فصلنامه مهندسی پزشکی زیستی
سال نهم شماره 4 (زمستان 1394)

Understanding the mechanism of artificial disc degeneration using animal models is useful to study the regenerative techniques in hope of finding potential therapeutic strategies. For any type of potential therapeutic techniques, first we need to have the degenerated model. Disc degeneration can be mimicked in animal studies using needle puncture. However, the detailed mechanical response of the artificial degenerated disc using needle puncture under physiological diurnal activities has not been analyzed well. Hence, reverse finite element analyses combined with in-vitro experiments were used in this study to find the mechanical properties of intact (N=8) and injured discs using needle puncture (N=8). Afterward, specimen-specific FE models for 16 discs were simulated during physiological diurnal activity. The results showed that the variation of axial displacement, intradiscal pressure, and total fluid exchange in intact discs were significantly higher than the injured ones after 24h. But the maximum axial stress within disc was significantly higher in injured group. The achieved results are correlated with previous human cadaver data for natural disc degeneration. Therefore, it is concluded that the G-16 needle puncture injury is a simple and cost-effective methodology which can be used to mimic the degeneration mechanism in animal models.

The patients with Type 1 diabetes need strict blood glucose level control because the body’s production and use of insulin are impaired and hence this increases the blood glucose level. In this paper, a fractional order sliding mode control and an adaptive fractional order sliding mode control are proposed to regulate the blood glucose in the presence of the parameter variations and meal disturbance. The Bergman minimal model is used to design the proposed controllers. The proposed controllers are appropriate for making the insulin delivery pumps in closed loop control of diabetes. The proposed controllers attenuate the effect of chattering. The fractional adaptive sliding mode control makes the controller immune to disturbance and uncertainties and the fractional calculus provides robustness performance. Finally the results are compared with some other methods such as backstepping sliding mode control and fractional order sliding mode control methods. Simulation results show that the proposed controllers are able to reject both uncertainties and disturbance with a chattering free control law.

The cervical multifidus muscle is known as one of the deep neck extensor muscles that its dysfunction have been reported in people with neck pain.With regard to the limits on the evaluation of this muscle activity using electromyography, ultrasound was used to find out its function recently. The aim of this study is evaluation of this muscle dimansions change during six shoulder joint activities in healthy subjects and people with chronic neck pain and providing predictive models. So The relationship between strength of shoulder joint during contraction with the changes of anterior-posterior dimension, lateral dimension, shape ratio and size of the cervical multifidus muscle were assessed using of Response Surface Method in the first step for subjects and activities and then for activities with subject blocking. Finally, predictive models were provided for abduction activity in 0-50% of maximum voluntary contraction (MVC) for healthy subjects and 50-100% for patients with data clustering. The anterior-posterior dimension showed a higher correlation with the shoulder joint strength than other factors. R2 values for this dimension in healthy subjects before and after data clustering is 0.552 and 0.66 and in patients is 0.339 and 0.505 respectively. Given the models correlation coefficient and its enhance by data clustering, it seems that evaluation of anterior-posterior dimension of this muscle during isometric abduction activiy of shoulder joint with the sttrength of 0-50% MVC for healthy subjects and 50-100% for patients with neck pain can be provide useful information about its function.

Abnormal oscillations of ventricular cell action potential can lead to cardiac arrhythmias. Early afterdepolarizations (EADs) is one kind of these oscillations that have been widely studied in the field of cardiac arrhythmias diagnosis and therapies. Nowadays although ventricular cell models have been developed, yet dynamical mechanisms of EADs remain unknown that need more researches. In this paper, using phase plane analysis of a minimal model of ventricular cell, we show that EADs are occurred as a result of Hopf and homoclinic bifurcations in ventricular cell. we also show that during period pacing, chaos happens at the transition from no EAD to EADs. This result provides a distinct explanation for the EAD behavior of the cardiac cells and also explains EADs dynamics in accordance with experiment results. While this research was performed for ventricular cells, but the achieved results can be extend to other excitable systems and used in the prediction of oscillation due to the changes of system parameters.

