Neuropathu preparation and revision, HS, GY, WE, and GT. Anti-seizure drugs. You can also visit a podiatrist up to 5 visits a year are subsidised for people living with diabetes.

Diabetic neuropathy in the face -

Having found some discriminatory features, we will cruise through the most important algorithms based on optimal ensemble learning for classification purposes.

In " Results " section, we apply these concepts to the time series associated with the spatio-temporal tensor expression profile for color variations obtained by EVM among both diabetic and control subjects.

Conclusions, discussions and future works are provided at the end of this paper. The study enrolled 27 subjects, divided into two groups.

Out of the 18 DM patients, 17 had been diagnosed with peripheral neuropathy, 17 with retinopathy, and 7 with diabetic nephropathy, and 5 of them had been diagnosed with peripheral arterial disease. The diagnosis of neuropathy was carried out using Semmes—Weinstein monofilament 10 g and biothesiometry indicating neuropathy with a threshold of 25 V.

Facial erythema was not apparent in any of the participants, except for one DM patient with slight facial red coloration. None of the DM patients had exhibited cranial neuropathies such as facial nerve palsy, optic neuropathy, or auditory neuropathy.

Furthermore, no individual was diagnosed with obstructive sleep apnoea OSA. More information on DM patients can be found in Table 1.

Control subjects had not been diagnosed with DM, peripheral arterial disease, or any disease affecting the nervous system. All participants were informed about the study, and signed, written informed consents to publish the results of this study were obtained.

Furthermore, informed consent for participation was obtained from the participants in the manuscript. All methods and experiments were carried out in accordance with relevant guidelines and regulations based on the Declaration of Helsinki. The experimental set-up included a laptop and a Canon EOS D digital camera mounted on a tripod.

The most important aspects to be considered in pursuance of the acquisition of an input video of adequate quality for subsequent processing were as follows: 1 stable camera, 2 constant background, 3 minimal reflections and shadows, 4 non-moving objects within the frequency range of interest, 5 choice of a digital camera, 6 sufficient temporal resolution of the video frame rate , 7 sufficient spatial resolution of the video and color depth, 8 short distance to the object and optics, 9 proper angle of the view point and 10 sufficient duration of the video The camera was placed above the laptop, about 84 cm in front of the subject being examined, so that the entire face was captured.

The subject was then asked to follow with their eyes a target on the screen, with the shape of a white circle moving on a black background either in a sudden form to force saccadic movements , or in a smooth fashion to force pursuit motions , while keeping the head as still as possible.

A series of movements occured randomly over a period of 7 min and 45 s including two sets of 30 s breaks. Before each command, a sound cue was emitted, to facilitate the synchronization between the video and the commands.

Two repetitions were recorded on each participant, with a resting time between repetitions of about 10 min. These videos were then fed into EVM for magnification of subtle changes invisible to the naked eyes.

It is worth noting that two factors that could affect facial erythema during video acquisition and potentially introduce errors were compensated for: 1 individual-specific factors that mostly affect facial color in a homogeneous manner, equally affecting all regions of the face e.

individual-specific score in the Fitzpatrick scale, sun exposure if bilateral and 2 circumstantial factors that cause color differences among different facial regions e. physical activity, emotional state, and certain skin conditions 18 , 19 , We addressed the first potential source of error by using the difference of redness between two different face regions to begin with.

We minimized heterogeneous color variations caused by the second potential source of error by using the same protocol for all participants, for example, by ensuring that none of participants engaged in any physical activity during the previous twenty minutes prior to the beginning of experimental recordings.

Schematic of our proposed method is shown in Fig. The future predictive performance of the method was estimated using Leave-one-out Cross-Validation LOO-XV technique. Revealing important subtle changes that fall outside human visual limits to be seen with the naked eye, and displaying them in an indicative manner using Eulerian video magnification technique was first introduced by EVM takes a video as input, and applies a sequence of spatial decomposition and temporal filtering to the frames.

