Churchhatch8027

Echocardiogram 4 years prior suggested mild to moderate biatrial enlargement with trivial mitral valve regurgitation. He did not go in for any follow-up until this admission. He had no other associated diseases, nor use of medicine.Hypertension (HPT) is a serious risk factor for cardiovascular disease and if not controlled in the early stage, can lead to serious complications. Long-standing HPT can induce heart muscle hypertrophy which will be reflected on electrocardiography (ECG). However, early stage of HPT may have no clinically discernible ECG perturbations, and is difficult to diagnose manually from the standard ECG. Hence, we propose an automated ECG based system that can automatically detect the ECG changes in the early stages of HPT. This work is based on ECG signals obtained from 139 HPT patients (SHAREE database) and 52 healthy subjects (PTB database). The ECG signal is non-stationary with relatively short duration, and rhythmic. Two-band optimal bi-orthogonal wavelet filter bank (BOWFB) and machine learning are used to automatically diagnose low, high-risk hypertension, and healthy control using ECG signals. Five-level wavelet decomposition is used to produce six sub-bands (SBs) from each ECG signal using BOWFB. Sample and wavelet entropy features are calculated for all six SBs. The features calculated SBs are fed to the k-nearest neighbor (KNN), support vector machine (SVM), and ensemble bagged trees (EBT) classifiers. In this work, we have obtained the highest average classification accuracy of 99.95% and area under the curve of 1.00 using EBT classifier in classifying healthy control (HC), low-risk hypertension (LRHPT) and high-risk hypertension (HRHPT) classes with ten-fold cross validation strategy. Hence the developed system can be used in clinics, or even in remote detection of HPT stages using ECG signals.Intestinal parasites are responsible for several diseases in human beings. In order to eliminate the error-prone visual analysis of optical microscopy slides, we have investigated automated, fast, and low-cost systems for the diagnosis of human intestinal parasites. In this work, we present a hybrid approach that combines the opinion of two decision-making systems with complementary properties (DS1) a simpler system based on very fast handcrafted image feature extraction and support vector machine classification and (DS2) a more complex system based on a deep neural network, Vgg-16, for image feature extraction and classification. DS1 is much faster than DS2, but it is less accurate than DS2. Fortunately, the errors of DS1 are not the same of DS2. During training, we use a validation set to learn the probabilities of misclassification by DS1 on each class based on its confidence values. When DS1 quickly classifies all images from a microscopy slide, the method selects a number of images with higher chances of misclassification for characterization and reclassification by DS2. Our hybrid system can improve the overall effectiveness without compromising efficiency, being suitable for the clinical routine - a strategy that might be suitable for other real applications. As demonstrated on large datasets, the proposed system can achieve, on average, 94.9%, 87.8%, and 92.5% of Cohen's Kappa on helminth eggs, helminth larvae, and protozoa cysts, respectively.This study numerically investigates the pathological changes of fluid flow in cartilage contact gap due to the changes in cartilage surface roughness and synovial fluid characteristics in osteoarthritic (OA) condition. First, cartilage surface topographies in both healthy and OA conditions are constructed using a numerical approach with consideration of both vertical and horizontal roughness. Then, constitutive equations for synovial fluid viscosity are obtained through calibration against previous experimental data. click here Finally, the roughness and synovial fluid information are input into the gap flow model to predict the gap permeability. The results show that the rougher surface of OA cartilage tends to decrease gap permeability by around 30%-60%. More importantly, with the reduction in gap size, the decrease in gap permeability becomes more significant, which could result in an early fluid ultrafiltration into the tissue. Moreover, it is demonstrated that the pathological synovial fluid has more deleterious effects on the gap permeability than the OA cartilage surface, as it could potentially increase the gap permeability by a few hundred times for pressure gradients less than 106 Pa/m, which could inhibit the fluid ultrafiltration into the tissue. The outcomes from this research indicate that the change in fluid flow behaviour in contact gap in OA condition could significantly affect the function of articular joints.

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of radiological imaging patterns of breast lesions into benign and malignant states.

We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers (CL) that we expect to yield more accurate numerical solutions than conventional whole-region of interest (ROI) sparse analyses. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP-S), or a log likelihood function (BBLL-S).

To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We utilized the proposed approach for separation of breast lesions into benign and malignant categories in mammograms. The level of difficulty is high in this application and the accuracy may depend on the lesion size. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem, producing AUC (area under the receiver operating curve) value of 89.1% for randomized 30-fold cross-validation.

Furthermore, our comparative experiments showed that the BBLL-S decision function may yield more accurate classification than BBMAP-S because BBLL-S accounts for possible estimation bias.

Furthermore, our comparative experiments showed that the BBLL-S decision function may yield more accurate classification than BBMAP-S because BBLL-S accounts for possible estimation bias.