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Fulltext Diagnosis of Lung Cancer by Fractal Analysis of Damaged DNA

Namazi H, Kiminezhadmalaie M

Comput Math Methods Med, 2015;2015:242695.
PMID: 26539245 DOI: 10.1155/2015/242695

Cancer starts when cells in a part of the body start to grow out of control. In fact cells become cancer cells because of DNA damage. A DNA walk of a genome represents how the frequency of each nucleotide of a pairing nucleotide couple changes locally. In this research in order to study the cancer genes, DNA walk plots of genomes of patients with lung cancer were generated using a program written in MATLAB language. The data so obtained was checked for fractal property by computing the fractal dimension using a program written in MATLAB. Also, the correlation of damaged DNA was studied using the Hurst exponent measure. We have found that the damaged DNA sequences are exhibiting higher degree of fractality and less correlation compared with normal DNA sequences. So we confirmed this method can be used for early detection of lung cancer. The method introduced in this research not only is useful for diagnosis of lung cancer but also can be applied for detection and growth analysis of different types of cancers.
Fulltext Fractional Diffusion Based Modelling and Prediction of Human Brain Response to External Stimuli

Namazi H, Kulish VV

Comput Math Methods Med, 2015;2015:148534.
PMID: 26089955 DOI: 10.1155/2015/148534

Human brain response is the result of the overall ability of the brain in analyzing different internal and external stimuli and thus making the proper decisions. During the last decades scientists have discovered more about this phenomenon and proposed some models based on computational, biological, or neuropsychological methods. Despite some advances in studies related to this area of the brain research, there were fewer efforts which have been done on the mathematical modeling of the human brain response to external stimuli. This research is devoted to the modeling and prediction of the human EEG signal, as an alert state of overall human brain activity monitoring, upon receiving external stimuli, based on fractional diffusion equations. The results of this modeling show very good agreement with the real human EEG signal and thus this model can be used for many types of applications such as prediction of seizure onset in patient with epilepsy.
Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals

Pakniyat N, Namazi H

Technol Health Care, 2021 Sep 17.
PMID: 34542048 DOI: 10.3233/THC-213052

BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience.
OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals.
METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents.
RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions.
CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.
Fulltext Multiscale brain modeling: bridging microscopic and macroscopic brain dynamics for clinical and technological applications

Krejcar O, Namazi H

Front Cell Neurosci, 2025;19:1537462.
PMID: 40046848 DOI: 10.3389/fncel.2025.1537462

The brain's complex organization spans from molecular-level processes within neurons to large-scale networks, making it essential to understand this multiscale structure to uncover brain functions and address neurological disorders. Multiscale brain modeling has emerged as a transformative approach, integrating computational models, advanced imaging, and big data to bridge these levels of organization. This review explores the challenges and opportunities in linking microscopic phenomena to macroscopic brain functions, emphasizing the methodologies driving progress in the field. It also highlights the clinical potential of multiscale models, including their role in advancing artificial intelligence (AI) applications and improving healthcare technologies. By examining current research and proposing future directions for interdisciplinary collaboration, this work demonstrates how multiscale brain modeling can revolutionize both scientific understanding and clinical practice.
Fulltext Mathematical Modelling and Prediction of the Effect of Chemotherapy on Cancer Cells

Namazi H, Kulish VV, Wong A

Sci Rep, 2015;5:13583.
PMID: 26316014 DOI: 10.1038/srep13583

Cancer is a class of diseases characterized by out-of-control cells' growth which affect DNAs and make them damaged. Many treatment options for cancer exist, with the primary ones including surgery, chemotherapy, radiation therapy, hormonal therapy, targeted therapy and palliative care. Which treatments are used depends on the type, location, and grade of the cancer as well as the person's health and wishes. Chemotherapy is the use of medication (chemicals) to treat disease. More specifically, chemotherapy typically refers to the destruction of cancer cells. Considering the diffusion of drugs in cancer cells and fractality of DNA walks, in this research we worked on modelling and prediction of the effect of chemotherapy on cancer cells using Fractional Diffusion Equation (FDE). The employed methodology is useful not only for analysis of the effect of special drug and cancer considered in this research but can be expanded in case of different drugs and cancers.
Complexity-based classification of EEG signal in normal subjects and patients with epilepsy

