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BACKGROUND: Color fundus photography have been extensively used to explore the link between retinal morphology changes associated with various disease i.e. Diabetic Retinopathy, Glaucoma. The development of multimodal imaging system that integrates Infrared Scanning Laser Ophthalmoscope (IR-SLO) and Optical Coherence Tomography (OCT) could help in studying these diseases at an early stage. The aim of this study was to test the agreement between the retinal vasculature parameters from the Infrared images obtained from optical coherence tomography and color fundus imaging. METHODS: The IR and Color retinal images were obtained from 16 volunteer participants and seven retinal vessel parameters, i.e. Fractal Dimension (FD), Average Angle (ABA), Total Angle Count (TAC), Tortuosity (ST), Vessel/Background ratio (VBR), Central Retinal Arteriolar Equivalent (CRAE) and Central Retinal Venular Equivalent (CRVE) were extracted from these retinal images using Retinal Image Vasculature Assessment software (RIVAS) and Integrative Vessel Analysis (IVAN). RESULTS: The Bland Altman plot was used to investigate the agreement between the two modalities. The paired sample t-test was used to assess the presence of fixed bias and the slope of Least Square Regression (LSR) line for the presence of proportional bias. The paired sample t-test showed that there was no statistically significant difference between Color and IR based on retinal vessel features (all p values > 0.05). LSR also revealed no statistically significant difference in the retinal vessel features between Color and IR. CONCLUSION: This study has revealed that there is a fair agreement between Color and IR images based on retinal vessel features. This research has shown that it is possible to use IR images of the retina to measure the retinal vasculature parameters which has the advantage of being flash-less, can be used even if there is opacity due to cataract, and can be performed along with OCT on the same device.

© 2019 Graphene possesses excellent mechanical strength and chemical inertness with high intrinsic carrier mobility and superior flexibility making them exceptional candidates for optoelectronic applications. Graphene quantum dots (GQDs) derived from graphene domains have been widely explored to study their photoluminescence properties which can be tuned by size. GQDs are biocompatible, low cytotoxic, strongly luminescent and disperse well in polar and non-polar solvents showing bright promise for the integration into devices for bioimaging, light emitting and photovoltaic applications. In the present study, graphene quantum dots were synthesized by an electrochemical cyclic voltammetry technique using reduced graphene oxide (rGO). GQDs have been incorporated into binder free TiO 2 paste and studied as a photoelectrode material fabricated on ITO/PEN substrates for flexible dye sensitised solar cells (DSSCs). DSSC based on GQDs-TiO 2 exhibited open circuit output potential difference (V oc ) of 0.73 V, and short circuit current density (J sc ) of 11.54 mA cm −2 with an increment in power conversion efficiency by 5.48%, when compared with those with DSSC build with just a TiO 2 photoanode (open-circuit output potential difference (V oc ) of 0.68 V and short circuit density (J sc ) of 10.67 mA cm −2 ). The results have been understood in terms of increased charge extraction and reduced recombination losses upon GQDs incorporation.

Purpose: to describe the methodology and present the retinal grading findings of an older sample of australians with well-defined indices of neurocognitive function in the Personality and total Health (PatH) through life project. Design: a cross-sectional study. Methods: three hundred twenty-six individuals from the PatH through life project were invited to participate. Participants completed a general questionnaire and 2-field, 45-degree nonmydriatic color digital retinal photography. Photographs were graded for retinal pathology according to established protocols. Results: two hundred fifty-four (77.9%) subjects, aged 72 to 78 years, agreed to participate in the eye substudy. gradable images of at least 1 eye were acquired in 211 of 254 subjects (83.1%). retinal photographic screening identified 1 or more signs of pathology in 130 of the 174 subjects (74.7%) with gradable images of both eyes. a total of 45 participants (17.7%) had self-reported diabetes and diabetic retinopathy was observed in 22 (48.9%) of these participants. Conclusions: this well-defined sample of older australians provides a unique opportunity to interrogate associations between retinal findings, including retinal vascular geometric parameters, and indices of neurocognitive function.

Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD). The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.

BACKGROUND: Diabetes mellitus is rapidly increasing in the Indian population. The purpose of this study was to identify changes in the retinal vasculature of diabetic people, ahead of visual impairments. Grayscale Fractal Dimension (FD) analysis of retinal images was performed on people with type 2 diabetes from an Indian population. METHODS: A cross-sectional study comprising 189 Optic Disc (OD) centred retinal images of healthy and diabetic individuals aged 14 to 73 years was conducted. Grayscale Box Counting FD of these retinal photographs was measured without manual supervision. Statistical analysis was conducted to determine the difference in the FD between diabetic and healthy (non-diabetic) people. RESULTS: The results show that grayscale FD values for diabetic cases are higher compared to controls, irrespective of the gender. It was also observed that FD was higher for male compared with females. CONCLUSIONS: There is difference in the grayscale fractal dimension of retinal vasculature of diabetic patients and healthy subjects, even when there is no reported retinopathy.

Surface electromyogram (sEMG) is a technique in which electrodes are placed on the skin overlying a muscle to detect the electrical activity. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one-to-one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity-independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent component analysis (ICA) provides the perfect answer of separating skin surface recordings into a set of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor placements in sEMG. The purpose of this paper is to describe the use of source separation for sEMG using ICA. The actual use in practical sEMG experiments is demonstrated, when the number of recording channels for electrical muscle activities is varied. © 2012 Wiley Publishing Ltd.

The need for developing reliable and flexible human computer interface is increased and applications of HCI have been in each and every field. Human factors play an important role in these kinds of interfaces. Research and development of new human computer interaction (HCI) techniques that enhance the flexibility and reliability for the user are important. Research on new methods of computer control has focused on three types of body functions: speech, bioelectrical activity, and use of mechanical sensors. Speech operated systems have the advantage that these provide the user with flexibility. Such systems have the potential for making computer control effortless and natural. This chapter summarizes research conducted to investigate the use of facial muscle activity for a reliable interface to identify voiceless speech based commands without any audio signals. System performance and reliability have been tested to study inter-subject and inter-day variations and impact of the native language of the speaker. The experimental results indicate that such a system has high degree of inter-subject and inter-day variations. The results also indicate that the variations of the style of speaking in the native language are low but are high when the speaker speaks in a foreign language. The results also indicate that such a system is suitable for a very small vocabulary. The authors suggest that facial sEMG based speech recognition systems may only find limited applications.

Surface electromyogram (sEMG) based control of prosthesis and computer assisted devices can provide the user with near natural control. Unfortunately there is no suitable technique to classify sEMG when the there are multiple active muscles such as during finger and wrist flexion due to cross-talk. Independent Component Analysis (ICA) to decompose the signal into individual muscle activity has been demonstrated to be useful. However, ICA is an iterative technique that has inherent randomness during initialization. The average improvement in classification of sEMG that was separated using ICA was very small, from 60% to 65%. To overcome this problem associated with randomness of initialization, multi-run ICA (MICA) based sEMG classification system has been proposed and tested. MICA overcame the shortcoming and the results indicate that using MICA, the accuracy of identifying the finger and wrist actions using sEMG was 99%. © Springer Science+Business Media, LLC 2010.

