Narrow Results

In the present study, both single channel electroencephalography (EEG) complexity and two channel interhemispheric dependency measurements have newly been examined for classification of patients with obsessive–compulsive disorder (OCD) and controls by using support vector machine classifiers. Three embedding entropy measurements (approximate entropy, sample entropy, permutation entropy (PermEn)) are used to estimate single channel EEG complexity for 19-channel eyes closed cortical measurements. Mean coherence and mutual information are examined to measure the level of interhemispheric dependency in frequency and statistical domain, respectively for eight distinct electrode pairs placed on the scalp with respect to the international 10–20 electrode placement system. All methods are applied to short EEG segments of 2 s. The classification performance is measured 20 times with different 2-fold cross-validation data for both single channel complexity features (19 features) and interhemispheric dependency features (eight features). The highest classification accuracy of 85 ±5.2% is provided by PermEn at prefrontal regions of the brain. Even if the classification success do not provided by other methods as high as PermEn, the clear differences between patients and controls at prefrontal regions can also be obtained by using other methods except coherence. In conclusion, OCD, defined as illness of orbitofronto-striatal structures [Beucke et al., JAMA Psychiatry70 (2013) 619–629; Cavedini et al., Psychiatry Res.78 (1998) 21–28; Menzies et al., Neurosci. Biobehav. Rev.32(3) (2008) 525–549], is caused by functional abnormalities in the pre-frontal regions. Particularly, patients are characterized by lower EEG complexity at both pre-frontal regions and right fronto-temporal locations. Our results are compatible with imaging studies that define OCD as a sub group of anxiety disorders exhibited a decreased complexity (such as anorexia nervosa [Toth et al., Int. J. Psychophysiol.51(3) (2004) 253–260] and panic disorder [Bob et al., Physiol. Res.55 (2006) S113–S119]).

Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients’ clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2min was partitioned into identical epochs of 20s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel–Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics’ brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics’ brain.

Many electrophysiological experiments have shown that epileptic seizures often originate from the synchronous activities of abnormally excitable neurons. The dynamic process of epilepsy is very complex, and characterized by a seemingly rapid and dramatic birth of new oscillations, essentially leading to a propagation and amplification of the original aberrant activity. It is very difficult to thoroughly understand the mechanism from a theoretical standpoint, however some special work can prove helpful. Here we present a theoretical framework to investigate chaos and complexity in the synchrony of excitable neurons in an effort to study the collective oscillations within a neural network. As endogenous rhythms, oscillations arise because most cellular processes contain feedback. The Chay model of excitable neurons is chosen because the model describes the abnormal process, where spiking can be transformed into bursting via bifurcation.

In our study, the Chay model is regarded as an abnormal oscillator and coupled via a resistor representing the effect of gap junctions (electrical synapses). In this paper, we present some models developed from the original Chay model, for the synchrony of two cells and a 2D neural network. Lyapunov exponent and phase portrait are utilized to evaluate the chaotic dynamics. Finally, approximate entropy is utilized to measure its complexity. Our results show that the synchrony of abnormal oscillations can occur when the coupling strength of the gap junction is sufficiently large. It is also found that the concentration of Ca²⁺ ions does not synchronize. In the 2D network, approximate entropies of different oscillations with strong coupling strength are greater than those with weak coupling strength. It is indicated that synchronous neurons have greater ability to produce new oscillations than asynchronous ones. This work shows that nonlinear analytical methods may prove useful in elucidating the mechanisms of pathologic conditions, where new oscillations are born and propagated, such as in epilepsy.

In this paper, a new 3D discrete hyperchaotic system is constructed, and its Lyapunov exponent and approximate entropy are calculated. We adopt the drive-response method and self-adaptive method to make the hyperchaotic system to reach synchronization, and then design an encrypted transmission system based on the synchronization of this discrete hyperchaotic system. In the synchronous hyperchaotic system, the initial value related to the hash value of the speech signal of the chaotic system is designed. Some security analyses are studied in detail.

