Detection of Depression Using Weighted Spectral Graph Clustering With EEG Biomarkers

The alarming annual growth in the number of people affected by Major Depressive Disorder (MDD) is a problem on a global scale. In the primary scrutiny of depression, Electroencephalography (EEG) is one of the analytical tools available. Machine Learning (ML) and Deep Neural Networks (DNN) methods are the most common techniques for MDD diagnosis using EEG. However, these ML methods heavily rely on manually annotated EEG signals, which can only be generated by experts, for training. This also necessitates a large amount of memory and time constraints. The requirement of huge amounts of data to foresee emerging tendencies or undiscovered alignments is enforced. This article develops an unsupervised learning method for identifying MDD in light of these difficulties. The preprocessed EEG is used to extract three quantitative biomarkers (Band Power: Beta, Delta, and Theta), and three signal features (Detrended Fluctuation Analysis (DFA), Higuchi’s Fractal Dimension (HFD), and Lempel-Ziv Complexity (LZC)). Through the extracted features, an undirected graph is created using the features as a weight along the edges, with nodes as channels in EEG recording. The bifurcation of the subjects in either of the classes (MDD or N) is done by implementing spectral clustering. A 98% accuracy with a 2.5% of miss-classification error is achieved for the left hemisphere. In contrast, a 97% accuracy with a 3.3% CEP (or miss-classification error or Classification Error Percentage) is achieved for the right hemisphere. FP1 and F8 channels have achieved the highest possible level of classification accuracy.publishedVersio

Designing of a Risk Assessment Model for Issuing Credit Card Using Parallel Social Spider Algorithm

The financial creditability of the customer needs to be verified by the lender/bank before issuing a credit card. This involves assessment of factors like the economic, social or social-economic background of the person. Thus, the features incorporated into the analysis are mixed data type ex. Income (numerical) and Property Owned (Categorical). In this manuscript, a credit card lending model is designed using a recently proposed parallel social spider algorithm by Shukla and Nanda in 2016. Suitable modifications have been introduced in the coding scheme and mating procedure to efficiently solve the credit assessment problem. Experiments are carried out on various standard credit card data available like German, Australian and Japanese credit card datasets. The superior performance of proposed algorithm is reported as compared to that achieved by K-means, parallel real genetic algorithm and parallel particle swarm optimization (PPSO). The Silhouette Index obtained by various algorithms specifically for Germen dataset are 0.56% by K-means, 0.86% by parallel Real Coded Genetic algorithm, 0.71% by PPSO and 0.84% by proposed method

Congenital rubella syndrome surveillance in India, 2016–21: Analysis of five years surveillance data

Background: In India, facility-based surveillance for congenital rubella syndrome (CRS) was initiated in 2016 to estimate the burden and monitor the progress made in rubella control. We analyzed the surveillance data for 2016–2021 from 14 sentinel sites to describe the epidemiology of CRS. Method: We analyzed the surveillance data to describe the distribution of suspected and laboratory confirmed CRS patients by time, place and person characteristics. We compared clinical signs of laboratory confirmed CRS and discarded case-patients to find independent predictors of CRS using logistic regression analysis and developed a risk prediction model. Results: During 2016–21, surveillance sites enrolled 3940 suspected CRS case-patients (Age 3.5 months, SD: 3.5). About one-fifth (n = 813, 20.6%) were enrolled during newborn examination. Of the suspected CRS patients, 493 (12.5%) had laboratory evidence of rubella infection. The proportion of laboratory confirmed CRS cases declined from 26% in 2017 to 8.7% in 2021. Laboratory confirmed patients had higher odds of having hearing impairment (Odds ratio [OR] = 9.5, 95% confidence interval [CI]: 5.6–16.2), cataract (OR = 7.8, 95% CI: 5.4–11.2), pigmentary retinopathy (OR = 6.7, 95 CI: 3.3–13.6), structural heart defect with hearing impairment (OR = 3.8, 95% CI: 1.2–12.2) and glaucoma (OR = 3.1, 95% CI: 1.2–8.1). Nomogram, along with a web version, was developed. Conclusions: Rubella continues to be a significant public health issue in India. The declining trend of test positivity among suspected CRS case-patients needs to be monitored through continued surveillance in these sentinel sites