Search results for: brain signals

Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

Abstract:

This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

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Authors: Toufik Bensana, Medkour Mihoub, Slimane Mekhilef

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The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally nonstationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA), and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, rolling element bearing, local mean decomposition, condition monitoring

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Authors: Anastasiya Paltarzhitskaya

Abstract:

Subjective time plays an important role in consciousness processes and self-awareness at the moment. The concept of intrinsic neural timescales (INT) explains the difference in perceiving various time intervals. The capacity to experience the present builds on the fundamental properties of temporal cognition. The challenge that both philosophy and neuroscience try to answer is how the brain differentiates the present from the past and future. In our work, we analyze papers which describe mechanisms involved in the perception of ‘present’ and ‘non-present’, i.e., future and past moments. Taking into account that we perceive time intervals even during rest or relaxation, we suppose that the default-mode network activity can code time features, including the present moment. We can compare some results of time perceptual studies, where brain activity was shown in states with different flows of time, including resting states and during “mental time travel”. According to the concept of mental traveling, we employ a range of scenarios which demand episodic memory. However, some papers show that the hippocampal region does not activate during time traveling. It is a controversial result that is further complicated by the phenomenological aspect that includes a holistic set of information about the individual’s past and future.

Keywords: temporal consciousness, time perception, memory, present

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Authors: Jacqueline Rose T. Alipo-on, Francesca Isabelle F. Escobar, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar Al Dahoul

Abstract:

Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases, which are considered one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis of ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heartbeat types. The dataset used in this work is the synthetic MIT-BIH Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: heartbeat classification, convolutional neural network, electrocardiogram signals, generative adversarial networks, long short-term memory, ResNet-50

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Authors: Skiker Kaoutar, Mounir Maouene

Abstract:

Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from McRae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-worls, resilience to damage

Procedia PDF Downloads 519

Authors: Ilham Akbar Rahman, Aflah Dhea Bariz Yasta, Iin Fadhilah Utami Tamasse, Devina Juanita

Abstract:

Meningioma is adult brain tumors originating from the meninges covering the brain and spinal cord. The females have approximately twice higher 2:1 than male in the incidence of meningioma. This study aimed to analyze the histopathological grading and clinical aspect in predicting the prognosis of meningioma patients. An observational study with cross sectional design was used on 53 meningioma patients treated at Dr. Wahidin Sudirohusodo hospital in 2016. The data then were analyzed using SPSS 20.0. Of 53 patients, mostly 41 (77,4%) were female and 12 (22,6%) were male. The distribution of histopathology patients showed the meningothelial meningioma of 18 (43,9%) as the most type found. Fibroplastic meningioma were 8 (19,5%), while atypical meningioma and psammomatous meningioma were 6 (14,6%) each. The rest were malignant meningioma and angiomatous meningioma which found in respectively 2 (4,9%) and 1 (2,4%). Our result found significant finding that mostly male were fibroblastic meningioma (50%), however meningothelial meningioma were found in the majority of female (54,8%) and also seizure comprised only in higher grade meningioma. On the outcome of meningioma patient treated operatively, histopathological grade remained insignificant (p > 0,05). This study can be used as prognostic value of meningioma patients based on gender, histopathological grade, and clinical manifestation. Overall, the outcome of the meningioma’s patients is good and promising as long as it is well managed.

Keywords: meningioma, prognostic value, histopathological grading, clinical manifestation

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Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

Abstract:

In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

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Authors: Escada A. L. A., Pereira C. A., Jorge A. O. C., Alves Claro A. P. R.

