Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus

Cardiovascular Magnetic Resonance Myocardial Perfusion Mapping for the Assessment of Coronary Artery Disease

Pixelwise myocardial perfusion mapping is a novel cardiovascular magnetic resonance (CMR) technique enables quantitative measurement of myocardial blood flow (MBF) at a pixel level. This could improve the accuracy of detection of obstructive coronary artery disease (CAD) and may also have a role in the diagnosis and assessment of coronary microvascular dysfunction (CMD). In this thesis, I explore the use of this novel technique in cohorts of clinical patients and controls with suspected CAD or CMD. Firstly, I demonstrate that stress MBF measured using perfusion mapping is accurate for the detection of CAD using invasive fractional flow reserve (FFR) as the reference standard, and that global stress MBF can be used as a marker of CMD using invasive index of microcirculatory resistance (IMR) as the reference standard. One limitation of adenosine stress testing is the confirmation of adequate hyperaemia with lack of gold standard non-invasive marker. Here, I demonstrate that regional stress MBF can be utilised as a non-invasive marker of adequate stress response. Another limitation of stress MBF is the relatively poor performance for the detection of multivessel disease. In a cohort of patients with confirmed two- and three-vessel disease I demonstrate that perfusion mapping is superior to visual analysis for the correct identification of disease severity. Perfusion mapping provides a host of options for quantitative image analysis. I show that the most reliable method for detection of coronary disease at a patient level is the presence of reduced MBF in two adjacent myocardial segments. In summary, in this thesis I performed a series of studies investigating the clinical utilisation of CMR perfusion mapping that can be translated to clinical practice to enhance the performance of stress perfusion CMR

Doctor of Philosophy

dissertationComputational simulation has become an indispensable tool in the study of both basic mechanisms and pathophysiology of all forms of cardiac electrical activity. Because the heart is comprised of approximately 4 billion electrically active cells, it is not possible to geometrically model or computationally simulate each individual cell. As a result computational models of the heart are, of necessity, abstractions that approximate electrical behavior at the cell, tissue, and whole body level. The goal of this PhD dissertation was to evaluate several aspects of these abstractions by exploring a set of modeling approaches in the field of cardiac electrophysiology and to develop means to evaluate both the amplitude of these errors from a purely technical perspective as well as the impacts of those errors in terms of physiological parameters. The first project used subject specific models and experiments with acute myocardial ischemia to show that one common simplification used to model myocardial ischemia-the simplest form of the border zone between healthy and ischemic tissue-was not supported by the experimental results. We propose a alternative approximation of the border zone that better simulates the experimental results. The second study examined the impact of simplifications in geometric models on simulations of cardiac electrophysiology. Such models consist of a connected mesh of polygonal elements and must often capture complex external and internal boundaries. A conforming mesh contains elements that follow closely the shapes of boundaries; nonconforming meshes fit the boundaries only approximately and are easier to construct but their impact on simulation accuracy has, to our knowledge, remained unknown. We evaluated the impact of this simplification on a set of three different forms of bioelectric field simulations. The third project evaluated the impact of an additional geometric modeling error; positional uncertainty of the heart in simulations of the ECG. We applied a relatively novel and highly efficient statistical approach, the generalized Polynomial Chaos-Stochastic Collocation method (gPC-SC), to a boundary element formulation of the electrocardiographic forward problem to carry out the necessary comprehensive sensitivity analysis. We found variations large enough to mask or to mimic signs of ischemia in the ECG

DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features

One of the leading causes of mortality worldwide is cardiovascular disease (CVD). Electrocardiography (ECG) is a noninvasive and cost-effective tool to diagnose the heart’s health. This study presents a multi-class classifier for the prediction of four different types of Cardiovascular Diseases, i.e., Myocardial Infarction, Hypertrophy, Conduction Disturbances, and ST-T abnormality using 12-lead ECG. There are four key steps involved in the presented work: data preprocessing, feature extraction, data preparation, and augmentation, and modelling for multi-class CVD classification. The sixteen-time domain augmented features are used to train the classifier. The work is divided into three parts: extracting the features from raw 12-lead ECG signals, data preparation and augmentation, and training, testing, and validating the classifier. A comparative study of the performance of five different classifiers (i.e., Random Forest (RF), K Nearest Neighbors (KNN), Gradient Boost, Adda Boost, and XG Boost has also been presented. Accuracy, precision, recall, and F1 scores are used for performance evaluation. Further, the Receiver Operating Curve (ROC) is traced, and the Area Under the Curve (AUC) is calculated to ensure the unbiased performance of the classifier. The application of the proposed classifier in the Smart Healthcare framework has also been discussed.publishedVersio

Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods

The "Cardio Vascular Diseases (CVDs)" had already attained worrisome proportions in both advanced and emerging nations in recent times. Physically inactive behaviors, altered eating, and occupational routines, and reduced daily fitness were all recognized as crucial contextual elements, in addition to genetics. Considering CVDs have such a significant morbidity and mortality, accurate and early diagnosis of cardiac disease by "ElectroCardioGram (ECG)" allows clinicians to decide suitable therapy for a multitude of cardiovascular disorders. The interpretation of ECG signal is an important bio-signal processing area that involves the application of computer science and engineering to detect and visualize the functional status of the heart. Therefore, in the present work, a detailed study on ECG signals denoising and abnormalities detection using different techniques were performed. Annoying distortions and noisy particles are common in ECG signals. The "Biased Finite Impulse Response (BFIR)" preprocessing filtering is employed in this research to eliminate the noises in the raw ECG signals. The "Nonlinear-Hamilton" segmentation method is employed to segment the 'R' peak signals.  To decrease the extraneous features included in the segmented ECG data, the innovative "Enhanced Principal Component Analysis (EPCA)" was applied for feature extraction. A unique "Enhanced version of the Support Vector Machine (ESVM)" framework with a "Weighting Kernel" based technique is proposed for classifying the ECG data. The 'Q', 'R', and 'S' waves in the given ECG data will be identified by this framework, allowing it to characterize the cardiac rhythm. The evaluation metrics of the EPCA-ESVM proposed method is comparatively analyzed with our previous approach EPSO. To estimate the results for the dataset from MIT-BIH it was experimented with by the EPSO and the EPCA-ESVM methods focused upon different parameters such as Accuracy, F1-score, etc. The final findings of the EPCA-ESVM method were good than the EPSO method in which the accuracy is higher even though unbalanced data were present

Association of adverse cardiovascular outcomes with weighted morphologic variability following non-ST-elevation acute coronary syndromes

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 65-68).Patients who have had an acute coronary syndrome (ACS) are at a relatively high risk of having subsequent adverse cardiac events. Several electrocardiographic (ECG) measures such as heart rate variability, heart rate turbulence, deceleration capacity, T-wave altemans, and morphologic variability have been used to identify patients at an increased risk of recurrent myocardial infarctions and cardiovascular death. In this work, we develop a new ECG-based measure for patient risk stratification called weighted morphologic variability. This measure is based on assessment of beat-to-beat changes in the morphology of consecutive beats. Weighted morphologic variability identifies patients who are at more than four-fold risk for cardiovascular death, which is an improvement in ECG-based risk stratification. The body of this work suggests that prognosticating patients based on electrocardiographic measures is an effective way of identifying those at risk of adverse cardiovascular outcomes.by Joyatee Mudra Sarker.M.Eng