Publications by authors named "Mohammad Reza Hashemi Golpayegani"

8 Publications

  • Page 1 of 1

Toward a complex system understanding of bipolar disorder: A chaotic model of abnormal circadian activity rhythms in euthymic bipolar disorder.

Aust N Z J Psychiatry 2016 Aug 10;50(8):783-92. Epub 2016 May 10.

Department of Psychological Sciences, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia.

Importance: In the absence of a comprehensive neural model to explain the underlying mechanisms of disturbed circadian function in bipolar disorder, mathematical modeling is a helpful tool. Here, circadian activity as a response to exogenous daily cycles is proposed to be the product of interactions between neuronal networks in cortical (cognitive processing) and subcortical (pacemaker) areas of the brain.

Objective: To investigate the dynamical aspects of the link between disturbed circadian activity rhythms and abnormalities of neurotransmitter functioning in frontal areas of the brain, we developed a novel mathematical model of a chaotic system which represents fluctuations in circadian activity in bipolar disorder as changes in the model's parameters.

Design, Setting And Participants: A novel map-based chaotic system was developed to capture disturbances in circadian activity across the two extreme mood states of bipolar disorder. The model uses chaos theory to characterize interplay between neurotransmitter functions and rhythm generation; it aims to illuminate key activity phenomenology in bipolar disorder, including prolonged sleep intervals, decreased total activity and attenuated amplitude of the diurnal activity rhythm. To test our new cortical-circadian mathematical model of bipolar disorder, we utilized previously collected locomotor activity data recorded from normal subjects and bipolar patients by wrist-worn actigraphs.

Results: All control parameters in the proposed model have an important role in replicating the different aspects of circadian activity rhythm generation in the brain. The model can successfully replicate deviations in sleep/wake time intervals corresponding to manic and depressive episodes of bipolar disorder, in which one of the excitatory or inhibitory pathways is abnormally dominant.

Conclusions And Relevance: Although neuroimaging research has strongly implicated a reciprocal interaction between cortical and subcortical regions as pathogenic in bipolar disorder, this is the first model to mathematically represent this multilevel explanation of the phenomena of bipolar disorder.
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http://dx.doi.org/10.1177/0004867416642022DOI Listing
August 2016

Towards a complex system understanding of bipolar disorder: A map based model of a complex winnerless competition.

J Theor Biol 2015 Jul 26;376:74-81. Epub 2015 Feb 26.

Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia.

Bipolar disorder is characterized by repeated erratic episodes of mania and depression, which can be understood as pathological complex system behavior involving cognitive, affective and psychomotor disturbance. In order to illuminate dynamical aspects of the longitudinal course of the illness, we propose here a novel complex model based on the notion of competition between recurrent maps, which mathematically represent the dynamics of activation in excitatory (Glutamatergic) and inhibitory (GABAergic) pathways. We assume that manic and depressive states can be considered stable sub attractors of a dynamical system through which the mood trajectory moves. The model provides a theoretical framework which can account for a number of complex phenomena of bipolar disorder, including intermittent transition between the two poles of the disorder, rapid and ultra-rapid cycling of episodes and manicogenic effects of antidepressants.
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http://dx.doi.org/10.1016/j.jtbi.2015.02.020DOI Listing
July 2015

Predicting the risk of squamous dysplasia and esophageal squamous cell carcinoma using minimum classification error method.

Comput Biol Med 2014 Feb 26;45:51-7. Epub 2013 Nov 26.

Digestive Disease Research Center, Shariati Hospital, Tehran University of Medical Sciences, P.O. Box 1411713135, Tehran, Iran.

Early detection of squamous dysplasia and esophageal squamous cell carcinoma is of great importance. Adopting computer aided algorithms in predicting cancer risk using its risk factors can serve in limiting the clinical screenings to people with higher risks. In the present study, we show that the application of an advanced classification method, the Minimum Classification Error, could considerably enhance the classification performance in comparison to the logistic regression model and the variable structure fuzzy neural network, as the latest successful methods. The results yield the accuracy of 89.65% for esophageal squamous cell carcinoma, and 88.42% for squamous dysplasia risk prediction.
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http://dx.doi.org/10.1016/j.compbiomed.2013.11.011DOI Listing
February 2014

Periodic and chaotic dynamics in a map-based model of tumor-immune interaction.