This technique can show events of interest that occur at specific temporal frequencies, such as the frequency band corresponding to the human pulse. The main advantages of EVM compared to the Lagrangian video magnification or other similar techniques are 1 its capability to cope with dynamic environments, 2 its robustness under different noisy condition and 3 its invariant performance under different skin-tones.

Assume image intensity function at pixel x , y at time t be determined by I x , y , t. To increase temporal signal-to-noise ratio and for the sake of computational efficiency, pooling multiple pixels while simultaneously processing spatially using low-pass filters were applied to the frames of the video.

In this study, however, we benefited from full Laplacian pyramid spatial filtering while for the temporal processing, subtraction of two Butterworth low-pass filters with cutoff frequencies of 4.

The temporal processing was uniform for all spatial levels and for all pixels within each level. The filtered spatial bands were then added back to the original signal and collapsed to generate the output video with magnified color. The location of the patches is detected all through the video, using an algorithm for the detection of facial landmarks.

The Marcenko—Pastur theorem 22 on the spectrum of empirical correlation matrices have been implemented in many, very different contexts including neural networks, image processing and population health studies. These quantities might resemble the motion of individual grains in a packed granular medium linked to systems featuring self-organized-criticality, or different biological indicators such as blood pressure or cholesterol level within a population Inspired by these studies, we attempted in this work to draw statements about the statistics of the eigenvalues of the spatio-temporal tensor expression profiles for the color variations using RMT.

We looked at the statistics of the bulk of eigenvalues and deployed the covariance matrix model to make statements about the empirical spectral density ESD function of the largest eigenvalues of our tensors.

where q. is the solution to the nonlinear differential equation associated with the Airy function. In this research, we study the statistics of the largest eigenvalues of the normalized spatio-temporal tensor expression profiles associated with each video segment 6 per subject for both group DM and C to explore whether they are asymptotically governed by the Tracy—Widom distribution.

We also pay special attention to the appearance of sharp edges and particularly to the tails of the spectrum and their decay rate. k can be positive, zero, or negative and was developed to model tails of a wide variety of distributions.

Distributions whose tails fall off as a polynomial i. Distributions whose tails decrease exponentially, such as the normal, correspond to a zero k , while distributions with finite tails, such as Gamma, correspond to a negative shape parameter k.

We estimate the right tail index parameters k of ESD function of the largest eigenvalues derived from the normalized spatio-termporal tensor expression profiles of video segments for both groups DM and C by fitting GP distribution.

Critical systems are dynamical systems with several interacting components that exhibit scale-invariant fluctuations Experiments have suggested that the healthy brain, capable of self-tuning to the critical state, also known as SOC, functions near phase transitions because criticality improves both information processing capabilities and health Experiments have shown that when the brain malfunctions, e.

Combining several base predictive learners using an ensemble of models aims at providing better predictions due to capturing the underlying distribution of the data in a more precise manner Different ensemble-based techniques range from bagging to boosting and stacking have been used in different research disciplines including health While the first two techniques, namely bagging and boosting primarily focus on reducing either variance or bias, stacking approaches attempt at finding the optimal approach to accumulate base learners so that the best trade-off between bias and variance is obtained.

Stacking technique searches for optimal weights using cross-validation, also known as Cross-validated Optimal Weighted Ensemble COWE.

COWE as presented in Table 2 intends to find the best way to combine predictions of base learners such as decision trees, linear discriminant analysis LDA , Naive Bayes, support vector machines SVM , K-nearest neighbors KNN and neural networks among others, by searching for the optimal weight to combine them so that the outcome ensemble minimizes the total expected prediction error MSE.

The optimization model of COWE assumes that the hyperparameters of each base learner are tuned prior to conducting the weighting task. This means that the hyperparameters are tuned in an optimal fashion as an independent process. The optimization process relies on three distinct algorithms, namely Bayesian optimization, random search or grid search.

