Abstract: Detection of Cardiac Arrhythmia (CA) is performed using the clinical analysis of the electrocardiogram (ECG) of a patient to prevent cardiovascular diseases. Machine Learning Algorithms have been presented as promising tools in aid of CA diagnoses, with emphasis on those related to automatic classification. However, these algorithms suffer from two traditional problems related to classification: (1) excessive number of numerical attributes generated from the decomposition of an ECG; and (2) the number of patients diagnosed with CAs is much lower than those classified as “normal” leading to very unbalanced datasets. In this paper, we combine in a coordinate way several data mining techniques, such as clustering, feature selection, oversampling strategies and automatic classification algorithms to create more efficient classification models to identify the disease. In our evaluations, using a traditional dataset provided by the UCI, we were able to improve significantly the effectiveness of Random Forest classification algorithm achieving an accuracy of over 88%, a value higher than the best already reported in the literature.

Abstract: In the field of CT medical image reconstruction, there are two approaches you can take to reconstruct the images: the analytical methods, or the algebraic methods, which can be divided into iterative or direct. Although analytical methods are the most used for their low computational cost and good reconstruction quality, they do not allow reducing the number of views taken and thus the radiation absorbed by the patient. In this paper, we present two direct algebraic approaches for CT reconstruction: performing the Sparse QR (SPQR) factorization of the system matrix or carrying out a singular values decomposition (SVD). We compare the results obtained in terms of image quality and computational time cost and analyze the memory requirements for each case.

Abstract: In recent years interest in blood viscosity has increased significantly in different biomedical areas. Blood viscosity, a measure of the resistance of blood flow, related to its thickness and stickiness, is one of the main biophysical properties of blood. Many factors affect blood viscosity, both in physiological and in pathological conditions. The aim of this study is to estimate blood viscosity by using the regression equation of viscosity which is based on hematocrit and total plasma proteins. It can be used to perform several observations regards the main factors which can influence blood viscosity. The main contribution regards the correlation between viscosity values and other important biological parameters such as cholesterol. This correlation has been supported by performing statistical tests and it suggest that the viscosity could be the main risk factor in cardiovascular diseases. Moreover, it is the only biological measure being correlated with the other cardiovascular risk factors. Results obtained are compliant with values obtained by using the standard viscosity measurement through a viscometer.