Numerical modeling and analysis of cardiac stent using blood hammer principle.

BACKGROUND: Atherosclerosis is a condition which disrupts blood flow due to plaque build-up inside the arteries. Under conditions where consecutive plaques are prevailing blood hammer principle is exhibited. OBJECTIVE: The pressure and shear stress produced at an infinitesimal area act as the governing equation for stent modeling. The leading order pressure lays the foundation for the design of cardiac stents with definite dimensions. METHOD: The designed stent was encapsulated inside a crimper validated through ANSYS-static and transient structural simulation to derive the total deformation, equivalent strain, and stress exerted on the stent. Five different biomaterials stainless steel 316, cobalt, chromium, platinum, and Poly lactic acid were selected for the material assessment. RESULT: Static and Transient structural analysis for a period of 1 and 10 secs was implemented for a stent with and without a crimper. The material performance in terms of total deformation, equivalent stress, and strain are analyzed. CONCLUSION: The paper envisions the dynamics of blood hammer in atherosclerosis that provides the changes in the pressure and clotting process. It shows the promising results of the stent behavior in varied forces which gives valuable insights for future improvement in stent design and material selection.

Cognizance detection during mental arithmetic task using statistical approach.

The handheld diagnosis and analysis are highly dependent on the physiological data in the clinical sector. Detection of the defect in the neuronal-assisted activity raises the challenge to the prevailing treatment that benefits from machine learning approaches. The congregated EEG data is then utilized in design of learning applications to develop a model that classifies intricate EEG patterns into active and inactive segments. During arithmetic problem-solving EEG signal acquired from frontal lobe contributes for intelligence detection. The low intricate statistical parameters help in understanding the objective. The mean of the segmented samples and standard deviation are the features extracted for model building. The feature selection is handled using correlation and Fisher score between {Fp1 and F8} and priority ranking of the regions with enhanced activity are selected for the classifier models to the training net. The R-studio platform is used to classify the data based on active and inactive liability. The radial basis function kernel for support vector machine (SVM) is deployed to substantiate the proposed methodology. The vulnerable regions F1 and F8 for arithmetic activity can be visualized from the correlation fit performed between regions. Using SVM classifier sensitivity of 92.5% is obtained for the selected features. A wide range of clinical problems can be diagnosed using this model and used for brain-computer interface.

An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm.

The interpretation of various cardiovascular blood flow abnormalities can be identified using Electrocardiogram (ECG). The predominant anomaly due to the blood flow dynamics leads to the occurrence of cardiac arrhythmias in the cardiac system. In this work, estimation of cardiac output (CO) parameter using blood flow rate analysis is carried out, which is a vital parameter to identify the subjects with left- ventricular arrhythmias (LVA). In particular, LVA is a resultant component of characteristic changes in blood rheology (blood flow rate). The CO is an intrinsic parameter derived from the stroke volume (SV) characterized by end-diastolic/systolic volumes (EDV/ESV) and heart rate. The pumping of blood from left ventricle (LV) reconciles in to R-R intervals depicted on ECG, which are used for heart rate estimation. The deviation from the nominal values of CO implies that, the subject is more prone to LVA. Further, the identification of subjects with LVA is accomplished by computing the features from the ECG signals. The proposed Feature Ranking Score (FRS) algorithm employs different statistical parameters to label the score of the extracted features. The feature score enables the selection optimal features for classification. The optimal features are further given to the Least Square- Support Vector Machine (LS-SVM) classifier for training and testing phases. The signals are acquired from public domain MIT-BIH arrhythmia database, used for validating the proposed technique for identifying the LVA using blood flow.