Abstract:

In the present work, a susceptibility and efficacy of the Ti–7.5Mo alloy nanotube and Ti–7.5Mo alloy to bacterial biofilm formation after surface treatment was evaluated. The Ti–7.5Mo alloy was obtained in arc furnace under an argon atmosphere. Ingots were then homogenized under vacuum at 1100 ◦C for 86.4 ks to eliminate chemical segregation and after cold worked discs were cutting. Nanotubes were processed using anodic oxidation in 0.25% NH4F electrolyte solution. Biofilms were grown in discs immersed in sterile brain heart infusion broth (BHI) containing 5% sucrose, inoculated with microbial suspension (106 cells/ml) and incubated for 5 days. Next, the discs were placed in tubes with sterile physiological solution 0.9% sodium chloride (NaCl) and sonicated for to disperse the biofilms. Tenfold serial dilutions were carried and aliquots seeded in selective agar, which were then incubated for 48 h. Then, the numbers CFU/ml (log 10) were counted and analyzed statistically. Scanning electron microscopy (SEM) on discs with biofilms groupswas performed, atomic force microscope (AFM) and contact angle. The results show that there is no difference in bacterial adhesion between Ti–7.5Mo alloy nanotube pure titanium and Ti–7.5Mo alloy.

Keywords: biofilm, titanium alloy, brain heart infusion, scanning electron microscopy

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Authors: Umar Rehman, Holly Roy, K. T. Tsang, D. S. Jeyaretna, W Singleton, B. Fisher, P. A. Glew, J. Greig, Peter C. Whitfield

Abstract:

Introduction: A brain abscess is a life-threatening condition, carrying significant mortality. It requires rapid identification and treatment. Management involves a combination of antibiotics and surgery. The aim of the current study was to identify common bacteria responsible for cerebral abscesses as well as the long term functional and neurological outcomes of patients following treatment in a retrospective series at a single UK neurosurgical centre. Methodology: We analysed patients that had received a diagnosis of 'cerebral abscess' or 'subdural empyema' between June 2002 and June 2018. This was done in the form of a retrospective review. The search resulted in a total of 180 patients; with 37 patients being excluded (spinal abscess, below 18 or non-abscess related admissions). Data were collected from medical case notes including information about demographics, comorbidities, immunosuppression, presentation, size/location of lesions, pathogens, treatment, and outcomes. Results: In total, we analysed 143 patients between the ages of 18-90. Focal neurological deficit and headaches were seen in 84% and 68% of patients respectively. 108 positive brain cultures were seen; with the largest proportion, 59.2% being gram-positive cocci, with strep intermedius being the most common pathogen identified in 13.9% of patients. Of the patients with positive blood cultures (n=11), 72.7% showed the same organism both in the blood and on the brain cultures. Long term outcomes (n=72) revealed that 48% of patients seizure-free without requiring anti-epileptics, 51.3% of patients had full recovery of their neurological symptoms. There was a mortality rate of 13.9% in the series. Conclusion: In conclusion, the largest bacterial cause of abscess within our population was due to gram-positive cocci. The majority of the patient demonstrated full neurological recovery with close to half of patients not requiring anti-epileptics following discharge.

Keywords: bacteria, cerebral abscess, long term outcome, neurological deficit

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Authors: Heidi Ahonen

Abstract:

Since antiquity, many cultures have seemingly known the power of low frequencies, incorporating them in healing practices through drumming, singing, humming, etc. Many music therapists recognize there is something in music that is transformative enough to make a difference in people’s lives. This paper summarizes the key findings of several low-frequency research with various client populations conducted by the author. Utilizing low-frequency sound (30 or 40 Hz) may have diverse therapeutic impacts: (1) Calming effect – decreased agitation (autism, brain injury, AD, dementia) (2) Muscle relaxation (CP & spasticity & pain/after surgery patients, MS, fibromyalgia) (3) Relaxation/stress release (anxiety, stress, PTSD, trauma, insomnia) (4) Muscular/motor functioning/ decrease of tremor (CP, MS, Parkinson) (5) Increase in alertness, cognitive awareness & short-term memory function (brain injury, severe global developmental delay, AD) (6) Increased focus (AD, PTSD, trauma). The paper will conclude by presenting ideas informing the clinical practice. Future studies need to investigate what frequencies are effective for particular client populations and why, what theories can explain the effect, and finally, something that has been long debated - is it auditive or kinaesthetic stimulation or the combination of both that is effective?