While the former aims at approximating the unknown function with surrogate models like Gaussian process, the two latter solutions are exhaustive search methods. Bayesian optimization tries to gather observations with the highest information in each iteration by making a trade-off between exploration and exploitation.

The architecture of our classifier is presented in Fig. A wide range of learners, their corresponding hyperparameters, and optimizers to search for them, along with the optimal ensemble weights were investigated.

We deployed Leave-one-out Cross-Validation LOO-XV to predict how well the developed method for feature extraction and classification will generalize on an independent data set. LOO-XV removes each observation in turn, constructs the classifier, and then computes whether this leave-one-out classifier correctly classifies the deleted observation.

This was iterated 27 times fold cross validation at subject level with a different observation subject reserved for testing purpose each time. The final assignment of a subject belonging to either class was based on majority voting with equal weights, i.

The performance of the classifier was finally calculated from the 27 testing observations, by using pre-determined performance metrics such as accuracy, sensitivity and specificity. Subtle color changes invisible to the naked eye top row is evident after magnification frames 0 and 39, vs.

frames 20 and 58 , where the time between heart beats is about 0. The intensity map on the left belongs to a subject from group DM, while the equivalent on the right belongs to a subject chosen from group C.

The intensity maps show that the mean of intensity difference among subjects of group DM is approximately 22 times larger than that of group C. Illustration of temporal facial color variations before top row and after applying Eulerian Video Magnification bottom row.

The left patch belongs to a subject from group DM and the right patch is generated from a subject of group C. Furthermore, statistics of the ESD function of the largest eigenvalues of normalized spatio-temporal tensor expression profiles for color variations among subjects of group DM and C are presented in Tables 3 and 4.

This observation holds for majority of subjects 7 out of 9 in C vs. Example of empirical spectral density function of the largest eigenvalues of spatio-temporal tensor expression profile of color variations left: subject from group C in log-log scale; right: subject from group DM.

Fitted Gamma probability and cumulative density functions to the tails of the empirical spectral density function of the largest eigenvalue of subjects of group DM.

Out of all the potential ensembles of learners listed in Fig. Observed minimum classification error of the best performing ensemble of learners and its corresponding scatter plot of model predictions are shown in Figs.

Furthermore, an example of a decision tree in which the ensemble is based upon is presented in Fig. To obtain optimal prediction model while aggregating predictive learners, all trees were given equal weights, being the most straightforward approach by simply averaging over the pre-tuned base models.

The best point hyperparameters were obtained out of 52 learners featuring an optimal learning rate of 0. Given that the number of subjects in class DM was twice as that of class C, and in order to cope with class imbalance and skewed nature of the data, RUSBoost with maximum split size of 39 and split criterion of maximum deviance reduction while finding all the surrogate decision splits were applied.

To overcome overfitting, fivefold cross-validation was applied. Bayesian optimization was preferred over other techniques, due to gathering observations with highest information while simultaneously incorporating prior beliefs.

The results of final phase of classification process, i. The results show that by integrating classification results from 3 video segments of group DM, only one subject was misclassified sensitivity of Similar results were observed among subjects of group C, in which integration of classification outcomes from 5 video segments resulted in the misclassification of only one subject.

Integration of results from 6 video segments shifted the label of the misclassified subject into undecided, due to an equal split in the voting outcome.

Classification results based on the majority voting of video segments, each column representing the number of aggregated video segments.

A useful demarcation line that makes the distinction between our proposed POCD and existing solutions crisp and easy to apply, can be formulated as follows.

Our POCD is an inexpensive solution that only requires a digital camera and a screen. Given recent advances and surge of interest in smartphones and tablets that are equipped with complex and powerful processors and high resolution peripherals such as illumination systems LEDs and cameras, converting our proposed POCD to a home-based solution is feasible.

The analysis of collected videos and potential outcomes of such investigations can be performed on a centralized cloud-based server maintained by healthcare professionals. This enables both patient and care givers to automatically update the status of the patient and plan future healthcare actions accordingly.

