Computational fluid dynamics study of the aortic valve opening on hemodynamics characteristics

In this work, the 3D geometry of patient specific aorta was utilized to carry out CFD studies on the effect of different valve opening (45°,62.5° and fully opening) on the hemodynamic properties. The result shows that the lower valve opening induced jet flow and hampered the flow on the additional carotid arteries. Besides, the leaflets were subjected to extreme stress values having disastrous consequences. Consequently, stenosis which is characterized by weaker leaflets and low valve openings has serious impact on the well being of humans

Non-fiducial based ECG biometric authentication using one-class support vector machine

Identity recognition encounters with several problems especially in feature extraction and pattern classification. Electrocardiogram (ECG) is a quasi-periodic signal which has highly discriminative characteristics in a population for subject recognition. The personal identity verification in a random population using kernel-based binary and one-class Support Vector Machines (SVMs) has been considered by other biometric traits, but has been so far left aside for analysis of ECG signals. This paper investigates the effect of different parameters of data set size, labeling data, configuration of training and testing data sets, feature extraction, different recording sessions, and random partition methods on accuracy and error rates of these SVM classifiers. The experiments were carried out with defining a number of scenarios on ECG data sets designed rely on feature extractors which were modeled based on an autocorrelation in conjunction with linear and nonlinear dimension reduction methods. The experimental results show that Kernel Principal Component Analysis has lower error rate in binary and one-class SVMs on random unknown ECG data sets. Moreover, one-class SVM can be robust recognition algorithm for ECG biometric verification if the sufficient number of biometric samples is available

Feature level fusion for biometric verification with two-lead ECG signals

Electrocardiogram (ECG) is a new generation of biometric modality which has unique identity properties for human recognition. There are few studies on feature level fusion over short-term ECG signals for extracting non-fiducial features from autocorrelation of ECG windows with an identical length. In this paper, we provide an experimental study on fusion at feature extraction level by using autocorrelation method in conjunction with different dimensionality reduction techniques over vector sets with different window lengths from short and long-term two-lead ECG recordings. The results indicate that the window and recording lengths have significant effects on recognition rates of the fused ECG data sets

Multiclass support vector machines for classification of ECG data with missing values

The article presents an experimental study on multiclass Support Vector Machine (SVM) methods over a cardiac arrhythmia dataset that has missing attribute values for electrocardiogram (ECG) diagnostic application. The presence of an incomplete dataset and high data dimensionality can affect the performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values

Multiclass support vector machines for classification of ECG data with missing values

The article presents an experimental study on multiclass Support Vector Machine (SVM) methods over a cardiac arrhythmia dataset that has missing attribute values for electrocardiogram (ECG) diagnostic application. The presence of an incomplete dataset and high data dimensionality can affect the performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values

Numerical analysis using a fixed grid method for cardiovascular flow application

Motivated by the current interest in the numerical simulation of biological flows in the human body, we develop a new method to simulate fluid flow embedded in a solid region. The novelty of this method lies on the use of a fixed grid in the entire computational domain. The formulation is an extension of the multiphase fluid flow that belongs to the category of the penalty method, where high viscosity is imposed on a solid region. A free open source library, namely, OpenFOAM, is used to integrate high order and advanced numerical schemes into these computational formulations. The Monotone Upstream System for Conservation Laws (MUSCL) scheme by van Leer, with a harmonic limiter from the category of the total variation bounded (TVB) scheme, is used for cell face interpolation. The robustness and accuracy of the solver are compared with the benchmark test case, namely, the free fall of a solid sphere. The test case validates that the rigidity of the solid sphere is ensured with the selected high viscosity ratio. The accurate terminal velocity of the falling solid sphere proves the no-slip condition at the solid-liquid interface. As a real application implementation, the flow on a simplified idealized model of heart valve stenosis is presented

ECG biometric authentication based on non-fiducial approach using kernel methods

Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications. Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic. At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). The effects of different dimensionality reduction techniques for use in feature extraction were investigated to evaluate verification performance rates of a multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach. The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA)

Antioxidant and Anti-Adipogenic Activities of Momordica cochinchinensis (Lour). Spreng Fruit Extracts

