This study was designed to elicit women's perceptions of their illness experience with myocardial infarction for the purpose of exploring and describing the nature and meaning of this illness experience and its impact on everyday life. The phenomenological method, a type of qualitative research, was used to direct the study. The data were compiled through a series of semi-structured intensive interviews with eight women. The women were 36 to 71 years of age. Six of the women were married and living with their husbands. The women had been at home following discharge from hospital for 2 to 14 weeks. Data collection and data analysis proceeded simultaneously and data collection ceased once consistent themes were identified and validated and the data collected were sufficiently rich and in-depth. Women explain their illness experience with myocardial infarction as a loss phenomenon and the central and dominant loss within the heart attack experience for women is loss of predictability. Women's need for information following myocardial infarction is not met and lack of energy is a prominent feature in everyday life after a heart attack. Traditional sex role socialization sets the stage for potential problems in women's cardiac rehabilitation, especially in relation to support and role enactment. Physical rehabilitation is not a selected strategy to gain control over their loss experience for women with myocardial infarction. The findings and conclusions of this study suggest a number of implications for nursing practice. There is clear direction for family-centered nursing care in the rehabilitation of women with myocardial infarction to address potential problems in relation to support and role enactment. This study reinforces the value of using the concepts of loss and grief to care for patients with myocardial infarction. Also, this study indicates that, in planning nursing care for women with myocardial infarction, nurses should focus on Interventions to increase support, to meet patient and family educational needs, and to help women to anticipate normal physical and psychological responses to myocardial infarction. This study also has specific implications for the development of structured cardiac rehabilitation programmes addressing the special needs of women. In relation to nursing education, nurses must be prepared to assess, teach, and counsel patients with myocardial infarction and their families. Most importantly, this study directs nursing educators to provide course work in women's health issues to sensitize nurses to this field of study and to equip nurses with the understanding necessary to facilitate changes in women's health care. Implications for future research include further exploration of information needs, support, and strategies for control in relation to women with myocardial infarction. / Applied Science, Faculty of / Nursing, School of / Graduate

Heart disease is the leading cause of death globally. Early diagnosis is the key to successful treatment. By providing noninvasive, non-ionizing, and real-time imaging, echocardiography plays a critical role in identifying heart disease. Compared to other imaging modalities, ultrasound has unparalleled temporal resolution. High frame-rate imaging has enabled the development of new metrics to characterize myocardial mechanics. Strain imaging measures the heart's deformation throughout the cardiac cycle, providing a quantitative assessment of cardiac health. The intention of this dissertation is to bring Myocardial Elastography (ME) closer to clinical realization. ME is a high frame-rate strain imaging technique for transthoracic and intracardiac echocardiography. This work consists of four Aims. There is a fundamental trade-off between spatial and temporal resolution in strain imaging. In Aim 1, the optimal transmit sequence that generates the most accurate and precise strain estimate was determined. Two common approaches to coherent compounding (full and partial aperture) were compared in simulation and in transthoracic imaging of healthy human subjects (n=5). The optimized subaperture compounding sequence (25-element subperture, 90° angular aperture, 10 virtual sources, 300 Hz frame rate) was compared to the optimized steered compounding sequence (60° angular aperture, 15° tilt, 10 virtual sources, 300 Hz frame rate) and was found to measure strain in healthy human subjects with equivalent precision. The optimal compounding configuration was then evaluated against two other high-frame rate transmit strategies, ECG-gated focused imaging, and wide-beam imaging, in simulation and in healthy subjects (n=7). Achieving the highest level of strain precision, ECG-gated focused imaging