An important challenge in respiration related studies is to investigate the influence of external stimuli on human respiration. Auditory stimulus is an important type of stimuli that influences human respiration. However, no one discovered any trend, which relates the characteristics of the auditory stimuli to the characteristics of the respiratory signal. In this paper, we investigate the correlation between auditory stimuli and respiratory signal from fractal point of view. We found out that the fractal structure of respiratory signal is correlated with the fractal structure of the applied music. Based on the obtained results, the music with greater fractal dimension will result in respiratory signal with smaller fractal dimension. In order to verify this result, we benefit from approximate entropy. The results show the respiratory signal will have smaller approximate entropy by choosing the music with smaller approximate entropy. The method of analysis could be further investigated to analyze the variations of different physiological time series due to the various types of stimuli when the complexity is the main concern.

It is known that geometry of cutting tool affects the cutting forces in machining operations. In addition, the value of cutting forces changes during machining operations and creates a chaotic time series (signal). In this paper, we analyze the variations of the complex structure of cutting force signal in rough end milling operation using fractal theory. In fact, we analyze the variations of cutting force signal due to variations of tool geometry (square end mill versus serrated end mill). In case of each type of end mill, we did the machining operation in wet and dry conditions. Based on the results, the fractal structure of cutting force signal changes based on the type of milling tool. We also did the complexity analysis using approximate entropy to check the variations of the complexity of cutting force signal, where the similar behavior of variations between different conditions was obtained. The method of analysis that was used in this research can be applied to other machining operations to study the influence of different machining parameters on variations of fractal structure of cutting force.

Coronavirus disease (COVID-19) is a pandemic disease that has affected almost all around the world. The most crucial step in the treatment of patients with COVID-19 is to investigate about the coronavirus itself. In this research, for the first time, we analyze the complex structure of the coronavirus genome and compare it with the other two dangerous viruses, namely, dengue and HIV. For this purpose, we employ fractal theory, sample entropy, and approximate entropy to analyze the genome walk of coronavirus, dengue virus, and HIV. Based on the obtained results, the genome walk of coronavirus has greater complexity than the other two deadly viruses. The result of statistical analysis also showed the significant difference between the complexity of genome walks in case of all complexity measures. The result of this analysis opens new doors to scientists to consider the complexity of a virus genome as an index to investigate its danger for human life.

The coronavirus has influenced the lives of many people since its identification in 1960. In general, there are seven types of coronavirus. Although some types of this virus, including 229E, NL63, OC43, and HKU1, cause mild to moderate illness, SARS-CoV, MERS-CoV, and SARS-CoV-2 have shown to have severer effects on the human body. Specifically, the recent known type of coronavirus, SARS-CoV-2, has affected the lives of many people around the world since late 2019 with the disease named COVID-19. In this paper, for the first time, we investigated the variations among the complex structures of coronaviruses. We employed the fractal dimension, approximate entropy, and sample entropy as the measures of complexity. Based on the obtained results, SARS-CoV-2 has a significantly different complex structure than SARS-CoV and MERS-CoV. To study the high mutation rate of SARS-CoV-2, we also analyzed the long-term memory of genome walks for different coronaviruses using the Hurst exponent. The results demonstrated that the SARS-CoV-2 shows the lowest memory in its genome walk, explaining the errors in copying the sequences along the genome that results in the virus mutation.

It is known that heart activity changes during aging. In this paper, we evaluated alterations of heart activity from the complexity point of view. We analyzed the variations of heart rate of patients with congestive heart failure that are categorized into four different age groups, namely 30–39, 50–59, 60–69, and 70–79 years old. For this purpose, we employed three complexity measures that include fractal dimension, sample entropy, and approximate entropy. The results showed that the trend of increment of subjects’ age is reflected in the trend of increment of the complexity of heart rate variability (HRV) since the values of fractal dimension, approximate entropy, and sample entropy increase as subjects get older. The analysis of the complexity of other physiological signals can be further considered to investigate the variations of activity of other organs due to aging.

