فصلنامه مهندسی پزشکی زیستی
سال سیزدهم شماره 4 (زمستان 1398)

The P300 event-related potentials (ERPs) has implicated in outcome evaluation and reward processing. It is controversial how reward processing affects the neural sources of P300. We try to investigate the effect of feedback on the neural sources of P300 component. Thirty healthy subjects were participated and their EEG signals were recorded by thirty channels through the start (30 minutes), feedback (60-90 minutes) and last (30 minutes) segments. We analyzed feedback segment where an equal number of audio and visual stimulus were applied to the participants to perform audio and visual recognition tasks. We punished participants for wrong answers where each wrong answer adds four more tests to this segment. The P300 component was extracted from the background EEG at all channels using the conventional time-locked synchronous grand averaging over all time frames and subjects. Next, two well-known source localization algorithms including standardize low resolution electromagnetic tomography (sLORETA) and shrinking sLORETA were applied to the P300 waveforms for estimating the activity of the P300 sources. Our finding show a significant increase in the activation of P300 sources in the feedback-locked compared to the stimulus-locked over right tempo-parieto-occipital areas (secondary association area) in visual recognition task, but difference of P300 sources is not significant in audio recognition task. The discrepancy between the audio and visual task confirms the hypothesis that our participants considered more probability of wrong answers in the audio task, but they respond to visual test with more confidence.

Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that is affordable and easy to operate compared to other neuromodulation techniques. Despite this method is promising in treating neurological diseases and enhancing cognitive functions, the precise mechanism of the effect of this sub-threshold stimulation has not been understanded well. Understanding the mechanism is important in designing the proper protocol and system for the brain's electrical stimulation. The aim of this paper is to identify this mechanism with the neural modeling approach. As the results of some physiological studies have shown that under tDCS, sudden calcium signaling associated with calcium signaling of astrocyte cells in the brain are found, in the proposed model, this cell is considered as well as the main neurons and interneurons. The purpose of this model is to simulate the effect of tDCS on cortical activity related to the evoked response potential (ERP) and to compare with the actual results of previous experimental studies on rats. The results show that this model can simulate all the evidence of experimental studies. While the proposed purely neuronal model in previous studies could not simulate all the evidence

Nowadays, patients crowd suffering from heart disease is increasing along with the development of technology and mechanized life. On the other hand, donor hearts ready for transplantation is limited in the world. Therefore, exploiting blood pumps is a suitable alternative for helping the patient during the waiting time and even until the end of life. The blood pumps should be able to satisfy the biological needs, including proper output pressure and flow rate, in an acceptable margin of safety in terms of blood injuries. Reduction of pump size, blood exposure time and blood damages such as hemolysis are mentioned as the important challenges in the design of blood pumps. 30% of the patients who are using a left ventricle blood pump, required right ventricle blood pump due to right ventricle failure. Fulfilling the physiological requirement of right ventricle a Rvad must generate pressure in the range of 15-25 mm Hg and flow rate equal to 5 L/min. generation of pressure over 25 mmHg will lead to pulmonary hypertension and its consequent problems. In this research, a centrifugal blood pump was designed for the right ventricle with an emphasis on impeller geometry. This pump was simulated for rotational speeds of 1500 , 2000 rpm and flow rates of 4 - 6 L/min by using the computational fluid dynamics. The designed pump produces a flow rate of 5 L/min at 1500 rpm and a pressure of 23 mmHg. The amount of the hemolysis index calculated by the Lagrangian method is 0.00413.

Controlling of neuroprostheses to restore grasping ability in patients with paralyzed or amputated upper limbs is one of the important applications of BCI systems. The ability to get objects is necessary for daily works so, for a reliable function of the neuroprostheses, it is necessary for the user to control the amount of force needed for grasping. For this reason, increasing the accuracy of continuous force decoding is an important issue for the convenient function of these BCI systems. In most studies in the field of force decoding, linear models such as wiener filter, Kalman filter, PLS, etc. are used to decode force. So far, the effect of using nonlinear models is not investigated on force decoding. The goal of this study is to investigate the effect of using nonlinear regression models based on kernel functions on the accuracy of force decoding in Vistar rats using local field potential signals. To do this, we choose ridge regression, PCR and PLS methods and use the Gaussian kernel function to construct a generalized nonlinear model for the force decoding. Evaluating kernel ridge, kernel PCR and kernel PLS methods shows that considering nonlinear relations between brain signal’s features improves decoding accuracy. The mean coefficient of determination (R2) improves 12.7% in kernel ridge toward ridge regression, 25.5% in kernel PCR toward PCR and 19.1% in kernel PLS toward PLS method. The best decoding accuracy has been achieved by the kernel ridge regression method and the mean correlation coefficient between the estimated and measured force is 0.72 and R2 is 0.62.