Keywords: low frequency, 40 Hz, sound, neuro disability

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Authors: Cristian Păuna

Abstract:

In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: Qasem Qananwah, Ahmad Dagamseh, Hiam AlQuran, Khalid Shaker Ibrahim

Abstract:

Blood pressure helps the physicians greatly to have a deep insight into the cardiovascular system. The determination of individual blood pressure is a standard clinical procedure considered for cardiovascular system problems. The conventional techniques to measure blood pressure (e.g. cuff method) allows a limited number of readings for a certain period (e.g. every 5-10 minutes). Additionally, these systems cause turbulence to blood flow; impeding continuous blood pressure monitoring, especially in emergency cases or critically ill persons. In this paper, the most important statistical features in the photoplethysmogram (PPG) signals were extracted to estimate the blood pressure noninvasively. PPG signals from more than 40 subjects were measured and analyzed and 12 features were extracted. The features were fed to principal component analysis (PCA) to find the most important independent features that have the highest correlation with blood pressure. The results show that the stiffness index means and standard deviation for the beat-to-beat heart rate were the most important features. A model representing both features for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) was obtained using a statistical regression technique. Surface fitting is used to best fit the series of data and the results show that the error value in estimating the SBP is 4.95% and in estimating the DBP is 3.99%.

Keywords: blood pressure, noninvasive optical system, principal component analysis, PCA, continuous monitoring

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

Abstract:

Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Aline M. Dantas, Alexander T. Sack, Elisabeth Bruggen, Peiran Jiao, Teresa Schuhmann

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Risk-taking behavior has been associated with the activity of specific prefrontal regions of the brain, namely the right dorsolateral prefrontal cortex (DLPFC) and the ventromedial prefrontal cortex (VMPFC). While the deactivation of the rDLPFC has been shown to lead to increased risk-taking behavior, the functional relationship between VMPFC activity and risk-taking behavior is yet to be clarified. Correlational evidence suggests that the VMPFC is involved in valuation processes that involve risky choices, but evidence on the functional relationship is lacking. Therefore, this study uses brain stimulation to investigate the role of the VMPFC during risk-taking behavior and replicate the current findings regarding the role of the rDLPFC in this same phenomenon. We used continuous theta-burst stimulation (cTBS) to inhibit either the VMPFC or DLPFC during the execution of the computerized Maastricht Gambling Task (MGT) in a within-subject design with 30 participants. We analyzed the effects of such stimulation on risk-taking behavior, participants’ choices of probabilities and average values, and response time. We hypothesized that, compared to sham stimulation, VMPFC inhibition leads to a reduction in risk-taking behavior by reducing the appeal to higher-value options and, consequently, the attractiveness of riskier options. Right DLPFC (rDLPFC) inhibition, on the other hand, should lead to an increase in risk-taking due to a reduction in cognitive control, confirming existent findings. Stimulation of both the rDLPFC and the VMPFC led to an increase in risk-taking behavior and an increase in the average value chosen after both rDLPFC and VMPFC stimulation compared to sham. No significant effect on chosen probabilities was found. A significant increase in response time was observed exclusively after rDLPFC stimulation. Our results indicate that inhibiting DLPFC and VMPFC separately leads to similar effects, increasing both risk-taking behavior and average value choices, which is likely due to the strong anatomical and functional interconnection of the VMPFC and rDLPFC.

Keywords: decision-making, risk-taking behavior, brain stimulation, TMS

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Authors: Benmansour Nassima, Benmansour Souheyla

Abstract:

The appropriate integration of reward processing and decision making provided by the environment is vital for behavioural success and individuals’ well being in everyday life. Functional neurological investigation has already provided an inclusive image on affective and emotional (motivational) processing in the healthy human brain and has recently focused its interest also on the assessment of brain function in anxious and depressed individuals. This article offers an overview on the theoretical approaches that relate emotion and decision-making, and spotlights investigation with anxious or depressed individuals to reveal how emotions can interfere with decision-making. This research aims at incorporating the emotional structure based on response and stimulation with a Bayesian approach to decision-making in terms of probability and value processing. It seeks to show how studies of individuals with emotional dysfunctions bear out that alterations of decision-making can be considered in terms of altered probability and value subtraction. The utmost objective is to critically determine if the probabilistic representation of belief affords could be a critical approach to scrutinize alterations in probability and value representation in subjective with anxiety and depression, and draw round the general implications of this approach.