We believe that the same principle could be applied to detect anemia, jaundice and infection and dehydration, using color magnification of lower eyelid mucosa, face and sclera and by observing increased heart rate, respectively. Even though no individual in our study group was diagnosed with obstructive sleep apnoea OSA ; which might result in exhibiting higher levels of haemoglobin, it is our belief that our algorithm would not suffer from this condition, as it relies on the time series associated with the difference between highest and lowest values of color intensity.

This results in our technique being independent from the absolute values of haemoglobin, shown to be high in subjects with OSA and low in subjects with anaemia, as the computations are based on relative values of haemoglobin level. Even though Eulerian video magnification algorithm does not provide superior performance in terms of computational speed compared to the proposed method in 25 , it has been proven to be a very robust algorithm for revealing subtle changes in both color and motion deformations that fall outside human visual limits.

Limitations with regards to video quality metrics, including noise level, video quality and long execution time that are associated with the existing video magnification techniques such as Eulerian video magnification, phase-based video magnification, Riesz pyramid for fast phase-based video magnification and enhanced Eulerian video magnification need to be taken into consideration.

The conclusions drawn on the statistics of the ESD function of the largest eigenvalues of normalized spatio-temporal color tensor expression profile, and their tails, were build upon modeling this process as a covariance matrix model in RMT.

Even though we observed that in a majority of cases in group C, the tails of these ESD functions decay with a power law exponent mimicking systems exhibiting SOC, while those of group DM follow Tracy—Widom statistics, a natural question of great importance is whether the Information-plus-noise model would be a better choice, which could lead to a concise universal results on the statistics of the tails and their decay rate.

It should be also noted that sharp edges in the bulk of these ESD functions were not observed. Another important aspect is the study of subspace stability spanned by the eigenvectors associated with the largest eigenvalues.

Following the subspace spanned by these eigenvectors, one expects that the top eigenvector wobble around the true direction either due to the measurement noise or due to the presence of a systematic rotation caused by a hidden mechanism. Having said that, our hypothesis is that there should be a genuine motion of the largest eigenvector in time towards the uniform vector among subjects belonging to group C and away from the uniform vector over time among subjects of group DM.

Studying the statistics of the largest eigenvectors and the stability of their corresponding spanned subspace is part of our future work. Our goal is not only to distinguish patients diagnosed with DN-related complications from a control group, but to map the journey, find transition points from mild to severe complications throughout this journey and to stage the disease, by studying different aspects of these eigenvectors.

The apparent shortcoming of this study is the small sample size and ambiguities around calculation of the power of the study. However, it is worth noting that: 1 in order to make a priori power analysis, we would need to have an idea of what the size of the effect will be, and the variance of the variable of interest.

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On this page What is diabetic neuropathy? What are the symptoms of diabetic neuropathy? What causes diabetic neuropathy?

When should I see my doctor? How is diabetic neuropathy diagnosed? How is diabetic neuropathy treated? Can diabetic neuropathy be prevented? Complications of diabetic neuropathy Resources and support Related information on Australian websites What is diabetic neuropathy?

It can also affect other nerves in your body known as the autonomic nerves and motor nerves. Autonomic nerves carry signals to help with balance, sweating, digestion and many of the things you do without thinking. Motor nerves carry signals to help you move.

Diabetic neuropathy can also cause: pain and discomfort in your arms or legs, especially at night not being able to feel sores or cuts sleep problems bloating and indigestion heat intolerance problems with walking diarrhoea problems with urinating passing water low blood pressure on standing problems with sexual function not being able to recognise when your blood sugar is low hypoglycaemia Talk to your doctor if you think you might have diabetes or diabetic neuropathy, or call the National Diabetes Services Scheme NDSS Helpline on The longer you have had diabetes, the more likely you are to develop diabetic neuropathy.

Other conditions can play a part, including: high blood pressure vitamin B deficiency alcohol abuse smoking kidney disease or liver disease some medicines , including some drugs used against cancer When should I see my doctor? Your doctor will talk to you, examine you, and may recommend that you have some blood tests.

