional training data and obtaining the time component, the CCA method is implemented between the obtained time component and the sine-cosine reference signal, and the optimal reference signal is made from its output. Finally, the MLR algorithm is used between the EEG test data and the optimal reference signal in order to achieve the target frequency. The general steps of the C-PARAFAC-CCA algorithm are also similar to PARAFAC-CCA, with the difference that in the calculation of the time component, constrained PARAFAC is used in such a way that in each step of the ALS algorithm, CCA is applied once and the time component is improved. The efficiency of the proposed algorithms was investigated on the real data set and it was shown that compared to the MCCA method, the proposed algorithms have reached a higher average accuracy.

In recent years, model-based ECG processing algorithms have been successfully developed in various fileds of ECG processing. The calculation of ECG dynamic model (EDM) is a crucial step for these methods. The EDM parameters can be calculated using optimization algorithms. One of the popular optimization methods in this field is an offline nonlinear method in which users have to manually select points on ECG signal in order to calculate EDM parameters. The objective function used in this algorithm is a complex function which is hard to optimize. In this paper an automatic optimization algorithm is proposed which uses meta-heuristic optimization algorithms to calculate EDM parameters. In this algorithm, we don’t need to select points manually. In addition, the objective function in this algorithm is broken in to several simple objective functions which makes the optimization more accurate. Meta-heuristic optimization algorithms may perform successfully on some optimization problems while failing on others. As a result, a specific algorithm cannot be considered the best optimizer for all optimization problems. For this reason, in this paper, the performances of nine popular meta-heuristic algorithms such as particle swarm optimization, artificial bee colony, cucko search, etc are investigated. In this paper, 200 ECG segments from different records of the MIT-BIH Normal Sinus Rhythm Database (NSRDB) have been selected for evaluation. The duration of each segment was 30 seconds. The EDM parameters for each segment were calculated using the aforemetinoned optimization algorithms. For evaluation, the similarities between the original signals and the synthetic ECG signals were inspected for each optimization algorithm. These synthetic signals were created using the calculated EDM parameters. The similarity results showed that the water evaporation optimization (WEO), teaching learning-based optimization (TLBO), and cucko’s search (CS) algorithms achived better results compared with other methods.

Working memory (WM) is an important cognitive function. Since WM capacity is limited, extensive research has been executed to improve it. Previous studies demonstrated that applying transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (DLPFC) enhances visual WM. Capacity enhancement of WM has a significant effect on the pilot's efficiency. However, little is known about the auditory-verbal WM of Pilots. Therefore, the aim of this study is to evaluate the effects of tDCS over the left DLPFC on the WM capacity augmentation of pilots. The auditory-verbal WM stimuli comprise characters that are random numbers and alphabet letters. The stimulus is presented through the pilot's headset, and he has been persuaded to memorize the auditory stimulus and repeat the memorized characters. The auditory task is a set of 30 voices and is designed in 6 stages. The task starts from the easiest stage (4 characters) and continues with 2 increments of characters per stage to the most difficult stage (14 characters). The experiment was conducted under three conditions: baseline, sham, and anodal-tDCS. Before running the task, 2mA electrical stimulation with a duration of 30 seconds for the sham and 10 minutes for the anodal-tDCS conditions, was applied over the left DLPFC region of pilots. The performance measure is the number of correct remembered characters. Statistical hypotheses showed significant effects of anodal-tDCS in comparison to baseline condition as follows: %6.41 WM enhancement by considering all stages; and also improved performance around %12.20 in stage 4, %9.00 in stage 5, and %10.44 in stage 6 which are the most difficult stages. As a result, we found that 2mA anodal-tDCS over the left DLPFC can modulate WM capacity. The current study can be utilized to discover evidence of cognitive, behavioral, or neural mechanisms of WM and its application for human augmentation.

