Classification of Atrial Fibrillation using Random Forest Algorithm

The electrocardiogram is indicates the electrical activity of the heart and it can be used to detect cardiac arrhythmias. In the present work, we exhibited a methodology to classify Atrial Fibrillation (AF), Normal rhythm, and Other abnormal ECG rhythms using a machine learning algorithm by analyzing single-lead ECG signals of short duration. First, the events of ECG signals will be detected, after that morphological features and HRV features are extracted. Finally, these features are applied to the Random Forest classifier to perform classification. The Physionet challenge 2017 dataset with more than 8500 ECG recordings is used to train our model. The proposed methodology yields an F1 score of 0.86, 0.97, and 0.83 respectively in classifying AF, normal, other rhythms, and an accuracy of 0.91 after performing a 5-fold cross-validation

A Machine learning Classification approach for detection of Covid 19 using CT images

Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in December 2019. World Health Organization declared Covid 19 as a transmission disease. The symptoms were cough, loss of taste, fever, tiredness, respiratory problem. These symptoms were likely to show within 11 –14 days. The RT-PCR and rapid antigen biochemical tests were done for the detection of COVID 19. In addition to biochemical tests, X-Ray and Computed Tomography (CT) images are used for the minute details of the severity of the disease. To enhance efficiency and accuracy of analysis/detection of COVID images and to reduce of doctors' time for analysis could be addressed through Artificial Intelligence. The dataset from Kaggle was utilized to analyze. The statistical and GLCM features were extracted from CT images for the classification of COVID and NON-COVID instances in this study. CT images were used to extract statistical and GLCM features for categorization. In the proposed/prototype model, we achieved the classification accuracy of 91%, and 94.5% using SVM and Random Forest respectively

Visit-to-visit and 24-h blood pressure variability: association with endothelial and smooth muscle function in African Americans

The purpose of this study was to investigate the association of visit-to-visit and 24-h blood pressure (BP) variability with markers of endothelial injury and vascular function. We recruited 72 African Americans who were non-diabetic, non-smoking and free of cardiovascular (CV) and renal disease. Office BP was measured at three visits and 24-h ambulatory BP monitoring was conducted to measure visit-to-visit and 24-h BP variability, respectively. The 5-min time-course of brachial artery flow-mediated dilation and nitroglycerin-mediated dilation were assessed as measures of endothelial and smooth muscle function. Fasted blood samples were analyzed for circulating endothelial microparticles (EMPs). Significantly lower CD31+CD42− EMPs were found in participants with high visit-to-visit systolic blood pressure (SBP) variability or high 24-h diastolic blood pressure (DBP) variability. Participants with high visit-to-visit DBP variability had significantly lower flow-mediated dilation and higher nitroglycerin-mediated dilation at multiple time-points. When analyzed as continuous variables, 24-h mean arterial pressure variability was inversely associated with CD62+ EMPs; visit-to-visit DBP variability was inversely associated with flow-mediated dilation normalized by smooth muscle function and was positively associated with nitroglycerin-mediated dilation; and 24-h DBP variability was positively associated with nitroglycerin-mediated dilation. All associations were independent of age, gender, body mass index and mean BP. In conclusion, in this cohort of African Americans visit-to-visit and 24-h BP variability were associated with measures of endothelial injury, endothelial function and smooth muscle function. These results suggest that BP variability may influence the pathogenesis of CV disease, in part, through influences on vascular health

Nitrogen sources on TPOMW valorization through solid state fermentation performed by Yarrowia lipolytica

This manuscript reports the valorization of two-phase olive mill waste (TPOMW) as raw material and carbon source for solid state fermentation using Yarrowia lipolytica as biocatalyst. Due to its chemical characteristics, a combination of different raw materials (TPOMW and wheat bran, WB) was evaluated and two distinct nitrogen sources were applied as supplementation for lipase production. A TPOMW/WB ratio of 1:1 and supplementation with ammonium sulfate was chosen as the best condition. The productivity in 24 h reached 7.8 U/gh and, after four days of process, only decreased about 35%. Process pH ranged from 5.5-5.9, remaining in an acid range. Thus, the successful use of TPOMW, a watery solid by-product with high content of lipids, as raw material for Yarrowia lipolytica growth and lipase production provided an environmental friendly alternative to valorize such waste.The authors kindly acknowledge the financial aid and research scholarships given by CAPES. Maria Alice Zarur Coelho thanks CNPq (Proc. 308890/ 2013-2)

The Effects of Energy Drinks on Anaerobic Human Performance and Mood

Please view abstract in the attached PDF file