Keywords: decision-making, motivation, alteration, reward processing, response selection

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Authors: Rodrigo Arnaldo Scarpel

Abstract:

Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Skiker Kaoutar, Mounir Maouene

Abstract:

Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from Mc Rae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-world, resilience to damage

Procedia PDF Downloads 518

Authors: Andrés C. Cuervo Pinilla, Christian G. Quintero M., Chinthaka Premachandra

Abstract:

This paper considers people’s driving skills diagnosis under real driving conditions. In that sense, this research presents an approach that uses GPS signals which have a direct correlation with driving maneuvers. Besides, it is presented a novel expert-driving-criteria approximation using fuzzy logic which seeks to analyze GPS signals in order to issue an intelligent driving diagnosis. Based on above, this works presents in the first section the intelligent driving diagnosis system approach in terms of its own characteristics properties, explaining in detail significant considerations about how an expert-driving-criteria approximation must be developed. In the next section, the implementation of our developed system based on the proposed fuzzy logic approach is explained. Here, a proposed set of rules which corresponds to a quantitative abstraction of some traffics laws and driving secure techniques seeking to approach an expert-driving- criteria approximation is presented. Experimental testing has been performed in real driving conditions. The testing results show that the intelligent driving diagnosis system qualifies driver’s performance quantitatively with a high degree of reliability.

Keywords: driver support systems, intelligent transportation systems, fuzzy logic, real time data processing

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Authors: Mulligan T., Barker-Collo S., Gobson K., Jones K.

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There is a relatively scarce body of literature regarding adolescent experiences of traumatic brain injury (TBI). This qualitative study explored how sustaining a TBI at this unique stage of development might impact a young person as they navigate the challenges of adolescence and transition to adulthood, and what might support recovery. Thirteen young adults who sustained a mild-moderate TBI as an adolescent (aged 13 – 17 years), approximately 7.7 years (range = 6.7 – 8.0 years) prior, participated in the research. Semi-structured individual interviews were conducted to explore participants’ experiences surrounding and following their TBIs. Thematic analysis of interview data produced five key categories of findings: (1) Following their TBIs, many participants experienced problems with cognitive (e.g., forgetfulness, concentration difficulties), physical (e.g., migraines, fatigue) and emotional (e.g., depression, anxiety) functioning, which were often endured into adulthood. (2) TBI-related problems often adversely affected important areas of life for the participant, including school, work and friendships. (3) Changes following TBI commonly impacted identity formation. (4) Recovery processes evolved over time as the participants coped initially by just ‘getting on with it’, before learning to accept new limitations and, ultimately, growing from their TBI experiences. (5) While the presence of friends and family assisted recovery, struggles were often exacerbated by a lack of emotional support from others, in addition to the absence of any assistance or information-provision from professionals regarding what to expect following TBI. The findings suggest that even mild TBI sustained during adolescence can have consequences for an individual’s functioning, engagement in life and identity development, whilst also giving rise to post-traumatic growth. Recovery following adolescent TBI might be maximised by facilitating greater understanding of the injury and acknowledging its impacts on important areas of life, as well as the provision of emotional support and facilitating self-reflection and meaning-making.

Keywords: adolescent, brain Injury, qualitative, post-traumatic growth

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Authors: Nicole Selvi Hill, Archchana Radhakrishnan

Abstract:

