فصلنامه مهندسی پزشکی زیستی
سال چهارم شماره 2 (تابستان 1389)

Based on the neurophysiologic findings, astrocytes provide not only structural and metabolic supports for the nervous system but also they are active partners in neuronal activities and synaptic transmissions. In the present study, we improved two biologically plausible cortical and thalamocortical neural population models (CPM and TCPM), which were developed previously by Suffczynski and colleagues, by integrating the functional role of astrocytes in the synaptic transmission in the models. In other words, the original CPM and TCPM are modified to integrate neuronastrocyte interaction considering the idea of internal feedback proposed by Iasemidis and collaborators. Using the modified CPM and TCPM, it is demonstrated that healthy astrocytes provide appropriate feedback control for regulating the neural activities. As a result, we observed that the astrocytes are able to compensate for the variations in the cortical excitatory input and maintain the normal level of synchronized behavior. Next, it is hypothesized that malfunction of astrocytes in the regulatory feedback loop can be one of the probable causes of seizures. That is, pathologic astrocytes are not any more able to regulate and/or compensate the excessive increase of the cortical excitatory input. Consequently, disruption of the homeostatic or signaling function of astrocytes may initiate the hypersynchronous firing of neurons. Our results confirm the hypothesis and suggest that the neuronastrocyte interaction may represent a novel target to develop effective therapeutic strategies to control seizures.

Different biological signals including EEG, EOG, and EMG are recorded in sleep labs to diagnose sleep disorders. Data recorded during sleep is usually analyzed by sleep specialists visually. Since the sleep data is usually recorded for a long time period- namely a whole night- its visual inspection and classification is a very demanding and time consuming task so automatic analysis can definitely facilitate that. The key to automatic sleep staging is to extract suitable features. In the current study two classes of features are extracted from EEG signal. The first group is the features calculated from the coefficients of wavelet packet transformation (WPT) and the second group consists of a number of frequency features and a time feature, the amplitude of EEG signal itself. These two sets of features were separately mapped on a two dimensional space by SOM neural networks. The mappings indicated that these features are highly discriminative in separating sleep stages automatically. The data extracted from awake and deep sleep EEGs were mapped on two totally different regions. The mapping also indicated that EEG signal is not enough to separate stages thoroughly, as extracted data from EEG during REM and the first stage of NREM are mapped on the same region. Data extracted from EEG signals in the second stage overlapped with other stages which are in agreement with physiological definition of sleep stages.

Writing is one of the high practiced and complex movement skills of human. Most of the proposed models for writing are bottom-up models, and therefore they could not reflect the biological aspects of movements in this process. Also there is not any model for illustrating the role of different parts of the brain in this task. In this paper we are going to describe some neurological and physiological aspects of the brain operation in the writing task. Then some evidence of prediction in writing and existence of internal models for limbs such as hand are presented. According to these, modeling of writing using model predictive control (MPC) is possible. Based on the presented simulations and experimental results it seems that the modeling of writing by MPC is very similar to the real skill, The proposed model has some advantages such as being consistent with the biological evidence, modeling prediction in writing and high correlation of the statical and dynamical features of the generated letters with those written by human.

In this paper, we propose a novel approach for classification of surface electromyogram (sEMG) signal with a view to controlling myoelectric prosthetic devices. The sEMG signal generated during isometric contraction is modeled by a stochastic process whose probability density function (PDF) is non- Gaussian for low levels of applied force. Since the PDF of ambient noise is assumed to be Gaussian, we extract correntropy features, as they contain information on non-Gaussian components (the sEMG signal) only; and utilize the linear discriminant analysis (LDA) to classify the sEMG signal using correntropy features. Our proposed method has lower classification error and requires much less computations as compared to other existing advanced methods.

Many methods are introduced for estimating the similarities or differences of time signals. One of theses methods, DTW algorithm, is also a utility for other domains including classification, data mining and matching regions between two time signals. DTW algorithm minimizes points distance between two signals by contracting or expanding the time axes to find the corresponding points. In this paper, with modification of the local constraints in DTW, a powerful method is proposed for measuring the global or local similarities between two signals. In addition to increasing the accuracy of signals distance measurements and decreasing the classification error, proposed algorithm is more stable than classic DTW against variations of structure and time signal source. The proposed method for dynamic signature verification was applied to a dataset of signatures from Turkish, Chinese and English people. The results of the experiments based on Fisher, Parzen Window and Support Vectors Machine classifications, showed that equal error rate (EER) is 1.46% and 3.51% with universal threshold for random and skilled forgeries, respectively.

Dimensionality reduction is an important task in machine learning, to simplify data mining, image processing, classification and visualization of high-dimensional data by mitigating undesired properties of high-dimensional spaces. Manifold learning is a relatively new approach to nonlinear dimensionality reduction. Algorithms for manifold learning are based on the intuition that the dimensionality of many data sets may be artificially high and each data point can be described as a function of only a few underlying parameters. Using this tool, intrinsic parameters of the system database, which are main distinction factors of data sets, are recognized and all of them lie on a manifold that shows the real relationship of parameters. One of the successful applications of these methods is in image analysis field. By this approach, each image is a data in high dimensional space that the pixels are its dimensions. Because echocardiography images obtained from a patient are different in quantitative parameters such as heartbeat periodic motion and noise, image sets are reduced to two-dimensional space by a proper manifold learning. In this article, after mapping echocardiography images in two-dimensional space, by using LLE and Isomap algorithms, similar images placed side by side and the relationships between the images according to the cyclic property of heartbeat became evident. The Results showed the weakness of Isomap algorithm and power of LLE algorithm in preserving the relation between consecutive frames. De-noising is an important application which extracted from this research.

Various reviews have shown that strong electromagnetic fields have negative effects on human health. This study focused on the effect of MRI radiation on liver functional test histometery of liver in adult male rats. For this purpose, we used an MRI device that could produce 1.5 T electromagnetic radiations, and chose 22 Wistar rats as laboratory animal models. Rats were divided into two equal groups. The first group exposed to 1.5T electromagnetic radiation and RF radiation during a 30- minute MRI scan as experimental group. The control group experienced 1.5T electromagnetic radiation exposure without RF radiation by the same MRI device. The rats were anesthetized and blood samples were obtained from cardiac chambers to measure the serum levels of LDL, HDL, ALT, AST, ALP, total cholesterol, total protein, albumin, total billirobin, and direct bilirobin. Livers were then removed and the specimens fixed. Serial sections (5 μm thick) were prepared from livers and the diameter of hepatocytes and their nuclei were measured. The findings of the present study indicate that, there was a significant increase (P<0.5) in amount of HDL, ALT, AST, ALP, total billirobin, direct bilirobin and there was a significant decrease (P<0.5) in amount of total cholesterol, LDL, total protein, and albumin in experimental group by comparison with control group. But no significant differences were seen in the diameter of hepatocytes and their nuclei between both groups. The electromagnetic radiations of MRI device may influence the level of liver enzymes and liver function without any histomorphologically changes. Conducting clinical trial studies with human subjects is recommended.