Namazi H, Aghasian E, Ala TS

Technol Health Care, 2020;28(1):57-66.
PMID: 31104032 DOI: 10.3233/THC-181579

Analysis of human brain activity is an important topic in human neuroscience. Human brain activity can be studied by analyzing the electroencephalography (EEG) signal. In this way, scientists have employed several techniques that investigate nonlinear dynamics of EEG signals. Fractal theory as a promising technique has shown its capabilities to analyze the nonlinear dynamics of time series. Since EEG signals have fractal patterns, in this research we analyze the variations of fractal dynamics of EEG signals between four datasets that were collected from healthy subjects with open-eyes and close-eyes conditions, patients with epilepsy who did and patients who did not face seizures. The obtained results showed that EEG signal during seizure has greatest complexity and the EEG signal during the seizure-free interval has lowest complexity. In order to verify the obtained results in case of fractal analysis, we employ approximate entropy, which indicates the randomness of time series. The obtained results in case of approximate entropy certified the fractal analysis results. The obtained results in this research show the effectiveness of fractal theory to investigate the nonlinear structure of EEG signal between different conditions.
Fulltext Predictors of COVID-19 Preventive Behavior Adoption Intention in Malaysia

Mat Dawi N, Namazi H, Maresova P

Front Psychol, 2021;12:616749.
PMID: 34093307 DOI: 10.3389/fpsyg.2021.616749

Preventive behavior adoption is the key to reduce the possibility of getting COVID-19 infection. This paper aims to examine the determinants of intention to adopt preventive behavior by incorporating perception of e-government information and services and perception of social media into the theory of reasoned action. A cross-sectional online survey was carried out among Malaysian residents. Four hundred four valid responses were obtained and used for data analysis. A partial least-square-based path analysis revealed direct effects of attitude and subjective norm in predicting intention to adopt preventive behavior. In addition, perception of e-government information and services and perception of social media were found to be significant predictors of attitude toward preventive behavior. The findings highlight the importance of digital platforms in improving people's attitudes toward preventive behavior and in turn contain the spread of the infectious disease.
Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia

Namazi H, Aghasian E, Ala TS

Technol Health Care, 2019;27(3):233-241.
PMID: 30829625 DOI: 10.3233/THC-181497

Brain activity analysis is an important research area in the field of human neuroscience. Moreover, a subcategory in this field is the classification of brain activity in terms of different brain disorders. Since the Electroencephalography (EEG) signal is, in fact, a non-linear time series, employing techniques to investigate its non-linear structure is rather crucial. In this study, we evaluate the non-linear structure of the EEG signal between healthy and schizophrenic adolescents using fractal theory. The results of our analysis revealed that in terms of all recording channels, the EEG signal of healthy subjects is more complex compared to the ones suffering from schizophrenia. The statistical analysis also indicated that there is a significant difference in the complex structure of the EEG signal between these two groups of subjects. We also utilized approximate entropy in our analysis in order to verify the obtained results of the fractal analysis. The result of the entropy analysis suggested that EEG signal for healthy subjects is less random compared to the EEG signal in schizophrenic individuals. In addition, the employed methodology in this research can be further investigated in order to classify the brain activity in terms of other brain disorders, where one can explore how the complex structure of the EEG signal alters between them.
Complexity-based decoding of the relation between human voice and brain activity

Ahamed MRA, Babini MH, Namazi H

Technol Health Care, 2020 Mar 13.
PMID: 32200368 DOI: 10.3233/THC-192105

BACKGROUND: The human voice is the main feature of human communication. It is known that the brain controls the human voice. Therefore, there should be a relation between the characteristics of voice and brain activity.
OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed.
METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed.
RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours.
CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.
Fulltext Physiological State and Learning Ability of Students in Normal and Virtual Reality Conditions: Complexity-Based Analysis

Babini MH, Kulish VV, Namazi H

J Med Internet Res, 2020 06 01;22(6):e17945.
PMID: 32478661 DOI: 10.2196/17945

BACKGROUND: Education and learning are the most important goals of all universities. For this purpose, lecturers use various tools to grab the attention of students and improve their learning ability. Virtual reality refers to the subjective sensory experience of being immersed in a computer-mediated world, and has recently been implemented in learning environments.
OBJECTIVE: The aim of this study was to analyze the effect of a virtual reality condition on students' learning ability and physiological state.
METHODS: Students were shown 6 sets of videos (3 videos in a two-dimensional condition and 3 videos in a three-dimensional condition), and their learning ability was analyzed based on a subsequent questionnaire. In addition, we analyzed the reaction of the brain and facial muscles of the students during both the two-dimensional and three-dimensional viewing conditions and used fractal theory to investigate their attention to the videos.
RESULTS: The learning ability of students was increased in the three-dimensional condition compared to that in the two-dimensional condition. In addition, analysis of physiological signals showed that students paid more attention to the three-dimensional videos.
CONCLUSIONS: A virtual reality condition has a greater effect on enhancing the learning ability of students. The analytical approach of this study can be further extended to evaluate other physiological signals of subjects in a virtual reality condition.
Information-based decoding of the coupling among human brain activity and movement paths

Kamal SM, Dawi NM, Namazi H

Technol Health Care, 2021;29(6):1109-1118.
PMID: 33749623 DOI: 10.3233/THC-202744