Early recognition of common diseases, including cancer, plays an essential role in preventing the progression of the disease. Among the various methods that have been invented for blood monitoring in recent years, the methods based on the use of micro-scale flow have received special attention. Isolation of biological nanoparticles is widely used in diagnosis, treatment and care in the field of medicine. Recent research on nano-sized extracellular carriers is of interest in the field of medicine. Biological nanoparticles such as viruses, DNA, proteins and exosomes contain significant information that can help diagnose and treat diseases such as cancer. One of the practical and effective methods for separating nanoparticles is the use of viscoelastic fluid, which does not have the complications of other methods. Unlike microparticles, the number of studies in the field of bio nanoparticles is low. Since previous research in the field of nanoparticle separation lacks comprehensive numerical information about the effect of aspect ratio and polymer concentration, in this article, the viscoelastic fluid flow along with particle physics has been numerically simulated with Comsol Multiphysics software. The effective parameters including aspect ratio 1, 1.5 and 2 and polymer concentration 0.05, 0.15 and 0.25 % have been investigated in the separation of 1000 up to 100 nm particles. Separation of 300 and 500 nm particles at a concentration of 0.05% and the channel with an aspect ratio 1 and 1.5 has been obtained from the other particles. It is possible to separate the particle 100nm as exosome particle from the other particles at an aspect ratio 2 and a polymer concentration of 0.05% as the best choice

Topography of extracellular matrix plays a major role in many biological events including tissue healing, morphogenesis and growth. It is known that matrix constitution and mechanical properties are deciding factors in governing the fate of its inhabitant cells. Besides the direct mechanical cues, matrices also facilitate the release and uptake of certain chemicals and participate in cell-cell and cell-ECM crosstalk. Mechanical strains in the matrix are proved to direct endothelial cell migration and elongation leading to angiogenesis, and there is a consensus that matrix stiffness, fiber density and fiber orientation can enhance angiogenesis in the preferred direction of stiffness gradient. In this study, we specifically investigated the role of topography in guidance of endothelial self-reorganization prompted by the effect of fluid flow hindrance and facilitation in certain directions. We adopted our previous model of fluid flow guided angiogenesis for cellular responses. Lattice Boltzmann model of fluid flow was adopted and modified to study the effect of unidirectional and randomly oriented fibers. To study the effect of fiber orientation, we customized a previously proposed model of porosity in lattice Boltzmann to suit this purpose. This model could reproduce the effects of fiber orientations in matrix on endothelial migration and vasculogenesis. Simulations showed better confluency of formed lumens when prescribed flow is in the direction of fiber orientation. These results can have further implications in understanding endothelial complications in certain diseases as well as in tumor angiogenesis and metastasis.

It is critical for developing treatment strategies to investigate and analyze the growth dynamics and changes of invasive tumors in response to various microenvironmental conditions. When a tumor reaches its maximum amount of non-vascular growth, its cells compete for more food and oxygen sources, triggering complex processes in its evolution. Understanding the distribution of oxygen in the tumor environment is critical for unraveling the complexities of cancer progression. Existing physical models for studying oxygen distribution in tumors are based on reaction-diffusion equations, which include factors such as the formation and distribution of the new vascular network. In this study, we presented a computational model to investigate the distribution of oxygen in a hypoxic tumor based on the formation of the vascular network, which has fewer limitations and computational complexity than many common methods and reduces the volume of calculations. When complete with sufficient clinical data, this model can lead to the development of efficient tools in the treatment strategy of some cancers.

Stroke is one of the causes of death and the main cause of disability in developed countries. Normally, identification of stroke lesions is done by magnetic imaging, and its analysis requires the continuous presence of a doctor in the treatment center. Therefore, intelligent processing of medical images will be an effective approach for automatic diagnosis of brain lesions.In this paper, a new integrated framework based on fuzzy inference system and deep neural network for automatic segmentation of brain lesions is introduced. In this regard, firstly, an improved U-net deep network (U-net) has been introduced for lesion detection and segmentation, which includes increasing the number of encoder and decoder layers along with changing the activation functions. Then, by using a fuzzy inference system based on if-then rules used by membership functions, the proposed approach of this study, which is based on the pre-processing of input images and the use of the unit network, has been introduced.The results showed that the integration of the fuzzy inference system in the pre-processing with the improved deep network could increase the DICE coefficient up to 0.84. In addition, improving the contrast of the input images by the fuzzy system compared to the usual pre-processing methods such as histogram equalization showed a much better performance in the detection of lesions with small dimensions, which is due to the ability to control the amount of contrast increase in the fuzzy systems compared to the usual methods.