Biophotonics methods for functional monitoring of complications of diabetes mellitus

The prevalence of diabetes complications is a significant public health problem with a considerable economic cost. Thus, the timely diagnosis of complications and prevention of their development will contribute to increasing the length and quality of patient life, and reducing the economic costs of their treatment. This article aims to review the current state-of-the-art biophotonics technologies used to identify the complications of diabetes mellitus and assess the quality of their treatment. Additionally, these technologies assess the structural and functional properties of biological tissues, and they include capillaroscopy, laser Doppler flowmetry and hyperspectral imaging, laser speckle contrast imaging, diffuse reflectance spectroscopy and imaging, fluorescence spectroscopy and imaging, optical coherence tomography, optoacoustic imaging and confocal microscopy. Recent advances in the field of optical noninvasive diagnosis suggest a wider introduction of biophotonics technologies into clinical practice and, in particular, in diabetes care units

DEVELOPMENT OF A REAL-TIME SINGLE CHANNEL BRAIN–COMPUTER INTERFACE SYSTEM FOR DETECTION OF DROWSINESS

Drowsiness or fatigue condition refers to feeling abnormally sleepy at an inappropriate time, especially during day time. It reduces the level of concentration and slowdown the response time, which eventually increases the error rate while doing any day-to-day activity. It can be dangerous for some people who require higher concentration level while doing their work. Study shows that 25–30% of road accidents occur due to drowsy driving. There are number of methods available for the detection of drowsiness out of which most of the methods provide an indirect measurement of drowsiness whereas electroencephalography provides the most reliable and direct measurement of the level of consciousness of the subject. The aim of this paper is to design and develop a portable and low cost brain–computer interface system for detection of drowsiness. In this study, we are using three dry electrodes out of which two active electrodes are placed on the forehead whereas the reference electrode is placed on the earlobe to acquire electroencephalogram (EEG) signal. Previous research shows that, there is a measurable change in the amplitude of theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave between the active state and the drowsy state and based on this fact theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave are separated from the normal EEG signal. The signal processing unit is interfaced with the microcontroller unit which is programmed to analyze the drowsiness based on the change in the amplitude of theta ([Formula: see text]) wave. An alarm will be activated once drowsiness is detected. The experiment was conducted on 20 subjects and EEG data were recorded to develop our drowsiness detection system. Experimental results have proved that our system has achieved real-time drowsiness detection with an accuracy of approximately 85%. </jats:p

RISK ANALYSIS AND CLASSIFICATION OF MYOCARDIAL INFARCTION FROM CAROTID INTIMA MEDIA THICKNESS OF B-MODE ULTRASOUND IMAGE USING VARIOUS MACHINE LEARNING AND DEEP LEARNING TECHNIQUES

Myocardial infarction (MI) is a life threatening condition that causes death in developing nations due to blockage in the blood vessel of the coronary artery that supplies blood to the heart. Any blockage in blood vessel of carotid artery located in the neck region can predict the risk of heart failure. Carotid Intima-Media Thickness (CIMT) measured from carotid artery supports in the estimation of heart failure. In this study, cardiovascular risk is considered based on the CIMT thickness of carotid artery blood vessels. In this paper, CIMT and Framingham risk score (FRS) boundary have been determined for both normal and cardiovascular disease (CVD) subjects, which aids in the prediction of heart failure. For 55 subjects with normal condition and 55 subjects with CVD disease, CIMT values were measured by utilizing an effective ultrasound examination system. Biochemical parameters were also measured for all the 110 subjects to predict the FRS score. Student t-test and Spearmans correlation performed showed significant results with p-value less than 0.01 for CIMT, biochemical parameters and FRS score. Receiver operating characteristic (ROC) has been plotted for measured CIMT value and FRS value indicates with an accuracy of 71%. The performance was also determined by comparing various classification techniques using machine learning and deep learning. Results observed through machine learning show that random forest, multilayer perceptron and K-nearest neighbor classifiers used in classification techniques give more superior accuracy of 79% and sensitivity of 78%. To improvise the investigation, deep learning technique using carotid artery ultrasound image of 1909 dataset has been used. Deep learning CNN architecture using VGG19 implemented gave an accuracy of 98% with sensitivity 98% and specificity 99%. It was also observed from the 20% data used for validation that 199 subjects are without risk of MI and 178 subjects were predicted with a risk of MI in future. Further relative risk analysis performed with FRS and CIMT showed that a person with low risk of FRS has 26% chances of getting abnormal CIMT and MI risk in future. </jats:p

