Bioelectrics
Sobhan Sheykhivand; Zohreh Mousavi; Tohid Yousefi Rezaii
Volume 14, Issue 3 , October 2020, , Pages 209-220
Abstract
Using a smart method to automatically detect different stages of epilepsy in medical applications, to reduce the workload of physicians in analyzing epilepsy data by visual inspection is one of the major challenges in recent years. One of the problems of automatic identification of different stages of ...
Read More
Using a smart method to automatically detect different stages of epilepsy in medical applications, to reduce the workload of physicians in analyzing epilepsy data by visual inspection is one of the major challenges in recent years. One of the problems of automatic identification of different stages of epilepsy is extraction of desirable features which can make the most distinction between different stages of epilepsy. The process of finding the proper features is generally time consuming. This study presents a new approach for the automatic identification of different epileptic stages. In this paper, a sparse represantion-based classification (SRC) with proposed dictionary learning is used to automatically identify the different stages of epilepsy using the EEG signal. The proposed method achieves 100% accuracy, sensitivity and specificity in 8 out of 9 scenarios. Also the proposed algorithm is resistant to Gaussian noise up to 0 decibels. The results show that using the proposed algorithm to identify different epileptic stages has a higher success rate than other similar methods.
Samira Rezvani Ardakani; Sajad Mohammad-Ali-Nezhad; Reza Ghasemi
Volume 13, Issue 3 , October 2019, , Pages 273-289
Abstract
Epilepsy is one of the most important neurological disorders in the world. In order to suppress epileptic seizures, various control algorithms have been used. Time to control and reduce attacks and robustness of the controller against variations of pathologic parameters and unwanted oscillations are ...
Read More
Epilepsy is one of the most important neurological disorders in the world. In order to suppress epileptic seizures, various control algorithms have been used. Time to control and reduce attacks and robustness of the controller against variations of pathologic parameters and unwanted oscillations are important to control epileptic seizure. In order to consider these requirements and considering that one of the methods used to suppress epileptic waves is the change in mean soma (electric) potential of the excitatory neurons, this paper applies a fixed-time integral super twisting sliding mode controller to the combination of cortical and optogenetic models. First, the ion current produced in ion channels in optogenetic method is applied to the state variable of the mean electric potential of the excitatory neurons of the cortical model and the cortical and optogenetic models are combined and the controlled voltage applied to the system is applied to neurons of the epileptic zone of the brain as optic photons via the optogenetic model. Then, the mentioned controller is applied to the hybrid model so that the healthy model is tracked by the epileptic model in a fixed time. Finally, using the fixed-time integral super twisting sliding mode controller, the convergence error of the epileptic state to the healthy state has become zero. The amplitude of the control signal is reduced compared to the classic sliding mode control and technical problems and unwanted oscillations which are the shortcomings of the classic sliding mode controller are resolved.
Biological Computer Modeling / Biological Computer Simulation
Mahmoud Amiri; Fariba Bahrami; Mahyar Janahmadi
Volume 4, Issue 2 , June 2010, , Pages 83-96
Abstract
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 ...
Read More
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.
