Comparison of Different Measurement Tools and Dimensional Parameters of Pterygium to Investigate its Impact on Refractive Indices and Ocular Aberrations.

OBJECTIVES: The aim of this study was to compare different measurement tools and parameters, including a new computer-assisted image processing technique for the quantitative analysis of the percentage of pterygium on the corneal surface, horizontal/vertical lengths obtained using slitlamp beam and surgical compass. METHODS: A total of 21 pterygia of 17 patients were included in the study. The pterygia were measured by three different methods: a slitlamp beam, a surgical compass, and a new computer-assisted image processing method. Refractive indices and higher-order aberrations were analyzed in all cases. RESULTS: The new computer-assisted image processing technique revealed excellent intraclass correlation coefficients for intraobserver and interobserver reliability (0.999 and 0.995, respectively). However, horizontal and vertical lengths revealed more deviation between the measurements obtained with slitlamp beam and surgical compass. Although uncorrected visual acuity did not show any significant correlation between horizontal and vertical lengths of pterygia measured by either slitlamp beam or surgical compass, it was correlated with the digital pterygium ratio (rho, 0.462; P=0.035). All ocular aberration (total, higher-order, coma, trefoil, quatrefoil, spherical, and higher-order astigmatism) Root-mean-square values more strongly correlated with higher percentage values of pterygium that covers the cornea, measured by the new computer-assisted image processing technique. CONCLUSION: The percentage of pterygium covering the corneal surface seems to be more associated with the pterygium-related visual disturbances than with horizontal and vertical lengths measured by conventional techniques. Moreover, the new computer-assisted image processing technique can accurately and reliably measure the percentage extension of pterygium on cornea.

Advanced analysis of auditory evoked potentials in hyperthyroid patients: the effect of filtering.

The purpose of the study is to evaluate Auditory Evoked Potentials (AEPs) in patients with hyperthyroidism and to compare their frequency components with those of healthy subjects. In this study the AEPs in hyperthyroidism were studied both in time and frequency domains rather than studying just in the time domain by peak scoring. This paper presents a method for filtering auditory oddball standard and target AEPs by using singular spectrum analysis (SSA) and feature extraction in the frequency domain via spectral analysis. AEPs were recorded during an auditory oddball paradigm in 25 newly diagnosed hyperthyroid patients and 15 healthy subjects. The signals are captured in the presence of ongoing background EEG activity so they are often contaminated by artifacts. This paper presents a method for filtering auditory odd-ball standard and target AEPs by using Singular spectrum analysis and feature extraction in frequency domain via spectral analysis. Information about the frequency composition of the signal is then used to compare normal and hyperthyroid states. While there was no significant difference either in the target or standard unfiltered signals between the hyperthyroid patients and the control group (p > 0.05), there was a significant difference in the filtered signals between the two groups (p < 0.01). In conclusion, our results revealed that SSA is an effective filtering method for AEPs. Thus, a much more objective and specific examination method was developed.

Selective cerebral extracorporeal circulation-enhanced total endovascular arch replacement using in situ fenestration.

In this article, we present a newly designed cerebral perfusion technique during the in situ fenestration procedure with three covered stent placement in an endovascular total aortic arch repair of a 68-year-old male patient. This technique enables the endovascular repair of the ascending aorta and aortic arch pathologies with commonly available thoracic aorta stent grafts in a safer and more effective manner.

Multimodal analysis of cortical activation in young male adults with internet gaming disorder: A resting state EEG-fNIRS study.

BACKGROUND: Internet gaming disorder (IGD) has become increasingly prevalent worldwide and is recognized as a significant public health concern because of its negative consequences on individuals mental and physical health, social relationships, academic performance and overall well-being. While research on IGD has gained significant momentum in the past decade, the neural substrates underlying this disorder remains unclear. This study aims to investigate resting-state cortical activation in male subjects with IGD using a concurrent functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) hybrid system. METHODS: Twenty-two male individuals with IGD (18-23 years old) and twenty-two male healthy, age-matched healthy controls were included in the study. Mean oxygenation of the prefrontal cortex (PFC) and whole head neural activity were measured using fNIRS and EEG respectively, during eyes-open and eyes-closed conditions at the resting state. EEG signals were decomposed into distinct frequency sub-bands with wavelet transform, followed by the analysis of the power spectral density within each band. Mean oxygenation of PFC is measured using a multichannel fNIRS system. RESULTS: Results revealed that the individuals with IGD had significantly higher beta power in the frontal region compared to the control group. Individuals with IGD showed significantly increased PFC oxygenation compared to healthy controls. Additionally, both frontal beta power and PFC oxygenation were significantly correlated with IGD severity. However, there were no significant correlations observed between oxygenation and beta powers. CONCLUSION: This study is the first to examine resting-state cortical activation using multimodal EEG-fNIRS system in young adults with IGD. Moreover, it provides an important contribution to the understanding of the underlying neural mechanisms of IGD and offer new insights for the diagnosis and intervention of the disorder using multimodal EEG-fNIRS system. Further studies should aim to replicate the findings of this study using a larger and more culturally diverse sample to support the neurophysiological basis of IGD.

Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques.

Recent achievements have made emotion studies a rising field contributing to many areas, such as health technologies, brain-computer interfaces, psychology, etc. Emotional states can be evaluated in valence, arousal, and dominance (VAD) domains. Most of the work uses only VA due to the easiness of differentiation; however, very few studies use VAD like this study. Similarly, segment comparisons of emotion analysis with handcrafted features also use VA space. At this point, we primarily focused on VAD space to evaluate emotions and segmentations. The DEAP dataset is used in this study. A comprehensive analytical approach is implemented with two sub-studies: first, segmentation (Segments I-VIII), and second, binary cross-comparisons and evaluations of eight emotional states, in addition to comparisons of selected segments (III, IV, and V), class separation levels (5, 4-6, and 3-7), and unbalanced and balanced data with SMOTE. In both sub-studies, Wavelet Transform is applied to electroencephalography signals to separate the brain waves into their bands (α, ß, γ, and θ bands), twenty-four attributes are extracted, and Sequential Minimum Optimization, K-Nearest Neighbors, Fuzzy Unordered Rule Induction Algorithm, Random Forest, Optimized Forest, Bagging, Random Committee, and Random Subspace are used for classification. In our study, we have obtained high accuracy results, which can be seen in the figures in the second part. The best accuracy result in this study for unbalanced data is obtained for Low Arousal-Low Valence-High Dominance and High Arousal-High Valence-Low Dominance emotion comparisons (Segment III and 4.5-5.5 class separation), and an accuracy rate of 98.94% is obtained with the IBk classifier. Data-balanced results mostly seem to outperform unbalanced results.

Comparison of efficacy of particulate and non-particulate steroids in patients with lumbosacral pain, non-operated patients with chronic radicular symptoms.

OBJECTIVES: In our study, we aimed to retrospectively evaluate patients who were given particulate and non-particulate steroids for transforaminal epidural steroid injection due to non-operated chronic low back pain with radicular symptoms in terms of the change in pain and the change in functional capacity before the procedure. METHODS: This study was carried out by examining the files of 130 patients, underwent an interventional procedure. Records of patients pertaining to age, gender, location of pain, Visual Analog Scale, Patient Global Impression of Change, and Oswestry Disability Index Scale (ODI) before the interventional procedure and at the 1st and 3rd months after the procedure were recorded using the hospital automation system and patient follow-up forms. RESULTS: The functional capacity of the patients was evaluated, and in the comparison of the ODI score before the procedure, at the 1st month, and at the 3rd month, a statistically significant difference was found in the particulate steroid group compared to the non-particulate group at the 1st and 3rd months. When evaluated using the Generalized Linear Models, a statistically significant difference was found in both groups (p=0.039), and the ODI score was approximately 2,951 units lower in patients who were treated with particulate steroids than those who were treated with non-particulate steroids at each measurement time. CONCLUSION: In our study, it has been demonstrated that particulate steroids are superior to non-particulate steroids in improving functional capacity in the early period, and non-particulate steroids are advantageous in the long term.

Correlation between the cord vitamin D levels and regulatory T cells in newborn infants.

Vitamin D is important for calcium homeostasis, muscle, and bone health. It has also immunomodulatory capacities in vivo and in vitro. Regulatory T cells (Treg) have been found to suppress a number of T cell-mediated immune disorders, including allergic responses and autoimmune diseases. This study aimed to investigate the correlation between 25-hydroxyvitamin D (25(OH)D) levels and the regulatory T cells in cord blood. The study group is comprised of 101 full-term newborn infants. Umbilical cord 25(OH)D levels and number and percentage of T lymphocyte, T helper, and Treg cells were measured. Infants were grouped according to 25-hydroxyvitamin D levels (25(OH)D <12 ng/ml and 25(OH)D >12 ng/ml) (converting factor of 25OHD level into SI unit, 2.6). Severe vitamin D deficiency (25(OH)D <12 ng/ml) was observed in 32% of the infants. There was no significant correlation between 25-hydroxyvitamin D levels and T cell number and percentages. There were also no significant differences in white blood cell, total lymphocyte count, T helper, and Treg cell percentage and number between groups. These results suggest that the serum level of 25-hydroxyvitamin D is not crucially involved in the correlation between vitamin D status and T cell regulation in cord blood.

Evaluation of divided attention using different stimulation models in event-related potentials.