Analysis of leg muscle activation and gait variability during locomotion is an important area of research in physiological and sport sciences. In this paper, we analyzed the coupling between the alterations of leg muscle activation and gait variability in single-task and dual-task walking. Since leg muscle activation in the form of electromyogram (EMG) signals and gait variability in the form of stride interval time series have complex structures, fractal theory and approximate entropy were used to evaluate their correlation at various walking conditions. Sixty subjects walked at their preferred speed for 10 min under the single-task condition and for 90s under the cognitive dual-task condition, and we evaluated the variations of the fractal dimension and approximate entropy of EMG signals and stride interval time series. According to the results, dual-task walking caused reductions in the complexity of EMG signals and stride interval time series than single-task walking. This technique can be used to evaluate the correlation between other organs during different locomotion.

One of the important areas of heart research is investigating how heart activity changes during aging. In this research, we employed complexity-based techniques to analyze how heart activity varies based on the age of subjects. For this purpose, the heart rate variability (HRV) of 54 healthy subjects (30 M, 24 F, 28.5–76 years old) in three different age groups was analyzed using fractal theory, sample entropy, and approximate entropy. We showed that the fractal dimension, sample entropy, and approximate entropy of the RR interval time series (as HRV) are related to the age of the subjects. In other words, as subjects get older, the complexity of their RR interval time series decreases. Therefore, we decoded the variations in HRV during aging. The method of analysis that was employed in this research can be used to analyze the variations of other physiological signals (e.g. Electroencephalogram (EEG) signals) during aging.

In this research, we apply complexity-based techniques to study the activations of the brain while the subjects perform different types of locomotion, including walking, jogging, and running. Therefore, we can study the effect of locomotion speed (or toughness level) on brain’s reactions. For this purpose, we analyzed the fractal dimension and approximate entropy of electroencephalogram (EEG) signals recorded from subjects while they walked, jogged, and ran for 20 s in the case of each activity. The analysis of 21 recorded samples showed that the complexity of EEG signals increases by increasing the locomotion speed. This result indicates a higher level of processing in the brain while the subjects perform a harder task. This analysis can be extended to the case of other physiological signals to study the effect of the level of exercise on different organs’ activations.

In this research, we investigated the effect of changes in walking speed on variations of the complexity of electromyogram (EMG) signals recorded from the right and left legs of subjects. We specifically employed fractal theory and approximate entropy to analyze the changes in the complexity of EMG signals recorded from 13 subjects walked at 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 km/h on a flat surface. The results showed that by increasing of walking speed, the complexity of EMG signals decreases. The statistical analysis also indicated the significant effect of variations in walking speed on the variations of the complexity of EMG signals. This method analysis can be applied to other physiological signals of humans (e.g. electroencephalogram (EEG) signals) to investigate the effect of walking speed on other organs’ activations (e.g. brain).

Analysis of the brain activity to external stimulation is an important area of research in biomedical engineering. In this paper, for the first time, we analyzed the brain reaction to visual stimuli with different frequencies using three complexity methods. For this purpose, we utilized fractal theory, sample entropy, and approximate entropy to study the variations of the complexity EEG signals while subjects received visual stimuli at 7, 9, 11, and 13 Hz. The results showed that, in general, by moving from 9 Hz to 13 Hz stimuli, the complexity of EEG signals increases, except in the case of 11 Hz stimulus. The statistical analysis also supported the results of the analysis. The conducted analysis in this research can be performed in the case of other types of external stimuli to study how the brain reacts in different conditions.

Analysis of the changes in brain activity between rest and various conditions is an important area of research. We investigated the changes in brain activity among rest and multitask workload (SIMKAP multitasking test) by quantifying the complexity of EEG signals. The results showed that EEG signals have smaller fractal dimensions, sample entropy, and approximate entropy during the SIMKAP multitasking test than the rest. Therefore, the complexity of EEG signals was lower during the SIMKAP multitasking test than the rest. In further research, we can study the changes in brain activity among other conditions, which has great benefits in decoding brain activity in various conditions.