Changes in surface electromyogram (sEMG) spectral content are commonly associated with localized muscle fatigue. However, the significance of the changes is only evident during pair-wise comparison and these can only be used for comparison between the rested and fatigued muscle and cannot be used for identifying the limit of muscle endurance without having the rested data for comparison. This is due to the large variations between sEMG at different levels of strengths of contraction, and between different people. This is further compounded when the contraction is not isometric but is cyclic because there is large variation of sEMG within each cycle. This research has developed a new sEMG based method for studying muscle fatigue and for identifying the limit of muscle endurance. It is based on motor unit synchronization and is called increase in synchronization (IIS) index. IIS index measures the level of independence between two channels of sEMG recorded from the muscle and is the log of the determinant of the global matrix (log||G||) which is generated by performing independent component analysis on the two channels. The experimental results for biceps brachii demonstrate that when the muscle was rested, the two channels had a high degree of independence and the IIS index was greater than -0.7 (range -0.65 to -0.05). However, the channels became dependent as the muscles progressively fatigued and IIS index became less than -6.2 (range -7.8 to -6.3 ) at the limit of muscle endurance. This was irrespective of the contraction being isometric or cyclic, or of the level of muscle contraction. © 2011 IEEE.

This research deals with computing the non-Gaussianity in Surface Electromyogram signal (sEMG) using Negative entropy and Kurtosis values. The signal was acquired from three different finger and wrist actions at four different levels of Maximum Voluntary Contractions (MVCs). Our experimental analysis shows that at constant force and for non-fatiguing contractions, probability density function (pdf) of sEMG signals were non-Gaussian. For lesser MVCs (below 30% of MVC) pdf measures tends to be Gaussian process. The above measures were verified by computing the kurtosis values for different MVCs. © 2011 IEEE.

Surface electromyogram (sEMG) is a non-invasive recording and it has numerous applications. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one-to-one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity-independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent Component Analysis (ICA) provides the perfect answer of un-mixing a set of skin surface recordings into a vector (set) of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor set in sEMG recording. The purpose of this paper is to describe the use of source separation for sEMG based on ICA. We demonstrate how this can be used in practical sEMG experiments, when the number of recording channels for electrical muscle activities is varied. Keywords: Hand gesture sensing, Bio-signal analysis, Independent component analysis (ICA), Surface electromyography (sEMG), Blind source separation (BSS). © 2011 ICIC International.

The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable in pattern classification. This research reports a new technique of sEMG using Independent Component Analysis (ICA). The technique uses blind source separation (BSS) methods to classify the patterns of Myo-electrical signals during different Maximum Voluntary Contraction (MVCs) at different low level finger movements. The results of the experiments indicate that patterns using ICA of sEMG is a reliable (p<0.001) measure of strength of muscle contraction even when muscle activity is only 20% MVC. The authors propose that ICA is a useful indicator of muscle properties and is a useful indicator of the level of muscle activity.

Classification of surface electromyogram (sEMG) signal is important for various applications such as prosthetic control and human computer interface. Surface EMG provides a better insight into the strength of muscle contraction which can be used as control signal for different applications. Due to the various interference between different muscle activities, it is difficult to identify movements using sEMG during low-level flexions. A new set of fractal features - fractal dimension and Maximum fractal length of sEMG has been previously reported by the authors.These features measure the complexity and strength of the muscle contraction during the low-level finger flexions. In order to classify and identify the low-level finger flexions using these features based on the fractal properties, a recently developed machine learning based classifier, Twin Support vector machines (TSVM) has been proposed. TSVM works on basic learning methodology and solves the classification tasks as two SVMs for each classes. This paper reports the novel method on the machine learning based classification of fractal features of sEMG using the Twin Support vector machines. The training and testing was performed using two different kernel functions - Linear and Radial Basis Function (RBF). © 2010 IEEE.

There is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer-assisted devices. Surface electromyogram (SEMG) is a non-invasive measure of the muscle activities but is not reliable because there are a multiple simultaneously active muscles. This study proposes the use of independent component analysis (ICA) for SEMG to separate activity from different muscles. A mitigation strategy to overcome shortcomings related to order and magnitude ambiguity related to ICA has been developed. This is achieved by using a combination of unmixing matrix obtained from FastICA analysis and weight matrix derived from training of the supervised neural network corresponding to the specific user. This is referred to as ICANN (independent component analysis neural network combination). Experiments were conducted and the results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user. © 2010 The Institution of Engineering and Technology.

