فصلنامه مهندسی پزشکی زیستی
سال دوازدهم شماره 1 (بهار 1397)

A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ability to provide a new dimension in communication or control of a device for the disabled persons. The neural activity used in the brain-computer interface can be recorded by various invasive and non-invasive methods and can be converted to the desired signal by different decoding algorithms.
In this study, 3 rats were used to perform a movement task which was pressing a key and receiving a drop of water by a mechanical arm for corrected trials. By implanting a 16-channel microelectrode array in the rat's motor cortex during an invasive process, the brain signals are recorded during the task, and simultaneously the signal received by the force sensor is also stored. By performing the necessary preprocessing on spikes and extracting the firing rates of signal as a feature vector by convolving a Gaussian window with the spike trains, the necessary inputs for the decoding algorithm, which is linear regression here, are obtained. Two patterns have been used for cross validation. The first pattern considers 60% of the data from the beginning of the signal as a train set and the remaining 40% of the signal as a test set and the second pattern is the opposite of the first one. Several methods have been used to evaluate the decoding algorithm used in the studies. In this paper, the correlation correlation and coefficient of determination have been used.

In clinical studies, it is difficult to determine the temperature distribution throughout both tumor and normal tissue during hyperthermia treatment, since temperatures are sampled at only a limited number of locations with conventional sensors. Simulation studies can help physicians understand better the effects of the treatment. In this study, three 2D tumor models are built in the COMSOL software environment based on the images of nano-particle distributions in sliced PC3, DU145 and LAPC4 tumors. The images are pre-processed in MATLAB before being imported into COMSOL. A uniform distribution model is added as a control group. Temperature distribution, maximum temperature, time to reach steady state, CEM43, iso-effective dose and heat flux at tumor-tissue boundary are analyzed to evaluate the effect of the nano particle distribution on hyperthermia treatment. The results indicate that a more concentrated nano-particle distribution is better in damaging diseased tissue than the uniform distribution under low heating power .A more uniform distribution is better than the concentrated distribution under high heating power. For concentrated nanoparticle distributions, the location where the nanoparticles are concentrated influences tissue damage: a more centered one has a better effect. Tumor tissue is more likely to be defective.

Depression is one of the most common mental disorders in the current century where early diagnosis can result in better treatment. One of the depression diagnostic methods is the analysis of the brain electrical signals. In this paper, we are seeking for a method to distinguish among the levels of the depression. The proposed model is a deep rule-based system based on the stacked principle and focuses on the interpretability of the rules alongside high accuracy. Fuzzy systems have the proper capability in the classification of medical data with various levels of uncertainty. Moreover, in the recent years, deep learning has been taken considerable attention in the field of Artificial Intelligence. In this paper, we aim to benefit from capabilities of both fields. The proposed architecture employs a robust fuzzy clustering approach that can determine an appropriate number of clusters in each layer, unsupervised and a hierarchical stacked structure to transfer the interpretable trained rules from the previous layers with the same linguistic labels to the next layer. The interpretability is due to the presence of the input space into the consequent ones. The presence of the output of the previous layer’s rules at the input space of the next parts equals to a fuzzy system with non-linear consequent or the certainty factor in a fuzzy system with linear consequent. EEG data were preprocessed and time, frequency and nonlinear features such as recurrent plot were extracted and selected and after that were employed in the proposed system. The proposed system was compared with common classifiers like Neural Net, Support Vector Machine, Naive Bayes, Decision Tree and Linear Discriminant Analysis. Accuracy results for the test data in 30 folds (49.01% in comparison to 41.42%, 40.47%, 40.01%, 38.35% and 40.28% respectively) demonstrate the considerable performance of the proposed system.

