Detection and classification of ADHD from EEG signals using tunable Q-factor wavelet transform

The automatic identification of Attention Deficit Hyperactivity Disorder (ADHD) is essential for developing ADHD diagnosis tools that assist healthcare professionals. Recently, there has been a lot of interest in ADHD detection from EEG signals because it seemed to be a rapid method for identifying and treating this disorder. This paper proposes a technique for detecting ADHD from EEG signals with the nonlinear features extracted using tunable Q-wavelet transform (TQWT). The 16 channels of EEG signal data are decomposed into the optimal amount of time-frequency sub-bands using the TQWT filter banks. The unique feature vectors are evaluated using Katz and Higuchi nonlinear fractal dimension methods at each decomposed levels. An Artificial Neural Network classifier with a 10-fold cross-validation method is found to be an effective classifier for discriminating ADHD and normal subjects. Different performance metrics reveal that the proposed technique could effectively classify the ADHD and normal subjects with the highest accuracy. The statistical analysis showed that the Katz and Higuchi nonlinear feature estimation methods provide potential features that can be classified with high accuracy, sensitivity, and specificity and is suitable for automatic detection of ADHD. The proposed system is capable of accurately distinguishing between ADHD and non-ADHD subjects with a maximum accuracy of 100%

Compression Performance Analysis of Electrogastrogram (Egg) Using Different Wavelet Transforms For Telemedicine

Electrogastrogram (EGG) is the non-invasive graphical representation of stomach’s electrical activity for diagnosing stomach Disorders. EGG signal compression has an important role in Tele-diagnosis, Tele-prognosis and survival analysis of all stomach dysrhythmias, when the patient is geographically isolated. There are plenty of signal compression techniques available and proposed over years. Due to some drawbacks like high cost, signal loss and poor compression ratio leads the signal into inefficient at receiver’s end. The compression of digital EGG in telemedicine holds three major advantages like efficient &amp; economic usage of storage data, reduction of the data transmission rate and good transmission bandwidth conversation. In this study EGG signals are tested with different wavelet transforms such as Biorthogonal, coiflet, Daubechies, Haar, reverse biorthogonal and symlet wavelet transforms using MATLAB software, in order to find best performance wavelet for telemedicine. The performance is mathematically analyzed using the values of Percent Root Mean Square Difference (PRD), Compression ratio (CR) and recovery ratio. The result of better compression performance in signal compression could definitely be a great asset in telemedicine field for transferring more quantities of Biological signals.</jats:p

Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers

Species distribution models (SDMs) link species occurrence to environmental characteristics to predict suitable habitats beyond known occurrences. The conventional procedure to fit SDMs for individual organisms detected at some distance away from observers is to characterize species’ associated habitat based on observer’s survey location. However, each surveyed individual may be detected in habitats distinct from those where observers are located. Here, we compared environmental variables centered on the observer and individual bird locations and the consequent effects on SDMs performance. We utilized remote sensing data on observer- and bird-locations to characterize habitat at three radii (pixel radius: 30-m; fixed radius: 100-m; species-specific effective detection radius). We trained Poisson boosted regression tree models for 105 bird species from structured professional surveys. We evaluated models’ predictability with Kendall’s rank correlation coefficient and used linear mixed-effect models to measure the effect of characterization locations and radii. Models based on bird locations exhibited a median increase of 22.9% in predictive performance, demonstrating higher Kendall’s rank correlation coefficients than those based on observer locations, leading to more reliable prediction maps. SDMs of habitat specialists and generalists performed better when habitat characterization was centered on bird instead of surveyor locations. A higher percentage of habitat specialists (72%) than generalists (55%) showed better model performance in bird-location than in observer-location models. Across radii, fixed radius generally performed better than species-specific effective and pixel radii. Our findings emphasize the importance of prioritizing habitat characterizations based on detected individuals’ locations to enhance model performance and improve species distribution predictions

Modelling and Blood Flow Analysis of Internal Pudendal Artery

A common male sexual disorder is erectile dysfunction which has multidimensions. In this fast-moving world, it is prominently seen in lots of males. There are many causes for Erectile Dysfunction, one of the major causes is the improper supply of blood to the penile organ. That may be due to vasoconstriction or blockage in the internal pudendal artery which supplies oxygen to the penile organ. A simulated model of the internal iliac artery to the internal prudential artery is designed and a flow simulation is done using Solid works software. The Computed Tomography of a male subject is obtained and a three-dimensional model of the abdominal artery is extracted using MIMICS (Materialize Interactive Medical Image Control System) software. By making use of the measured dimensions from the three-dimensional image. The 3D models (Normal condition, Abnormal condition with blockage, and Abnormal condition with constrictions) are designed and the Flow analysis is done in Solid works software. By the end of the study, we came to a conclusion that at normal temperature and pressure, the simulated normal volumetric blood flow at the internal pudendal artery is 6.88701e-09 m3/s and for abnormal cases the simulated volumetric blood flow is 2.6107e-09m3/s.</jats:p