Myoelectric signal classification is one of the most difficult pattern recognition problems because large variations in surface electromyogram features usually exist. In the literature, attempts have been made to apply various pattern recognition methods to classify surface electromyography into components corresponding to the activities of different muscles, but this has not been very successful, as some muscles are bigger and more active than others. This results in dataset discrepancy during classification. Multicategory classification problems are usually solved by solving many, one-versus-rest binary classification tasks. These subtasks unsurprisingly involve unbalanced datasets. Consequently, we need a learning methodology that can take into account unbalanced datasets in addition to large variations in the distributions of patterns corresponding to different classes. Here, we attempt to address the above issues using hybrid features extracted from independent component analysis and twin support vector machine techniques. © 2010 by Walter de Gruyter · Berlin · New York.

This research paper reports an experimental study on identification of the changes in fractal properties of surface Electromyogram (sEMG) with the changes in the force levels during low-level finger flexions. In the previous study, the authors have identified a novel fractal feature, Maximum fractal length (MFL) as a measure of strength of low-level contractions and has used this feature to identify various wrist and finger movements. This study has tested the relationship between the MFL and force of contraction. The results suggest that changes in MFL is correlated with the changes in contraction levels (20%, 50% and 80% maximum voluntary contraction (MVC)) during low-level muscle activation such as finger flexions. From the statistical analysis and by visualisation using box-plot, it is observed that MFL (p ≈ 0.001) is a more correlated to force of contraction compared to RMS (p≈0.05), even when the muscle contraction is less than 50% MVC during low-level finger flexions. This work has established that this fractal feature will be useful in providing information about changes in levels of force during low-level finger movements for prosthetic control or human computer interface. © 2010 IEEE.

Surface electromyogram (sEMG) is a measure of the muscle activity from the skin surface, and is an excellent indicator of the strength of muscle contraction. It is an obvious choice for control of prostheses and identification of body gestures. Using sEMG to identify posture and actions that are a result of overlapping multiple active muscles is rendered difficult by interference between different muscle activities. In the literature, attempts have been made to apply independent component analysis to separate sEMG into components corresponding to the activities of different muscles, but this has not been very successful, because some muscles are larger and more active than the others. We address the problem of how to learn to separate each gesture or activity from all others. Multicategory classification problems are usually solved by solving many one-versus-rest binary classification tasks. These subtasks naturally involve unbalanced datasets. Therefore, we require a learning methodology that can take into account unbalanced datasets, as well as large variations in the distributions of patterns corresponding to different classes. This paper reports the use of twin support vector machine for gesture classification based on sEMG, and shows that this technique is eminently suited to such applications. © 2009 IEEE.

This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA. ©2009 IEEE.

Previous research studies have reported that spectral compression of the surface Electromyogram (SEMG) towards lower frequencies is associated with onset of localized muscle fatigue. One reason for this spectral compression has been attributed to motor unit synchronization in literature. According to this, motor units are pseudo randomly excited during muscle contraction, and the recruitment pattern changes during the onset of muscle fatigue, such that the firing of motor units becomes more synchronized. While this theory is widely accepted, there is little experimental proof of the phenomenon. This paper has used source dependence properties and measures developed in research related to independent component analysis (ICA) to test for synchronization. This paper has also determined that the global matrix can be used as a measure for estimating localized muscle fatigue during cyclic movements. © 2009 IEEE.

Spectral compression of surface electromyogram (sEMG) is associated with onset of localized muscle fatigue. The spectral compression has been explained based on motor unit synchronization theory. According to this theory, motor units are pseudo randomly excited during muscle contraction, and with the onset of muscle fatigue the recruitment pattern changes such that motor unit firings become more synchronized. While this is widely accepted, there is little experimental proof of this phenomenon. This paper has used source dependence measures developed in research related to independent component analysis (ICA) to test this theory. ©2009 IEEE.