was determined to be the preferred imaging approach in patients capable of sustaining a breath hold, with compounding preferred in those unable to do so. Rapid diagnosis is essential to successful treatment of myocardial infarction. In Aim 2, ME's ability to track infarct formation and recovery, and localize infarct using regional strain measurments, was investigated in a large animal survival model (n=11). Infarcts were generated via ligation of the left anterior descending, imaging regularly for up to 28 days. A radial strain-based metric, percentage of healthy myocardium by strain (PHM_ε), was developed as a marker for healthy myocardial tissue. PHM_ε was strongly linearly correlated with actual infarct size as determined by gross pathology (R2 = 0.80). ME was capable of diagnosing individual myocardial segments as non-infarcted or infarcted with high sensitivity (82%), specificity (92%), and precision (85%) (ROC AUC = 0.90), and tracked infarct recovery from collateral reperfusion through time. Noninvasive strain imaging at rest can improve pre-test probability accuracy, and reduce unnecessary stress testing. In Aim 3, ME's potential to provide early diagnosis of coronary artery disease was investigated in an ongoing study. Patients undergoing myocardial perfusion imaging were recruited (n=126). Perfusion scores were used as the reference standard. Morphological transformations were integrated into the processing pipeline to reduce variability in the strain measurements. PHM_ε was reintroduced and used to differentiate between patients with and without coronary artery disease. ME was capable of distinguishing between normal patients and those with significant ischemia or infarct (subjects with perfusion defects at rest) with statistical significance (p < 0.05), although a greater sample size is needed to confirm the results. One of the most common treatments for arrhythmia, catheter ablation, can fail if the lesion line intended to terminate the abnormal rhythm is non-contiguous. In Aim 4, the gap resolution and clinical feasibility of Intracardiac Myocardial Elastography (IME) strain imaging, an ablation monitoring technique, was investigated. Lesion size estimation and gap resolution was evaluated in an open chest canine model (n=3), wherein lesion lines consisting of three lesions and two gaps were generated in each canine left ventricle via epicardial ablation. All gaps were resolvable. Average lesion and gap areas were measured with high agreement (33 ± 14 mm2 and 30 ± 15 mm2, respectively) when compared against gross pathology (34 ± 19 mm2 and 26 ± 11 mm2, respectively). Gaps as small as 11 mm2 (3.6 mm on epicardial surface) were identifiable. Patients undergoing ablation to treat typical cavotricuspid isthmus atrial flutter (n=5) were imaged throughout the procedure. In all patients, strain decreased in the cavotricuspid isthmus after ablation (mean paired difference of -17 ± 11 %, p < 0.05). Together, these Aims intend to translate a promising imaging method from research to clinical reality.

Cardiac conduction abnormalities can often lead to heart failure, stroke and sudden cardiac death. Heart disease stands as the leading cause of mortality and morbidity in the United States, accounting for 30% of all deaths. Early detection of malfunctions such as arrhythmias and systolic heart failure, the two heart conditions studied in this dissertation, would definitely help reduce the burden cardiovascular diseases have on public health and overcome the current clinical challenges. The imaging techniques currently available to doctors for cardiac activation sequence mapping are invasive, ionizing, time-consuming and costly. Thus, there is an undeniable urgent need for a non-invasive and reliable imaging tool, which could play a crucial role in the early diagnosis of conduction diseases and allow physicians to choose the best course of action. The 12-lead electrocardiogram (ECG) is the current non-invasive clinical tool routinely used to diagnose and localize cardiac arrhythmias prior to intracardiac catheter ablation. However, it has limited accuracy and can be subject to operator bias. Besides, QRS complex narrowing on the clinical ECG after pacing device implantation is also used for response assessment in patients undergoing Cardiac Resynchronization Therapy (CRT). The latter is an established treatment for systolic heart failure patients who have Left Bundle Branch Block as well as a reduced ejection fraction and prolonged QRS duration. Yet, it is still not well understood why 30 to 40 % of CRT recipients do not respond. Echocardiography, due to its portability and ease-of-use, is the most frequently used imaging modality in clinical