This study presents a Silicon Nano-bio-sensor based on modified continuum mechanics model of Euler-Bernoulli beam theory. This cantilever resonant nano-sensor works based on the shift of resonant frequency due to the adsorption of very small particles such as viruses and bacteria. To this end, the surface of nano bio-sensor is impregnated into a biologically active substance such as Myosin as an adsorbate layer. However, most conducted studies have ignored the effects of mass and stiffness of this adsorbate layer and nonlocal parameter, whereas these factors play a major role in changing the resonant frequency at nano-scale and the precision of mechanical nano bio-sensors. By calculating and regarding all of the mentioned effects, in this study a Silicon nano bio-sensor with a full coverage of the adsorbate layer is precisely analyzed. The results show that the calculation of nonlocal effect reduces the resonant frequency of the nano sensor, and this effect cannot be ignored in the nano-scale. It is also observed that considering the effects of the mass and stiffness of the adsorbate layer separately, may not lead to the exact answer, but the result of both of these effects should be taken into account. In fact, simultaneously considering these effects, it reduces the resonant frequency of nano sensor, which can be useful in designing and analyzing mechanical Silicon nano bio-sensors and increasing the accuracy of their detection. Finally, for the purpose of verification assessment, the numerical results were compared with the results of other studies in the full coverage of the myosin adsorbate layer, which showed complete agreement with them.

Parkinson’s disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Different pathological attacks in Parkinson’s disease can be investigated by directional relations in the base spontaneous fluctuations of the brain from the resting state functional magnetic resonance imaging (RS-fMRI) data. In this paper, for analyzing the directional brain network at rest, Directed Transform Function (DTF) technique with graph theory has been used in two frequency sub-bands and intra/inter group connectivities were compared by statistical analysis. The result of group comparison between PD and healthy which has been done, showed that there are more significant connections in the low frequency band in Parkinson’s disease and control group compared to high frequency band. The relation between basal ganglia and cerebellum has been disturebed in Parkinson’s disease . Furthermore, some brain regions such as left cerebellum has the most information flow in healthy group which characterized by pivotal regions which were influenced by the other brain regions, this connection became disordered in Parkinsonism.

Intramedullary nailing is a common technique for treatment of femoral shaft fractures. Nail deformation after insertion into the bone, makes the distal hole locking a challenging task for the surgeon. The proximally mounted targeting systems for locating the position of the distal hole become ineffective due to the nail deformation. The previous image-based techniques have often considered the shape of the distal end of the nail only to find the position and direction of the distal hole. The purpose of this study was to evaluate the hypothesis of possibility of locating the position of the distal hole using mechanical modeling and radiography data simultaneously. In the proposed method, according to the nail geometry (length to diameter ratio between 25 and 50), an Euler-Bernoulli beam model is used to simulate the mechanical behavior of the nail and calculate its deformation pattern. Then, by registering the deformation pattern with the sagittal radiography image of the nail, using iterative closest point algorithm, the nail deformation and the position of the distal hole are predicted. In order to evaluate the research hypothesis, a number of experiments were performed on five cadaveric femurs and the predicted and actual positions of the distal hole were compared. Results indicated that by using mechanical modeling and the imaging data of the nail curvature, the position of the distal hole could be predicted with a mean error of 0.84 mm and a maximum error of 1.3 mm. It is expected that by combining the proposed method with the image-based techniques, which make use of the shape data of the hole, a sub-millimeter error in locating the distal hole could be achieved in future.

With the increase in lifespan there are many concerns related to ability of the hard tissues such as teeth to meet the physical demands over an extended period of function. The dentin has a special microstructural feature that governs its mechanical behavior, e.g., fracture mechanics: cylindrical tubules that are called dentin tubules. These tubules are gradually occluded in the elderly. The present study is aimed to investigate the effects of microstructure and its aging-related changes of the considered fiber-reinforced composite dentin on the fracture behavior and crack propagation trajectory, utilizing linear elastic fracture mechanics and finite element method. Obtained results indicate that the crack propagation path depends on geometrical microstructure of the dentin as well as respective mechanical properties and arrangement of dentin tubules. Also our results delineate that occlusion of dentinal tubule due to the aging plays a significant role at crack propagation trajectory and behaves as a barrier to crack growth.