AUTOMATED CLASSIFICATION OF AUTISM SPECTRUM DISORDER USING EEG SIGNALS AND CONVOLUTIONAL NEURAL NETWORKS

Children suffering from Autism Spectrum Disorder (ASD) have impaired social communication, interaction and restricted and repetitive behaviors. ASD is caused by abnormal brain developments which give rise to the behavioral characteristics associated with ASD. The clinical diagnosis of ASD is performed on the basis of behavioral assessment and it causes a time delay in early intervention, as there is a time gap between abnormal brain developments and associated behavioral characteristics. Electroencephalography (EEG) is a technique which measures the electrical activity produced by the brain and it has been used to detect several neurological disorders. Studies have shown that there is a variation in the EEG signals of a normal subject and EEG signals of ASD subjects. In this study, we obtained scalograms of EEG signals by using Continuous Wavelet Transform (CWT). Pre-trained deep Convolutional Neural Networks (CNNs) such as GoogLeNet, AlexNet, MobileNet and SqueezeNet were used for extracting the features from scalograms and classification of obtained scalograms from EEG signals of normal and ASD subjects. We also used Support Vector Machine (SVM) algorithm and Relevance Vector Machine (RVM) for classification of the features extracted by the deep CNNs. The GoogLeNet, AlexNet, MobileNet and SqueezeNet deep CNNs achieved a validation accuracy of 75%, 75.84%, 79.45% and 82.98% in classifying the scalograms generated from EEG signals. The SVM achieved an accuracy of 71.6%, 74.76%, 70.70% and 81.47% using GoogleNet, Mobilenet, AlexNet and SqueezeNet for scalogram feature extraction. The RVM achieved an accuracy of 65.5%, 69.9%, 65.3% and 72.59% when used for classification using the features generated from GoogLeNet, AlexNet, MobileNet and SqueezeNet.The SqueezeNet deep CNN performed better than GoogLeNet, AlexNet and MobileNet for classification of the EEG scalograms. The feature extraction using SqueezeNet also resulted in better classification accuracy obtained by SVM and RVM. The results indicate that pre-trained models can be used for classifying the ASD using scalograms of the EEG signals. </jats:p

Characterization and Comparison of Three Species of the Genus Salacia.

Some of the plant species of the genus Salacia are considered as an important anti-diabetic drug in ayurveda. Among them S. chinensis, S. fruticosa and S. oblonga possess great medicinal importance, mostly because of its promising anti-diabetic activity. The increasing demand of this drug, resulted in a huge decline in its availability and it is replaced by spurious ones mainly because of lack of adequate quality standards. There is now a felt need to develop a systematic approach for the authentication of these plants and to develop well-designed methodologies for its standardization. The present study focused on the Pharmacopoeial parameters like pharmacognostical characterization and preliminary phytochemical screening of these three species. which were found to be sufficient to evaluate the raw material and can also be used as reference standards for the quality control/quality assurance purposes. The pharmacognostic study revealed that there are specific diagnostic features for distinguishing these three species among themselves and also from other related species. The external morphology of these three species are also shows variation in their colour, odour and taste. Both the species of Salacia ie, S. chinensis is having bitter taste and S. fruticose is with astringent taste whereas S. oblonga has no characteristic taste of its own. The TLC profile showed similar pattern in S. chinensis and S. fruiticosa where as S. oblonga showed more band in the profile than that of the other two. This reveals that the S. chinensis and S. fruiticosa having similar compounds responsible for therapeutic efficacy, whereas S. oblonga has more chemical constituents.</jats:p