Introduction: Traumatic brain injury (TBI) is a leading cause of mortality among trauma patients. In the management of TBI, the main principle is avoiding cerebral ischemia, as this is a strong determiner of neurological outcomes. The use of vasoactive drugs, such as vasopressin, has an important role in maintaining cerebral perfusion pressure to prevent secondary brain injury. Current guidelines do not suggest a preferred vasoactive drug to administer in the management of TBI, and there is a paucity of information on the therapeutic potential of vasopressin following TBI. Vasopressin is also an endogenous anti-diuretic hormone (AVP), and pathways mediated by AVP play a large role in the underlying pathological processes of TBI. This creates an overlap of discussion regarding the therapeutic potential of vasopressin following TBI. Currently, its popularity lies in vasodilatory and cardiogenic shock in the intensive care setting, with increasing support for its use in haemorrhagic and septic shock. Methodology: This is a review article based on a literature review. An electronic search was conducted via PubMed, Cochrane, EMBASE, and Google Scholar. The aim was to identify clinical studies looking at the therapeutic administration of vasopressin in severe traumatic brain injury. The primary aim was to look at the neurological outcome of patients. The secondary aim was to look at surrogate markers of cerebral perfusion measurements, such as cerebral perfusion pressure, cerebral oxygenation, and cerebral blood flow. Results: Eight papers were included in the final number. Three were animal studies; five were human studies, comprised of three case reports, one retrospective review of data, and one randomised control trial. All animal studies demonstrated the benefits of vasopressors in TBI management. One animal study showed the superiority of vasopressin in reducing intracranial pressure and increasing cerebral oxygenation over a catecholaminergic vasopressor, phenylephrine. All three human case reports were supportive of vasopressin as a rescue therapy in catecholaminergic-resistant hypotension. The retrospective review found vasopressin did not increase cerebral oedema in TBI patients compared to catecholaminergic vasopressors; and demonstrated a significant reduction in the requirements of hyperosmolar therapy in patients that received vasopressin. The randomised control trial results showed no significant differences in primary and secondary outcomes between TBI patients receiving vasopressin versus those receiving catecholaminergic vasopressors. Apart from the randomised control trial, the studies included are of low-level evidence. Conclusion: Studies favour vasopressin within certain parameters of cerebral function compared to control groups. However, the neurological outcomes of patient groups are not known, and animal study results are difficult to extrapolate to humans. It cannot be said with certainty whether vasopressin’s benefits stand above usage of other vasoactive drugs due to the weaknesses of the evidence. Further randomised control trials, which are larger, standardised, and rigorous, are required to improve knowledge in this field.

Keywords: catecholamines, cerebral perfusion pressure, traumatic brain injury, vasopressin, vasopressors

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Authors: Misagh Faezipour

Abstract:

System dynamics is a systems engineering approach that can help address the complex challenges in different systems. Little is known about how the brain represents people to predict behavior. This work is based on how the brain simulates different personal behavior and responds to them in the case of young children ages one to five. As we know, children’s minds/brains are just as clean as a crystal, and throughout time, in their surroundings, families, and education center, they grow to develop and have different kinds of behavior towards the world and the society they live in. Hence, this work aims to identify how young children respond to various personality behavior and observes their reactions towards them from a system dynamics perspective. We will be exploring the Big Five personality traits in young children. A causal model is developed in support of the system dynamics approach. These models graphically present the factors and factor relationships that contribute to the big five personality traits and provide a better understanding of the entire behavior model. A simulator will be developed that includes a set of causal model factors and factor relationships. The simulator models the behavior of different factors related to personality traits and their impacts and can help make more informed decisions in a risk-free environment.

Keywords: personality traits, systems engineering, system dynamics, causal model, behavior model

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Authors: Likith Reddy Pinninti, Fredrik Ribsskog Staven, Leslie Robert Noble, Jorge Manuel de Oliveira Fernandes, Deepti Manjari Patel, Torstein Kristensen