J Theor Biol 2013 Oct 11;334:130-40. Epub 2013 Jun 11.

Complex Systems and Cybernetic Control Lab., Faculty of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, P.O. Box 1591634311, Iran.

Clinicians and oncologists believe that tumor growth has unpredictable dynamics. For this reason they encounter many difficulties in the treatment of cancer. Mathematical modeling is a great tool to improve our better understanding of the complicated biological system of tumor growth. Also, it can help to identify states of the disease and as a result help to predict later behaviors of the tumor. Having an insight into the future behaviors of the tumor can be very useful for the oncologists and clinicians to decide on the treatment method and dosage of the administered drug. This paper suggests that a suitable model for the tumor growth system should be a discrete model capable of exhibiting periodic and complex chaotic dynamics. This is the key feature of the proposed model. The model is validated here through experimental data and its potential dynamics are analyzed. The model can explain many biologically observed tumor states and dynamics, such as exponential growth, and periodic and chaotic behaviors in the steady states. The model shows that even an avascular tumor could become invasive under certain conditions.
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http://dx.doi.org/10.1016/j.jtbi.2013.05.031DOI Listing
October 2013

A variable structure fuzzy neural network model of squamous dysplasia and esophageal squamous cell carcinoma based on a global chaotic optimization algorithm.

J Theor Biol 2013 Feb 19;318:164-72. Epub 2012 Nov 19.

Complex Systems and Cybernetic Control Lab., Faculty of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, P.O. Box 1591634311, Iran.

Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error.
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http://dx.doi.org/10.1016/j.jtbi.2012.11.013DOI Listing
February 2013

Predicting the spontaneous termination of atrial fibrillation based on poincare section in the electrocardiogram phase space.

Proc Inst Mech Eng H 2012 Jan;226(1):3-20

Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran.

Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space. The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge 2004 was applied in the present study. It includes one training dataset and two testing datasets, A and B. The present investigation was initiated by producing a two-dimensional reconstructed phase space (RPS) of the ECG. Then, a Poincare line was drawn in a direction that included the maximum point distribution in the RPS and also passed through the origin of the RPS coordinate system. Afterward, the coordinates of the RPS trajectory intersections with this Poincare line were extracted to capture the local behavior related to the arrhythmia under investigation. The POS corresponding to atrial activity were selected with regard to the fact that similar ECG morphologies such as P waves, which are corresponding to atrial activity, distribute in a specific region of the RPS. Thirteen features were extracted from the selected intersection points to quantify their distributions. To select the best feature subset, a genetic algorithm (GA), in combination with a support vector machine (SVM), was applied to the training dataset. Based on the selected features and trained SVM, the performance of the proposed method was evaluated using the testing datasets. The results showed that 86.67% of dataset A and 80% of dataset B were correctly classified. This classification accuracy is in the same range as or higher than that of recent studies in this area. These results show that the proposed method, in which no complicated QRST cancelation algorithm was used, has the potential to predict AF termination.
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http://dx.doi.org/10.1177/0954411911425839DOI Listing
January 2012

A novel human--machine interface based on recognition of multi-channel facial bioelectric signals.

Australas Phys Eng Sci Med 2011 Dec 29;34(4):497-513. Epub 2011 Nov 29.

School of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multi-channel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Frontalis and Temporalis facial muscles. The acquired signals are passed through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean square features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down) is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed.
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http://dx.doi.org/10.1007/s13246-011-0113-1DOI Listing
December 2011

A chaotic viewpoint on DBS treatment of Parkinson's disease.

J Neuropsychiatry Clin Neurosci 2010 ;22(2):E12-3

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http://dx.doi.org/10.1176/jnp.2010.22.2.247.e12DOI Listing
August 2010