Walking is one of the most widely used movements affecting life quality. Therefore, the study of factors affecting human gait has always been an important issue. Walking speed, as a physical perturbation, affects the quality of human walking. The purpose of this study is to estimate the effects of walking speed on the short-time gait parameters. Thirty-two healthy subjects (mean SD, age: 27.56 ± 20.4 years; body height: 158.19 ± 20.83 cm; body weight: 54.89 ± 20.59 kg; gender: 59% female) participated in this study. Kinetic, kinematic and electromyographic data were recorded at the following five walking speed categories: very slow, slow, medium, fast and very fast. The effect of speed on spatio-temporal parameters, muscle synergy space, walking smoothness, representation of joints displacement and the correlation between lower limb displacement and also correlation between muscles activation patterns were studied. Having being used physical perturbation, 46 predictors were extracted from one gait cycle information, some of which were proposed for the first time in the literature for example size of muscle synergy, minimum angular jerk, lower limb contributions and skewness, kurtosis and curvature of joints movements . Using muscle synergies showed that increasing walking speed leads to increase the size of synergy space. It could be concluded that central nervous system tries to adopt more organaized strategy for recruiting muscles and remaining stable at fast speeds. Our results showed that, speed plays a crucial role in human gait characteristic. We can investigate our methods among more subjects and also patients with gait disorders. We can evaluate other indices like gait stability based on short-term data recording.

The inference of Gene Regulatory Network (GRN) using gene expression data is significantly important in order to understand gene dependencies, regulatory functions among genes, biological processes, way of process occurrence and avoiding some unplanned processes (disease). The accurate inference of GRN needs the accurate inference of predictor set. Generally, the main limitations of the predictor set inference are the small number of samples, the large number of genes and also the possibility influence of noise in gene expression data. Hence, providing efficient methods to infer predictor set with high reliability is a serious need. In this paper, an efficient method is proposed to infer predictor set using Gravitational Search Algorithm (GSA). A GSA is used for each target gene to infer the predictor subset of the gene. In a population, a mass represents a predictor subset of the associated gene. The initial population per target gene is generated by Pearson Correlation Coefficient (PCC). In order to guide the GSA, Mean Conditional Entropy (MCE) is used as the assessment criterion. Experimental results show that the proposed method has a good ability to infer the predictor set with high reliability. In addition, we also compared the proposed algorithm with a recent similar method based on genetic algorithm. Comparison results reveal the advantage of the proposed algorithm on biological datasets with small data volumes and large network scales.

Detection of Event Related Potentials (ERP) is an important prerequisite in the ERP-based Brain-Computer Interface (BCI) systems. In order to increase the classification accuracy in these systems, different filtering methods are used for improving the signal to noise ratio. This improvement facilitates the diagnosis and classification of the ERPs. In a number of studies, the performance of P300 detection systems which are based on common spatial pattern (CSP) and common temporal pattern (CTP) has been investigated. The former uses spatial filters while the latter is based on temporal filters. In these methods the filters are trained such that they maximize variance of one class and simultaneously minimize the other class variance. The associated results show that in P300 speller systems, the temporal filters outperform the spatial filters. In this study, in order to improve the performance of the CTP based systems, a Weighted Common Temporal Pattern (WCTP) algorithm which is a combined method is proposed. In this algorithm, each category of features has a weight based on the importance of its eigenvalues. In fact, the features produced by the initial and final CTP filters have more weight in the decision making process. In the combined method used in this algorithm, the LDA classifiers are used. It is shown that the set of features obtained by the WCTP method leads to an average classification accuracy of 90.2 percent which is about 4 percent better than the CTP method. The experiments are performed considering two different subjects on 5 trials.

Falls are one of the main reasons to injury, especially in the elderly people. These injuries can be reduced by quick and accurate response or reaction, but this is not possible often in elderly people because they usually live alone and after injury caused the falling, cannot call for help. This paper presents a fall detection system to do two major tasks properly and quickly; firstly, it should detect fall from other daily activities and secondly, transmit falling person’s necessary information to help. This system is implemented on Android-based smartphone and it used tri-axial accelerometer and microphone to fall detection. Everyday interaction with the smartphone makes our system more familiar to the user. The accelerometer is used to record variations of acceleration in three directions. This system is improved with detecting the noise caused the falling, by analyzing environmental sounds. After fall detection, a warning text message that contains information about time and location of the falling will be sent to the caregivers. A comprehensive evaluation with 18 volunteers shows that the proposed system has sensitivity of 96% and specificity of 77% for different types of fall in quiet and noisy environments.