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Understanding fish behavior is essential to improve animal welfare in aquaculture research. Behavioral traits can have a strong influence on fish health and habituation. To identify the genes and biological pathways responsible for lumpfish behavior, we performed an experiment to understand the interspecies relationship (mutualism) between the lumpfish and salmon. Also, we tested the correlation between the gene expression data vs. observational/physiological data to know the essential genes that trigger stress and swimming behavior in lumpfish. After the de novo assembly of the brain transcriptome, all the samples were individually mapped to the available lumpfish (Cyclopterus lumpus L.) primary genome assembly (fCycLum1.pri, GCF_009769545.1). Out of ~16749 genes expressed in brain samples, we found 267 genes to be statistically significant (P > 0.05) found only in odor and control (1), model and control (41) and salmon and control (225) groups. However, genes with |LogFC| ≥0.5 were found to be only eight; these are considered as differentially expressed genes (DEG’s). Though, we are unable to find the differential genes related to the behavioral traits from RNA-Seq data analysis. From the correlation analysis, between the gene expression data vs. observational/physiological data (serotonin (5HT), dopamine (DA), 3,4-Dihydroxyphenylacetic acid (DOPAC), 5-hydroxy indole acetic acid (5-HIAA), Noradrenaline (NORAD)). We found 2495 genes found to be significant (P > 0.05) and among these, 1587 genes are positively correlated with the Noradrenaline (NORAD) hormone group. This suggests that Noradrenaline is triggering the change in pigmentation and skin color in lumpfish. Genes related to behavioral traits like rhythmic, locomotory, feeding, visual, pigmentation, stress, response to other organisms, taxis, dopamine synthesis and other neurotransmitter synthesis-related genes were obtained from the correlation analysis. In KEGG pathway enrichment analysis, we find important pathways, like the calcium signaling pathway and adrenergic signaling in cardiomyocytes, both involved in cell signaling, behavior, emotion, and stress. Calcium is an essential signaling molecule in the brain cells; it could affect the behavior of fish. Our results suggest that changes in calcium homeostasis and adrenergic receptor binding activity lead to changes in fish behavior during stress.

Keywords: behavior, De novo, lumpfish, salmon

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Authors: Muhammad Fajar Suardi, Fathimatufzzahra, Dela Isnaini Sendra

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Learning is an activity that has been used to do, especially for a student or academics. But a handful of people have not been using and maximizing their brains work and some also do not know a good brain work time in capturing the lessons, so that knowledge is absorbed is also less than the maximum. Indonesia Memory Sports Council (IMSC) is an institution which is engaged in the performance of the brain and the development of effective learning methods by using several techniques that can be used in considering the lessons and knowledge to grasp well, including: loci method, substitution method, and chain method. This study aims to determine the techniques and benefits of using the method given in learning and memorization by applying memory techniques taught by Indonesia Memory Sports Council (IMSC) to students and the difference if not using this method. This research uses quantitative research with survey method addressed to students of Indonesian Memory Sports Council (IMSC). The results of this study indicate that learn, understand and remember the lesson using the techniques of memory which is taught in Indonesia Memory Sport Council is very effective and faster to absorb the lesson than learning without using the techniques of memory, and this affects the academic achievement of students in each educational institution.

Keywords: chain method, Indonesia memory sports council, loci method, substitution method

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Authors: Aman Kaur, Colin Ribton, Wamadeva Balachandaran

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Electron beam welding due to its inherent advantages is being extensively used for material processing where high precision is required. Especially in aerospace or nuclear industries, there are high quality requirements and the cost of materials and processes is very high which makes it very important to ensure the beam quality is maintained and checked prior to carrying out the welds. Although the processes in these industries are highly controlled, however, even the minor changes in the operating parameters of the electron gun can make large enough variations in the beam quality that can result in poor welding. To measure the beam quality a simple device has been designed that can be used at high powers. The device consists of two slits in x and y axis which collects a small portion of the beam current when the beam is deflected over the slits. The signals received from the device are processed in data acquisition hardware and the dedicated software developed for the device. The device has been used in controlled laboratory environments to analyse the signals and the weld quality relationships by varying the focus current. The results showed matching trends in the weld dimensions and the beam characteristics. Further experimental work is being carried out to determine the ability of the device and signal processing software to detect subtle changes in the beam quality and to relate these to the physical weld quality indicators.