Discuss with your doctor or diabetes nurse how to protect your skin and deal with any pain. Pain relievers, such as paracetamol and ibuprofen, might not work with the pain of diabetic neuropathy.

If so, talk to your doctor about other forms of pain relief. When you have diabetes it is important to take care of your feet. Appropriate footwear is important.

You can also visit a podiatrist up to 5 visits a year are subsidised for people living with diabetes. For more insights on neurology, check out our weekly tips on our Neurology Office Facebook page.

Continue reading. Blog Home » Neurology Insights ». Neurology Insights. February 2, Posted by Joseph Kandel, M. I want to address a very common problem that patients with Diabetes face; Diabetic Neuropathy.

There are four types of Diabetic Neuropathy, and the symptoms that a patient will experience depends on which type of nerve becomes damaged. Diabetic Peripheral Neuropathy: this refers to the small nerve twigs of the body, usually affecting the legs and the feet. Symptoms include pain, numbness, burning, and tingling in the lower extremities.

Diabetic Proximal Neuropathy: this refers to damage of the nerves of the hips, thighs, or buttocks. Diabetic Autonomic Neuropathy: this affects the nerves of the autonomic often thought of as the automatic nervous system. This includes nerves of the gastrointestinal tract stomach and bowel , urinary tract kidney and bladder , the genital system penis or vagina , and vascular system heart and blood vessels.

These problems can include the inability to control the bladder, stomach bloating and constipation, erectile dysfunction in men or vaginal dryness in women, changes in sweating, loss of the ability to regulate blood pressure and pulse especially on standing after sitting for a while , poor body temperature regulation, and many more.

Diabetic Focal Neuropathy: this involves anyone nerve anywhere in the body. Depending on where the affected nerve is, this could include problems with vision and focus, pain in the low back, shin, foot, pelvis, chest or abdomen.

If the nerve gets compressed, it can lead to painful symptoms. One very common type of problem in diabetics is Carpal Tunnel Syndrome pinching of the nerve at the wrist. How Does this occur? How do I go about getting this checked out? What will a Neurologist do to diagnose your problem? What are the neurologic tests to diagnose diabetic neurologic problems?

OK, assuming I have neuropathy. How do I treat it? Pain Relief treatments:. Alternative treatments: Topical creams, acupuncture, massage may help.

Book Your Appointment Today! Neurology Office, Joseph Kandel M. Tags: Chronic Pain , Neurologic Disorders. Newer What is Neurology, and What Does a Neurologist Do? Back to list. Older Arthritis: What is it, and what can I do for it? February 2, Posted by brad idprojection.

Our five senses are how we connect and communicate with the world around us. If you have been blesse If you know someone living with chronic pain, you may be familiar with the toll it can take on one's Happy Summer from Neurology Office! As the temperature rises, we encourage all our patients to do Did you know?

Diabetic neuropathy in the face I Diabetc the opportunity to evaluate a year old woman who complained of right side facial pain that by its description beuropathy had a Nutritional balance origin. Her Diabetic neuropathy in the face was Fat-burning foods and was most intense during the faxe few bites of a meal. In addition, as she brought food to her lips, which initiates salivationher pain greatly intensified. The pain was described as bright, sharp, and debilitating during eating and lingered even after the meal was over. Prior to her consultation in my office she had seen a number of ENT doctors whose evaluation did not lead to a diagnosis or an effective course of treatment. All dental exams and X-rays were also negative. Nehropathy neuropathy Nfuropathy a gace of nerve damage that can occur if you have diabetes. High blood sugar Nutritional needs during growth spurts can injure nerves Isotonic drink flavors the body. Diabetic neuropathy most often damages nerves in the legs and feet. Depending on the affected nerves, diabetic neuropathy symptoms include pain and numbness in the legs, feet and hands. It can also cause problems with the digestive system, urinary tract, blood vessels and heart. Some people have mild symptoms.