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements.
OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view.
METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents.
RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.
Fulltext Synchronization of the neurons coupled with sequential developing electrical and chemical synapses

Wang Z, Ramamoorthy R, Xi X, Namazi H

Math Biosci Eng, 2022 Jan;19(2):1877-1890.
PMID: 35135233 DOI: 10.3934/mbe.2022088

There is some evidence representing the sequential formation and elimination of electrical and chemical synapses in particular brain regions. Relying on this feature, this paper presents a purely mathematical modeling study on the synchronization among neurons connected by transient electrical synapses transformed to chemical synapses over time. This deletion and development of synapses are considered consecutive. The results represent that the transient synapses lead to burst synchronization of the neurons while the neurons are resting when both synapses exist constantly. The period of the transitions and also the time of presence of electrical synapses to chemical ones are effective on the synchronization. The larger synchronization error is obtained by increasing the transition period and the time of chemical synapses' existence.
Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation

Soundirarajan M, Kuca K, Krejcar O, Namazi H

Technol Health Care, 2021 Nov 19.
PMID: 34842201 DOI: 10.3233/THC-213528

BACKGROUND: Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science.
OBJECTIVE: In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy.
METHOD: The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals.
RESULTS: The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals.
CONCLUSION: The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.
Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path

Mujib Kamal S, Babini MH, Krejcar O, Namazi H

Front Physiol, 2020;11:602027.
PMID: 33324242 DOI: 10.3389/fphys.2020.602027

Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.
Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

Pakniyat N, Babini MH, Kulish VV, Namazi H

Technol Health Care, 2021 Aug 13.
PMID: 34397441 DOI: 10.3233/THC-213136

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart's activity, a relationship should exist among their activities.
OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis.
METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18-22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked.
RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566).
CONCLUSION: We conclude that heart and brain activities are related.
Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli

Soundirarajan M, Pakniyat N, Sim S, Nathan V, Namazi H

Technol Health Care, 2021;29(1):99-109.
PMID: 32568131 DOI: 10.3233/THC-192085

BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles.
OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.
METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).
RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.
CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.
Fulltext Mathematical Based Calculation of Drug Penetration Depth in Solid Tumors

Namazi H, Kulish VV, Wong A, Nazeri S

Biomed Res Int, 2016;2016:8437247.
PMID: 27376087 DOI: 10.1155/2016/8437247

Cancer is a class of diseases characterized by out-of-control cells' growth which affect cells and make them damaged. Many treatment options for cancer exist. Chemotherapy as an important treatment option is the use of drugs to treat cancer. The anticancer drug travels to the tumor and then diffuses in it through capillaries. The diffusion of drugs in the solid tumor is limited by penetration depth which is different in case of different drugs and cancers. The computation of this depth is important as it helps physicians to investigate about treatment of infected tissue. Although many efforts have been made on studying and measuring drug penetration depth, less works have been done on computing this length from a mathematical point of view. In this paper, first we propose phase lagging model for diffusion of drug in the tumor. Then, using this model on one side and considering the classic diffusion on the other side, we compute the drug penetration depth in the solid tumor. This computed value of drug penetration depth is corroborated by comparison with the values measured by experiments.
Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

Namazi H, Akrami A, Nazeri S, Kulish VV

Biomed Res Int, 2016;2016:5469587.
PMID: 27699169

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.
Decoding the correlation between heart activation and walking path by information-based analysis

Kamal SM, Babini MH, Tee R, Krejcar O, Namazi H

Technol Health Care, 2023;31(1):205-215.
PMID: 35848002 DOI: 10.3233/THC-220191

BACKGROND: One of the important areas of heart research is to analyze heart rate variability during (HRV) walking.
OBJECTIVE: In this research, we investigated the correction between heart activation and the variations of walking paths.
METHOD: We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.
RESULTS: According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.
CONCLUSION: The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.
Fulltext A signal processing based analysis and prediction of seizure onset in patients with epilepsy

Namazi H, Kulish VV, Hussaini J, Hussaini J, Delaviz A, Delaviz F, et al.

Oncotarget, 2016 Jan 5;7(1):342-50.
PMID: 26586477 DOI: 10.18632/oncotarget.6341

One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More than 30% of people with epilepsy cannot control seizure. Epileptic seizure prediction, refers to forecasting the occurrence of epileptic seizures, is one of the most important but challenging problems in biomedical sciences, across the world. In this research we propose a new methodology which is based on studying the EEG signals using two measures, the Hurst exponent and fractal dimension. In order to validate the proposed method, it is applied to epileptic EEG signals of patients by computing the Hurst exponent and fractal dimension, and then the results are validated versus the reference data. The results of these analyses show that we are able to forecast the onset of a seizure on average of 25.76 seconds before the time of occurrence.

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