Narrow Results

This study reports a new technique for the analysis of electromyographic signals from the low back muscles. More specifically, the effect of unexpected load on a normal subject and a subject with chronic low back pain was determined and quantified using wavelet based analysis (Morlet wavelet). The analysis was performed using a Wavelet software system, subsequently referred to as PSCW. The system identified automatically, accurately, and in a uniquely reproducible manner the time response of the erector spinae muscle. The exact number of responses as well as their corresponding time and amplitude were determined and tabulated. It was observed that the initial reaction time for the normal subject was faster than the reaction time for the subject chronic low back pain. The importance of this observation may help in the understanding of the physiology of the neuromuscular system associated with low back spine disorders. It is believed that an occupational and clinical test based on this observation that could give an accurate assessment of the status of low back disorder could be designed. Based on this assessment a rehabilitation program could be developed with the objective of improving the condition of a spine disorder (decrease the initial response time) by muscle strengthening.

A noninvasive optical method for determining the optical properties of normal and cancerous breast tissues by interpolating wavelet approach using the characteristics of nanoscale FinFET sensor has been theoretically developed and presented in this paper. This novel approach classifies the normal and cancerous tissues of human breast by calculating the surface potential variations of nanoscale FinFET illuminated by laser source of different wavelengths. Using these surface potential variations, the optical properties of the tissues are determined. By using this method, the point-to-point variations in tissue composition and structural variations in healthy and diseased tissues could be identified. The results obtained are used to examine the performance of the device for its suitable use as a nanoscale sensor.

Glaucoma is one of the most common causes of blindness. Robust mass screening may help to extend the symptom-free life for affected patients. To realize mass screening requires a cost-effective glaucoma detection method which integrates well with digital medical and administrative processes. To address these requirements, we propose a novel low cost automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The paper discusses a system for the automated identification of normal and glaucoma classes using higher order spectra (HOS), trace transform (TT), and discrete wavelet transform (DWT) features. The extracted features are fed to a support vector machine (SVM) classifier with linear, polynomial order 1, 2, 3 and radial basis function (RBF) in order to select the best kernel for automated decision making. In this work, the SVM classifier, with a polynomial order 2 kernel function, was able to identify glaucoma and normal images with an accuracy of 91.67%, and sensitivity and specificity of 90% and 93.33%, respectively. Furthermore, we propose a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT, and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images.