cardiology. In this dissertation, we assess the clinical performance of Electromechanical Wave Imaging (EWI) as a high frame rate ultrasound-based functional modality that can non-invasively map the electromechanical activation of the heart, i.e., the transient deformations immediately following the electrical activation. The objective of this dissertation is to demonstrate the potential clinical value of EWI for both arrhythmia detection and CRT characterization applications. The first step in translating EWI to the clinic was ensuring that the technique could reli- ably and reproducibly measure the electromechanical activation sequence independently of the probe angle and imaging view in healthy human volunteers (n=7). This dissertation then demonstrated the accuracy of EWI for localizing a variety of ventricular and atrial arrhythmias (accessory pathways in Wolff-Parkinson-White (WPW) syndrome, premature ventricular contractions, focal atrial tachycardia and macro-reentrant atrial flutter) in pediatric (n=14) and adult (n=55) patients prior to catheter ablation more accurately than 12-lead ECG predictions, as validated against electroanatomical mapping. Additionally, 3D-rendered EWI isochrones were illustrated to be capable of significantly distinguishing different biventricular pacing conditions (p≤0.05) with the RWAT and LWAT metrics, assessing the ventricular dyssynchrony change in heart failure patients (n=16) undergoing CRT, and visualizing it in 3D. EWI also provided quantification of %𝘙𝘔𝘓𝘝 in CRT patients (n=38): the amount of left-ventricular resynchronized myocardium, which was found to be a reliable response predictor at 3-, 6-, or 9-month clinical follow-up through its post-CRT values by significantly identifying super-responders from non-responders within 24 hours of implantation (p≤0.05). Furthermore, 3D-rendered isochrones successfully characterized the ventricular activation resulting from His Bundle pacing for the first time (n=4), which was undistinguishable from true physiological activation in sinus rhythm healthy volunteers with the EWI-based activation time distribution dispersion metric. The dispersion was, however, reported to significantly discriminate novel His pacing from other more conventional biventricular pacing schemes (p≤0.01). Finally, we developed and optimized a fully automated zero-crossing algorithm towards a faster, more robust and less observer dependent EWI isochrone generation process. The support vector machine (SVM) and Random Forest machine learning models were both shown capable of successfully identifying the accessory pathway in WPW patients and the pacing electrode location in paced canines. Nevertheless, the best performing algorithm was hereby proven to be the Random Forest classifier with n=200 trees with a precision rising to 97%, and a predictivity that was not impacted by the type of testing dataset it was applied to (human or canine). Overall, in this dissertation, we established the clinical potential of EWI as a viable assisting visual feedback tool, that could not only be used for diagnosis and treatment planning prior to surgical procedures, but also for monitoring during, and assessing long-term resolution of arrhythmia after catheter ablation or heart failure after a CRT implant.

The Electrocardiogram (ECG) is a tool measuring the electrical activity of the heart, and it is extensively used for diagnosis and monitoring of heart diseases. The ECG signal reflects not only the heart activity but also many other physiological processes. The respiratory activity is a prominent process that affects the ECG signal due to the close proximity of the heart and the lungs. In this thesis, several methods for the extraction of respiratory process information from the ECG signal are presented. These methods allow an estimation of the lung volume and the lung pressure from the ECG signal. The potential benefit of this is to eliminate the corresponding sensors used to measure the respiration activity. A reduction of the number of sensors connected to patients will increase patients’ comfort and reduce the costs associated with healthcare. As a further result, the efficiency of diagnosing respirational disorders will increase since the respiration activity can be monitored with a common, widely available method. The developed methods can also improve the detection of respirational disorders that occur while patients are sleeping. Such disorders are commonly diagnosed in sleeping laboratories where the patients are connected to a number of different sensors. Any reduction of these sensors will result in a more natural sleeping environment for the patients and hence a higher sensitivity of the diagnosis.