Feature selection is a well-known preprocessing technique in machine learning, data mining, and especially bioinformatics microarray analysis with a high-dimension, low-sample-size (HDLSS) data. The diagnosis of genes responsible for disease using microarray data is an important issue to promoting knowledge about the mechanism of disease and improves the way of dealing with the disease. In feature selection methods based on information theory, which cover a wide range of feature selection methods, the concept of entropy is used to define criteria for relevance, redundancy, and complementarity.In this paper, we propose a new relevancy criterion based on the concept of pure continuity rather than the concept of entropy. In the proposed method, to control and reduce redundancy, the relevancy between a feature and each class is separately examined, while in most of the filter methods the value of a feature is measured based on its relation to the entire class. This solution allows us to identify the most efficient features (genes) of each class separately, while identifying common features (genes) is also possible. Discretization is another challenge in some available techniques. Using a homomorphism transformation in proposed method avoids engaging with discretization complexities, while taking advantages of it. Seven types of cancer microarrays with three types of classification models (e.g. NB, KNN, and SVM) are used to establish a comparison between the proposed method and other relevant methods. The results confirm the efficiency of the proposed method in the term of accuracy and number of selected genes as two parameters of classification.

In this research, the response of a novel drug delivery system responsive to the temperature, as a unique stimulus, was studied. The performance of the system was modeled at the unsteady state, using the numerical method. The system has three individual layers, containing a drug core, a phase-transient intermediate layer and an external protective layer. The system has the ability to start and stop the release of the drug, according to the On-Off mechanism, by exerting any changes in the temperature of the release medium. Mathematical modeling was performed by solving the heat and mass transfer equations governing the layers of the system at the unsteady state. The lag time of system at On state, the drug release kinetics at On state and undesired drug release kinetics at Off state were determined as functions of the parameters of the system. The results obtained from the modeling showed that response of the system was under the influence of different parameters, such as the geometry of the system, the kind of constituents of the intermediate and protective layers and the ratio of the thermal conductivity of the intermediate layer at molten state to the thermal conductivity of the protective layer. It was shown that a reduced lag time for the system could be achieved by manipulating these parameters. From the viewpoint of the drug release kinetics at On state, it could be declared that the amount of the released drug is a function of the time constant of the system and the drug release could be increased by decreasing the time constant value. The results also showed that the undesired release of the drug could be accelerated by adjusting the parameters of the protective layer, such as the kind of constituents and the thickness of the layer. Using the obtained results from the numerical modeling, one can design and produce the temperature-responsive smart drug delivery systems with desired characteristics for practical applications.

A primary objective in many human upright state movements is control of balance and monitoring, analysis, and intervention to improve it, has become a part of human biomechanics research.In studies with a quantitative approach to human balance, it is necessary to know the numerical quantity of balance in a body state or at any moment during a path.This study proposes a new quantitative Criterion to express stable state during walking cycle.The basis of this quantitative criterion is the Probability of dynamic success in completing the swing phase without losing balance and the initiation of a fall. The probability of motion realization has been calculated and simulated on a seven-link model with a distributed mass.In this study by taking into consideration the kinematic constraints, energy consumption, muscle stimulation level and changes in stimulation beside maximizing balance, the movement in stance phase is calculated as an optimal movement.The optimal step length has been calculated considering a weight for probability of motion realization and energy consumption. In this method both the maximum balance and minimum energy consumption have been considered.For instance, the optimal step length considering the maximum balance constraint in the specific path for an individual with the height of 187 cm and mass of 92 kg was calculated about 27 cm with this probabilistic approach.One of the factors in maintaining balance is cadence rate. By increasing the of center of mass average velocity, the probability of balance maintenance decreases, thus also with considering center of mass average velocity beside maximum balance constraint , the optimal step length is calculated 46 cm.