Keywords: electron beam welding, beam quality, high power, weld quality indicators

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Authors: Mark Andrew

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Geopolitical tensions are at a thirty-year high, and the pace of technological innovation is driving asymmetry in force capabilities between nation states and between non-state actors. Technology futures are a vital component of defence capability growth, and investments in technology futures need to be informed by accurate and reliable forecasts of the options for ‘systems of systems’ innovation, development, and deployment. This paper describes a method for forecasting technology futures developed through an analysis of four key systems’ development stages, namely: technology domain categorisation, scanning results examining novel systems’ signals and signs, potential system-of systems’ implications in warfare theatres, and political ramifications in terms of funding and development priorities. The method has been applied to several technology domains, including physical systems (e.g., nano weapons, loitering munitions, inflight charging, and hypersonic missiles), biological systems (e.g., molecular virus weaponry, genetic engineering, brain-computer interfaces, and trans-human augmentation), and information systems (e.g., sensor technologies supporting situation awareness, cyber-driven social attacks, and goal-specification challenges to proliferation and alliance testing). Although the current application of the method has been team-centred using paper-based rapid prototyping and iteration, the application of autonomous language models (such as GPT-3) is anticipated as a next-stage operating platform. The importance of forecasting accuracy and reliability is considered a vital element in guiding technology development to afford stronger contingencies as ideological changes are forecast to expand threats to ecology and earth systems, possibly eclipsing the traditional vulnerabilities of nation states. The early results from the method will be subjected to ground truthing using longitudinal investigation.

Keywords: forecasting, technology futures, uncertainty, complexity

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Authors: Mark Pizzato

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How do ideas, images, and emotions in stage-plays and videos affect us? Do they evoke a greater awareness (or cognitive reappraisal of emotions) through possible shifts between left-cortical, right-cortical, and subcortical networks? To address these questions, this presentation summarizes the research of various neuroscientists, especially Bernard Baars and others involved in Global Workspace Theory, Matthew Lieberman in social neuroscience, Iain McGilchrist on left and right cortical functions, and Jaak Panksepp on the subcortical circuits of primal emotions. Through such research, this presentation offers an ‘inner theatre’ model of the brain, regarding major hubs of neural networks and our animal ancestry. It also considers recent experiments, by Mario Beauregard, on the cognitive reappraisal of sad, erotic, and aversive film clips. Finally, it applies the inner-theatre model and related research to survey results of theatre students who read and then watched the ‘To be or not to be’ speech in 8 different video versions (from stage and screen productions) of William Shakespeare’s Hamlet. Findings show that students become aware of left-cortical, right-cortical, and subcortical brain functions—and shifts between them—through staging and movie-making choices in each of the different videos.

Keywords: cognitive reappraisal, Hamlet, neuroscience, Shakespeare, theatre

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Authors: Olga Maksakova

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A work with unconscious patients after acute brain damages besides special knowledge and practical skills of all the participants requires a very specific organization. A lot of said about team approach in neurorehabilitation, usually as for outpatient mode. Rehabilitologists deal with fixed patient problems or deficits (motion, speech, cognitive or emotional disorder). Team-building means superficial paradigm of management psychology. Linear mode of teamwork fits casual relationships there. Cases with deep altered states of consciousness (vegetative states, coma, and confusion) require non-linear mode of teamwork: recovery of consciousness might not be the goal due to phenomenon uncertainty. Rehabilitation team as Semi-open Complex System includes the patient as a part. Patient's response pattern becomes formed not only with brain deficits but questions-stimuli, context, and inquiring person. Teamwork is sourcing of phenomenology knowledge of patient's processes as Third-person approach is replaced with Second- and after First-person approaches. Here is a chance for real-time change. Patient’s contacts with his own body and outward things create a basement for restoration of consciousness. The most important condition is systematic feedbacks to any minimal movement or vegetative signal of the patient. Up to now, recovery work with the most severe contingent is carried out in the mode of passive physical interventions, while an effective rehabilitation team should include specially trained psychologists and psychotherapists. It is they who are able to create a network of feedbacks with the patient and inter-professional ones building up the team. Characteristics of ‘Team-Patient’ system (TPS) are energy, entropy, and complexity. Impairment of consciousness as the absence of linear contact appears together with a loss of essential functions (low energy), vegetative-visceral fits (excessive energy and low order), motor agitation (excessive energy and excessive order), etc. Techniques of teamwork are different in these cases for resulting optimization of the system condition. Directed regulation of the system complexity is one of the recovery tools. Different signs of awareness appear as a result of system self-organization. Joint meetings are an important part of teamwork. Regular or event-related discussions form the language of inter-professional communication, as well as the patient's shared mental model. Analysis of complex communication process in TPS may be useful for creation of the general theory of consciousness.