Divided attention is defined as focusing on different tasks at once, and this is described as one of the biggest problems of today's society. Default examinations for understanding attention are questionnaires or physiological signals, like evoked potentials and electroencephalography. Physiological records were obtained using visual, auditory, and auditory-visual stimuli combinations with 48 participants-18-25-year-old university students-to find differences between sustained and divided attention. A Fourier-based filter was used to get a 0.01-30-Hz frequency band. Fractal dimensions, entropy values, power spectral densities, and Hjorth parameters from electroencephalography and P300 components from evoked potentials were calculated as features. To decrease the size of the feature set, some features, which yield less detail level for data, were eliminated. The visual and auditory stimuli in selective attention were compared with the divided attention state, and the best accuracy was found to be 88.89% on a support vector machine with linear kernel. As a result, it was seen that divided attention could be more difficult to determine from selective attention, but successful classification could be obtained with appropriate methods. Contrary to literature, the study deals with the infrastructure of attention types by working on a completely healthy and attention-high group. Graphical abstract.

Effects of Methylphenidate on Reaction Time in Children with Attention Deficit / Hyperactivity Disorder.

INTRODUCTION: Attention-deficit/hyperactivity disorder (ADHD) is associated with a broad range of neuropsychological impairments that are attenuated with methylphenidate (MPH) treatment. The aim of this study was to determine how MPH effects attentional functioning in terms of reaction time (RT) in ADHD. METHODS: Eighteen pre-medicated ADHD children (7 to 12 years old) and eighteen gender matched normal controls (7 to 12 years old) were included in the study. Participants performed an auditory attention task and the RT of participants to each target response was calculated automatically. The same test was repeated 3 months after OROS-MPH administration for ADHD group. RT, RT standard deviation (RTSD), and response errors (omission and commission errors) were compared between control and pre-MPH ADHD groups, and between Pre-MPH and post-MPH ADHD groups. RESULTS: Relative to control subjects, significantly longer RTs, higher RTSD and more errors of omission were observed in unmedicated ADHD children during auditory attention task. Analyses revealed significant effects of medication across all measures except commission errors. After treatment RTs were faster, RTSD values were lower, and errors of omission were attenuated compared to pre-medication condition in ADHD group. There were no significant differences in terms of commission errors between groups. CONCLUSION: In this study it was observed that MPH reduced RTs to stimuli, attenuated omission errors during the task in ADHD group and after 3 months of treatment ADHD children showed similar patterns in RT as compared to controls. Results suggest that when treating ADHD, it might help clinicians to evaluate objective and non-invasive cognitive outcomes such as RT, RTSD and response errors to evaluate the effects of treatment.

Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of optic nerve disease from visual-evoked potential signals.

OBJECTIVE: This paper presents a new method based on combining principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to diagnose the optic nerve disease from visual-evoked potential (VEP) signals. The aim of this study is to improve the classification accuracy of ANFIS classifier on diagnosis of optic nerve disease from VEP signals. With this aim, a new classifier ensemble based on ANFIS and PCA is proposed. METHODS AND MATERIAL: The VEP signals dataset include 61 healthy subjects and 68 patients suffered from optic nerve disease. First of all, the dimension of VEP signals dataset with 63 features has been reduced to 4 features using PCA. After applying PCA, ANFIS trained using three different training-testing datasets randomly with 50-50% training-testing partition. RESULTS: The obtained classification results from ANFIS trained separately with three different training-testing datasets are 96.87%, 98.43%, and 98.43%, respectively. And then the results of ANFIS trained with three different training-testing datasets randomly with 50-50% training-testing partition have been combined with three different ways including weighted arithmetical mean that proposed firstly by us, arithmetical mean, and geometrical mean. The classification results of ANFIS combined with three different ways are 98.43%, 100%, and 100%, respectively. Also, ensemble ANFIS has been compared with ANN ensemble. ANN ensemble obtained 98.43%, 100%, and 100% prediction accuracy with three different ways including arithmetical mean, geometrical mean and weighted arithmetical mean. CONCLUSION: These results have shown that the proposed classifier ensemble approach based on ANFIS trained with different train-test datasets and PCA has produced very promising results in the diagnosis of optic nerve disease from VEP signals.

Utilization of Discretization method on the diagnosis of optic nerve disease.

The optic nerve disease is an important disease that appears commonly in public. In this paper, we propose a hybrid diagnostic system based on discretization (quantization) method and classification algorithms including C4.5 decision tree classifier, artificial neural network (ANN), and least square support vector machine (LSSVM) to diagnose the optic nerve disease from Visual Evoked Potential (VEP) signals with discrete values. The aim of this paper is to investigate the effect of Discretization method on the classification of optic nerve disease. Since the VEP signals are non-linearly-separable, low classification accuracy can be obtained by classifier algorithms. In order to overcome this problem, we have used the Discretization method as data pre-processing. The proposed method consists of two phases: (i) quantization of VEP signals using Discretization method, and (ii) diagnosis of discretized VEP signals using classification algorithms including C4.5 decision tree classifier, ANN, and LSSVM. The classification accuracies obtained by these hybrid methods (combination of C4.5 decision tree classifier-quantization method, combination of ANN-quantization method, and combination of LSSVM-quantization method) with and without quantization strategy are 84.6-96.92%, 94.20-96.76%, and 73.44-100%, respectively. As can be seen from these results, the best model used to classify the optic nerve disease from VEP signals is obtained for the combination of LSSVM classifier and quantization strategy. The obtained results denote that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.