Studying the activity of organs during aging is a very important research area. On the other hand, simultaneous analysis of the activities of various organs is important to understand how their activities are correlated. For the first time, this research analyzes the brain-heart correlation in younger and older subjects. We analyzed the sample entropy (SampEn) and approximate entropy (ApEn) of EEG and R-R signals (as heart rate variability (HRV)) of younger and older participants while they sat comfortably in an armchair with their eyes open. The results indicated that older subjects’ EEG and R-R signals have greater values of sample and approximate entropies than younger subjects. Therefore, as subjects age, their EEG and R-R signals become more complex. This analysis can be extended to investigate the correlation between other physiological signals among different age groups.

The analysis of extraocular muscles’ activation is crucial for understanding eye movement patterns, providing insights into oculomotor control, and contributing to advancements in fields such as vision research, neurology, and biomedical engineering. Ten subjects went through the experiments, including normal watching, blinking, upward and downward movements of eyes, and eye movements to the left and right while their electromyogram (EMG) signals were recorded. We analyzed the complexity of recorded EMG signals using fractal theory, sample entropy, and approximate entropy (ApEn). The results showed that the techniques are able to decode the changes in the complexity of EMG signals between different eye movements. In other words, we can use these methods to study extraocular muscle activations in different conditions.

Analysis of the brain activity in different mental tasks is an important area of research. We used complexity-based analysis to study the changes in brain activity in four mental tasks: relaxation, Stroop color-word, mirror image recognition, and arithmetic tasks. We used fractal theory, sample entropy, and approximate entropy to analyze the changes in electroencephalogram (EEG) signals between different tasks. Our analysis showed that by moving from relaxation to the Stroop color-word, arithmetic, and mirror image recognition tasks, the complexity of EEG signals increases, respectively, reflecting rising brain activity between these conditions. Furthermore, only the fractal theory could decode the significant changes in brain activity between different conditions. Similar analyses can be done to decode the brain activity in case of other conditions.

The examination of brain responses in individuals with a pornography addiction compared to those without sheds light on the neurobiological aspects associated with this behavior. Neuroscientific studies utilizing techniques such as electroencephalography (EEG) have shown that porn-addicted individuals may exhibit alterations in neural pathways related to reward processing and impulse control. In this paper, we analyzed the variations in the brain response of porn-addicted versus healthy individuals under five function tasks including baseline, emotional state, memorize task, executive task, and recall task. For this purpose, we analyzed the complexity of EEG signals using fractal theory, approximate entropy (ApEn), and sample entropy. The results showed that the EEG signals of porn-addicted teenagers are more complex than the ones for healthy individuals, which reflects a higher level of brain activity for porn-addicted teenagers. This method of analysis can be extended to examine the brain activity of other types of addiction versus healthy brains.

The influence of video games on the human brain has been a topic of extensive research and discussion. Video games, characterized by their dynamic and immersive qualities, have demonstrated the capacity to impact diverse cognitive processes. In this study, we conducted a detailed analysis of brain response variations to different genres of computer games, specifically focusing on boring, calm, horror, and funny games. To achieve this, we computed the sample entropy and approximate entropy of electroencephalograms (EEG) signals recorded from participants while they engaged with each type of game. Our findings revealed that EEG signals exhibited the highest complexity during the funny game and the lowest complexity during the calm game. This suggests that the brain is most active when playing the funny game and least active during the calm game. These results provide valuable insights into how different types of video game content can influence brain activity. The methodology employed in this study can be extended to explore brain activity under various conditions, potentially offering a broader understanding of how different stimuli impact cognitive processes. This approach can be useful in examining the effects of various interactive media on brain function and could inform the design of video games and other digital experiences to optimize cognitive engagement and mental well-being.