Keywords: rehabilitation team, urgent rehabilitation, severe brain damage, consciousness disorders, complex system theory

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Authors: Shreya Borthakur, Aastha Vartak

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This Electroencephalogram (EEG) project on Rapid Visual Serial Processing (RSVP) paradigm explores the temporal dynamics of visual attention processing in response to rapidly presented visual stimuli. The study builds upon previous research that used real-world images in RSVP tasks to understand the emergence of object representations in the human brain. The objectives of the research include investigating the differences in accuracy and reaction times between 5 Hz and 20 Hz presentation rates, as well as examining the prominent brain waves, particularly alpha and beta waves, associated with the attention task. The pre-processing and data analysis involves filtering EEG data, creating epochs for target stimuli, and conducting statistical tests using MATLAB, EEGLAB, Chronux toolboxes, and R. The results support the hypotheses, revealing higher accuracy at a slower presentation rate, faster reaction times for less complex targets, and the involvement of alpha and beta waves in attention and cognitive processing. This research sheds light on how short-term memory and cognitive control affect visual processing and could have practical implications in fields like education.

Keywords: RSVP, attention, visual processing, attentional blink, EEG

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Ugo Galvanetto, Pirto G. Pavan, Mirco Zaccariotto

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The discipline of Computer-integrated surgery (CIS) will provide equipment able to improve the efficiency of healthcare systems and, which is more important, clinical results. Surgeons and machines will cooperate in new ways that will extend surgeons’ ability to train, plan and carry out surgery. Patient specific CIS of the brain requires several steps: 1 - Fast generation of brain models. Based on image recognition of MR images and equipped with artificial intelligence, image recognition techniques should differentiate among all brain tissues and segment them. After that, automatic mesh generation should create the mathematical model of the brain in which the various tissues (white matter, grey matter, cerebrospinal fluid …) are clearly located in the correct positions. 2 – Reliable and fast simulation of the surgical process. Computational mechanics will be the crucial aspect of the entire procedure. New algorithms will be used to simulate the mechanical behaviour of cutting through cerebral tissues. 3 – Real time provision of visual and haptic feedback A sophisticated human-machine interface based on ergonomics and psychology will provide the feedback to the surgeon. The present work will address in particular point 2. Modelling the cutting of soft tissue in a structure as complex as the human brain is an extremely challenging problem in computational mechanics. The finite element method (FEM), that accurately represents complex geometries and accounts for material and geometrical nonlinearities, is the most used computational tool to simulate the mechanical response of soft tissues. However, the main drawback of FEM lies in the mechanics theory on which it is based, classical continuum Mechanics, which assumes matter is a continuum with no discontinuity. FEM must resort to complex tools such as pre-defined cohesive zones, external phase-field variables, and demanding remeshing techniques to include discontinuities. However, all approaches to equip FEM computational methods with the capability to describe material separation, such as interface elements with cohesive zone models, X-FEM, element erosion, phase-field, have some drawbacks that make them unsuitable for surgery simulation. Interface elements require a-priori knowledge of crack paths. The use of XFEM in 3D is cumbersome. Element erosion does not conserve mass. The Phase Field approach adopts a diffusive crack model instead of describing true tissue separation typical of surgical procedures. Modelling discontinuities, so difficult when using computational approaches based on classical continuum Mechanics, is instead easy for novel computational methods based on Peridynamics (PD). PD is a non-local theory of mechanics formulated with no use of spatial derivatives. Its governing equations are valid at points or surfaces of discontinuity, and it is, therefore especially suited to describe crack propagation and fragmentation problems. Moreover, PD does not require any criterium to decide the direction of crack propagation or the conditions for crack branching or coalescence; in the PD-based computational methods, cracks develop spontaneously in the way which is the most convenient from an energy point of view. Therefore, in PD computational methods, crack propagation in 3D is as easy as it is in 2D, with a remarkable advantage with respect to all other computational techniques.

Keywords: computational mechanics, peridynamics, finite element, biomechanics

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