Momordica cochinchinensis (Lour) Spreng, known as gac fruit, is rich in bioactive compounds like carotenoids (β-carotene, lycopene, and lutein). This study assessed the antioxidant, cytotoxic, and anti-adipogenic properties of gac fruit extracts (GFE) from different fractions (peel, pulp, aril), using 3T3-L1 adipocytes. Method: Gac extracts’ DPPH radical scavenging was tested with 1000µg/mL dilutions. 3T3-L1 pre-adipocyte viability was measured via MTT assay. Differentiated adipocytes were treated (75, 150, 300 µg/mL) with GFE for 7 days. Inhibitory effects on adipogenesis and lipid accumulation were studied through Oil Red O staining. Triglyceride content was quantified and compared to controls. Results: IC50 values against DPPH radicals were 660µg/mL (peel), 560µg/ mL (pulp), and 820µg/mL (aril). 3T3-L1 cell viability was unaffected up to 200µg/mL. However, 200µg/mL GFE decreased viability, inhibiting growth. Gac extracts effectively reduced lipid accumulation and hindered cell differentiation dose-dependently. Pulp extract notably reduced intracellular triglycerides, surpassing aril and peel effects. Conclusion: Gac fruit extract fractions (peel, pulp, and aril) efficiently inhibited adipogenesis in 3T3-L1 cells, evidenced by lowered lipid accumulation, triglyceride content, and cell viability. This study highlights gac fruit extracts’ potential therapeutic use against obesity

Perceived learning needs among coronary artery disease patients: a study in a tertiary hospital

Introduction: Determination of learning needs is central for holistic patient education, to sustain behavior changes and to control patient’s risk factor. However, patients often sense that their learning needs are unmet and information provided was too general. Thus, this study aimed to determine the perceived learning needs and their level of importance among Coronary Artery Disease (CAD) patients. Methods: The current investigation is a descriptive, cross-sectional study for which all CAD patients were selected using the cencus method. The data was collected using Cardiac Patients Learning Needs Inventory. The questionnaire was delivered to 140 CAD patients who had their follow-up in a cardiology clinic. The instrument is reliable with a Cronbach’s alpha coefficient of 0.96. The study design followed STROBE cross-sectional design process guideline. Results: Participants’ mean age was 58.96 ± 9.42 years. More than half of the participants were males (62.9%), employed (52.0%) and had attained secondary level education (69.3%). Around two-thirds (60.7%) of the patients perceived to have high learning needs. Gender and highest educational achievement were significantly associated with perceived learning needs. The most significant perceived learning needs were medication information, risk factors for CAD, information on diet, physical activity, anatomy and physiology, and other related information. Conclusion: This study has identified the important domains of learning needs among CAD patients. Findings from the present study will provide important input for future cardiac educational strategies to reduce the rate of hospital readmission and death

Physicochemical Properties, Proximate Composition, and Carotenoid Content of Momordica cochinchinensis L. Spreng (Gac) Fruit

The study aims to determine the physical and chemical properties of Momordica cochinchinensis L. Spreng (gac) fruits. Fruit size varied, weighing 359.17 to 588.33g, with lengths of 11.10 to 13.92 cm and circumferences of 27.43 to 30.67 cm. Components included pulp (34.06 to 41.58%), seeds (23.11 to 29.70%), peel (16.65 to 20.60%), and aril (15.64 to 18.64%). Skin and aril colour parameters (L*, a*, b*) indicated maturity and carotenoid content. Aril had higher acidity (pH 5.54±0.02, titratable acidity [TA] 0.03 to 0.05g/L), total soluble solids (TSS, 11.57%±0.52 °Brix), and carbohydrates (55.6 g/100 g) than pulp (pH 5.65±0.02, TA 0.01 to 0.02g/L, TSS 4.90%±0.33 °Brix, carbohydrates 30.9 g/100 g). Peel contained most protein (6.2 g/100 g) and dietary fibre (56.9 to 58.1 g/100 g). Glucose and fructose were found in both pulp and aril. Potassium levels were highest in peel (817.59 mg/100 g), followed by pulp (658.20 mg/100 g) and aril (228.79 mg/100 g). Lycopene dominated carotenoids, especially in aril (31.7 to 103.7 mg/g). β-carotene, lutein, astaxanthin, and zeaxanthin were also present. β-carotene (2.9 to 9.6 mg/g) was second to lycopene, followed by astaxanthin (1.54 to 4.91 mg/g), lutein (0.16 to 1.35 mg/g), and zeaxanthin (0.35 to 1.49 mg/g), absent in pulp. These findings have implications for the food industry, offering insights into gac fruit’s nutritional potential. Malaysian gac exhibited superior nutritional content, with pulp and aril as notable sources of carbohydrates and minerals for consumption and aril as a promising source of healthy oils