The effect of generalized discriminate analysis (GDA) to the classification of optic nerve disease from VEP signals.

In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of optic nerve disease from visual evoke potentials (VEP) signals. The GDA method has been used as a pre-processing step prior to the classification process of optic nerve disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of optic nerve disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of optic nerve disease from VEP signals.

Training a learning vector quantization network using the pattern electroretinography signals.

In this study, the pattern electroretinography (PERG) signals derived from evoked potential across retinal cells of subjects after visual stimulation were analyzed using artificial neural network (ANN) with 172 healthy and 148 diseased subjects. ANN was employed to PERG signals to distinguish between healthy eye and diseased eye. Supervised network examined was a competitive learning vector quantization network. The designed classification structure has about 94% sensitivity, 90.32% specifity, 5.94% false negative, 9.67% false positive and correct classification is calculated to be 92%. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation.

Classification of macular and optic nerve disease by principal component analysis.

In this study, pattern electroretinography (PERG) signals were obtained by electrophysiological testing devices from 70 subjects. The group consisted of optic nerve and macular diseases subjects. Characterization and interpretation of the physiological PERG signal was done by principal component analysis (PCA). While the first principal component of data matrix acquired from optic nerve patients represents 67.24% of total variance, the first principal component of the macular patients data matrix represents 76.81% of total variance. The basic differences between the two patient groups were obtained with first principal component, obviously. In addition, the graphic of second principal component vs. first principal component of optic nerve and macular subjects was analyzed. The two patient groups were separated clearly from each other without any hesitation. This research developed an auxiliary system for the interpretation of the PERG signals. The stated results show that the use of PCA of physiological waveforms is presented as a powerful method likely to be incorporated in future medical signal processing.

Utilization of artificial neural networks in the diagnosis of optic nerve diseases.

This research is concentrated on the diagnosis of optic nerve disease through the analysis of pattern electroretinography (PERG) signals with the help of artificial neural network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented. The designed classification structure has about 96.4% sensitivity, 90.4% specifity and positive prediction is calculated to be 94.2%. The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation.

Utilization of artificial neural networks and autoregressive modeling in diagnosing mitral valve stenosis.

This research is concentrated on the diagnosis of mitral heart valve stenosis through the analysis of Doppler Signals' AR power spectral density graphic with the help of ANN. Multilayer feedforward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented in the MATLAB environment. Correct classification of 94% was achieved, whereas 4 false classifications have been observed for the test group of 68 subjects in total. The designed classification structure has about 97.3% sensitivity, 90.3% specifity and positive prediction is calculated to be 92.3%. The stated results show that the proposed method can make an effective interpretation.

Automatic detection of age-related macular degeneration pathologies in retinal fundus images.

Advanced techniques in image processing and analysis are being extensively studied to assist clinical diagnoses. Digital colour retinal fundus images are widely utilised to investigate various eye diseases. In this paper, we describe the detection of optic disc (OD), macula and age-related macular degeneration (ARMD) pathologies of the macular regions in colour fundus images. ARMD causes the loss of central vision in older adults. If the disease is detected early and treated promptly, much of the vision loss can be prevented. Eighty colour retinal fundus images were tested using our proposed algorithm. The Hough transform was employed for OD determination. A fundus coordinate system was established based on the macula location. An ARMD pathology detection methodology using a subtraction process after contrast-limited adaptive histogram equalisation operations was proposed. The accuracies of the automated segmentations of the OD, macula and ARMD pathologies obtained were 100%, 100% and 95.49%, respectively. These results show that our algorithm is a useful tool for detecting ARMD in retinal fundus images. The application of our method may reduce the time needed by ophthalmologists to diagnose ARMD pathology while providing dependable detection precision. Integration of our technique into traditional software could be used in clinical implementations as an aid in disease diagnosis and as a tool for quantitative evaluation of treatment effectiveness.

Neural network-based diagnosing for optic nerve disease from visual-evoked potential.

In this paper, we purpose a diagnostic procedure to identify the optic nerve disease from visual evoked potential (VEP) signals using an Artificial Neural Network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented. The correct classification rate was 96.87% for subjects having optic nerve disease and 96.66% for healthy subjects. The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis, angiography, VEP and pattern electroretinography. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.