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Submitted in fulfillment of the requirements for the Degree of Masters in Technology: Cardiology, Durban University of Technology, 2012. / Cardiomyopathies and their resultant heart failure (HF) remain a major cause of cardiovascular morbidity and mortality (Wood and Picard, 2004). Idiopathic dilated cardiomyopathy (IDCMO) is a primary myocardial disease of unknown cause, characterized by left ventricular (LV) or biventricular dilatation and impaired myocardial contractility. Dilated cardiomyopathy (DCMO), along with rheumatic heart disease and hypertension (HPT), is one of the leading causes of HF in Africa. In fact, in an epidemiology study of 884 patients in Soweto, IDCMO was the second major cause of HF. Thirty five percent of patients in the study, with HF, had IDCMO (Sliwa, Damasceno, Mayosi, 2005). Methodology: Patients referred to the cardiomyopathy (CMO) clinic at Chris Hani Baragwanath hospital, situated in the echocardiographic lab, were recruited, provided they satisfied the exclusion and inclusion criteria and were enrolled after obtaining voluntary informed consent. From May 2009 to September 2010, 70 patients with IDCMO were recruited for this trial. Patients with DCMO were identified by means of echocardiographic criteria which included a left ventricular ejection fraction (LVEF) of less than 45% and an end diastolic dimension (EDD) of greater than of 52 mm (2D in long parasternal axis). Results: In the present study the prevalence of left ventricular (LV) thrombus in patients with IDCMO was 18.6%. When using Univariate logistic regression, the only independent predictors of LV thrombus formation was LVEF and age. However, when multivariate logistic regression analysis was applied to the data, the only predictor with a significant association was age. The reason for this is not clear. It is postulated that perhaps younger patients have differences in the pathophysiology of their disease such as a greater smoldering inflammatory component which may therefore predispose them to thrombus formation. For example the presence of IL-6 may be important in the formation of LV clot in cases of LV dysfunction (Sosin, Bhatia, Davis, Lip, 2003). The association between LVEF and LV thrombus was borderline significant. Conclusion: The prevalence of LV thrombus formation in this cohort of patients with IDCMO was 18.6%. Echocardiographic parameters alone cannot predict which patients are more likely to develop thrombus formation. / National Research Foundation

Lu, Yan. / "October 2010." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 65-68). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Electrocardiogram --- p.1 / Chapter 1.1.1 --- ECG Measurement --- p.2 / Chapter 1.1.2 --- Cardiac Conduction Pathway and ECG Morphology --- p.4 / Chapter 1.1.3 --- A Basic Clinical Approach to ECG Analysis --- p.6 / Chapter 1.2 --- Cardiovascular Disease --- p.7 / Chapter 1.3 --- Motivation --- p.9 / Chapter 1.4 --- Related Work --- p.10 / Chapter 1.5 --- Overview of Proposed Approach --- p.11 / Chapter 1.6 --- Thesis Outline --- p.13 / Chapter 2. --- ECG Signal Preprocessing --- p.14 / Chapter 2.1 --- ECG Model and Its Generalization --- p.14 / Chapter 2.1.1 --- ECG Dynamic Model --- p.14 / Chapter 2.1.2 --- Generalization of ECG Model --- p.15 / Chapter 2.2 --- Empirical Mode Decomposition --- p.17 / Chapter 2.3 --- Baseline Wander Removal --- p.20 / Chapter 2.3.1 --- Sources of Baseline Wander --- p.20 / Chapter 2.3.2 --- Baseline Wander Removal by EMD --- p.20 / Chapter 2.3.3 --- Experiments on Baseline Wander Removal --- p.21 / Chapter 2.4 --- ECG Denoising --- p.24 / Chapter 2.4.1 --- Introduction --- p.24 / Chapter 2.4.2 --- Instantaneous Frequency --- p.26 / Chapter 2.4.3 --- Problem of Direct ECG Denoising by EMD : --- p.28 / Chapter 2.4.4 --- Model-based Pre-filtering --- p.30 / Chapter 2.4.5 --- EMD Denoising Using Significance Test --- p.33 / Chapter 2.4.6 --- EMD Denoising using Instantaneous Frequency --- p.35 / Chapter 2.4.7 --- Experiments --- p.39 / Chapter 2.5 --- Chapter Summary --- p.44 / Chapter 3. --- ECG Classification --- p.45 / Chapter 3.1 --- Database --- p.45 / Chapter 3.2 --- Feature Extraction --- p.46 / Chapter 3.2.1 --- Feature Selection --- p.46 / Chapter 3.2.2 --- Feature Dimension Reduction by GDA --- p.48 / Chapter 3.3 --- Classification by Support Vector Machine --- p.50 / Chapter 3.4 --- Experiments --- p.53 / Chapter 3.4.1 --- Performance of Feature Reduction --- p.54 / Chapter 3.4.2 --- Performance of Classification --- p.57 / Chapter 3.4.3 --- Performance Comparison with Other Works --- p.60 / Chapter 3.5 --- Chapter Summary --- p.61 / Chapter 4. --- Conclusions --- p.63 / Reference --- p.65