Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

ECG classification efficient modeling with artificial bee colony optimization data augmentation and attention mechanism

In addressing the key issues of the data imbalance within ECG signals and modeling optimization, we employed the TimeGAN network and a local attention mechanism based on the artificial bee colony optimization algorithm to enhance the performance and accuracy of ECG modeling. Initially, the TimeGAN network was introduced to rectify data imbalance and create a balanced dataset. Furthermore, the artificial bee colony algorithm autonomously searched hyperparameter configurations by minimizing Wasserstein distance. Control experiments revealed that data augmentation significantly boosted classification accuracy to 99.51%, effectively addressing challenges with unbalanced datasets. Moreover, to overcome bottlenecks in the existing network, the introduction of the Efficient network was adopted to enhance the performance of modeling optimized with attention mechanisms. Experimental results demonstrated that this integrated approach achieved an impressive overall accuracy of 99.70% and an average positive prediction rate of 99.44%, successfully addressing challenges in ECG signal identification, classification, and diagnosis

Cardiac Arrhythmia Monitoring and Severe Event Prediction System

Abnormalities of cardiac rhythms are correlated with significant morbidity. For example, atrial fibrillation, affecting at least 2.3 million people in the United States, is associated with an increased risk of both stroke and mortality; supra-ventricular tachycardia, detected in approximately 90,000 cases annually in the United States, ventricular arrhythmias cause 75% to 80% of the cases of sudden cardiac death; bradyarrhythmias may cause syncope, fatigue from chronotropic incompetence, or sudden death from asystole or ventricular tachycardia. Due to the time-sensitive nature of cardiac events, it is of utmost importance to ensure that medical intervention is provided in a timely manner, which could benefit greatly from a cardiac arrhythmia monitoring system that can detect and preferably also predict abnormal cardiac events. In recent years, with the development of medical monitoring devices, vast amounts of physiological signal data have been collected and become available for analysis. However, the extraction of the relevant information from physiological signals is hindered by the complexity within signal morphology, which leads to vague definitions and ambiguous guidelines, causing difficulties even for medical expert. To address the variability-related issues, most traditional methods depend heavily on pre-processing to identify specific morphology types and extract the related features. Despite many successes, one of the drawbacks of these methods is that they require signal data of high quality and tend to be less effective in the presence of noise. Although not an issue in almost noiseless situations, such pre-processing--based methods have become insufficient with the advent of portable arrhythmia monitoring devices in recent years capable of collecting physiological signals in real time, albeit with more noise. Therefore, to enable automated clinical decision, it is desirable to introduce new methods that require minimal pre-processing prior to analysis and are robust to noise. This thesis aims to develop a cardiac arrhythmia monitoring and prediction system applicable to portable arrhythmia monitoring devices. The analysis is based on a novel algorithm which does not rely on the detailed morphological information contained within each heartbeat, thus minimizing the impact of noise. Instead, the method works by analyzing the similarity and variability within strings of consecutive heartbeats, relying only on the broad morphology type of each heartbeat and utilizing the computer's ability to store and process a large number of heartbeats beyond humanly possible. The novel algorithm is based on deterministic probabilistic finite-state automata which have found great success in the field of natural language processing by studying the relationships among different words in a sentence rather than the detailed structure of the individual words. The proposed algorithm has been employed in experiments on both detection and prediction of various cardiac arrhythmia types and has achieved an AUC in the range of 0.70 to 0.95 for detection and prediction of different types of cardiac arrhythmias and cardiac events with data collected from publicly available databases, hospital bedside database and data collected from portable devices. Comparing with other well-established methods, the proposed algorithm has achieved equal or better classification results. In addition, the performance of the proposed algorithm is almost identical with or without any pre-processing on the data. The work in the thesis could be deployed as a cardiac arrhythmia monitoring and severe event prediction system which could alert patients and clinicians of an impending event, thereby enabling timely medical interventions.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169873/1/zcli_1.pd

Interventional Electrophysiology in Advanced Heart Disease Atrial Fibrillation and Heart Failure

The optimal therapy for atrial fibrillation (AF) associated with heart failure (HF) is unclear. Drug-based rhythm control has not proved clinically beneficial. Catheter ablation-based rhythm control improves cardiac function in HF patients, but impact on physiological performance has not been formally evaluated in a randomised trial. A randomised trial was designed and conducted, comparing catheter ablation with rate control in adults with symptomatic heart failure, radionuclide left ventricular ejection fraction (EF) ≤35%, and persistent AF. The primary outcome was change in peak oxygen consumption (VO2) at cardiopulmonary exercise test. Secondary endpoints included change in quality of life (Minnesota), 6-minute walk, BNP, and EF. Patients were followed-up for 12 months, and results analysed by intention-to-treat. 52 patients (63±9y, EF 24±8%, VO2 17.3±5.1ml/kg/min) were randomised, 26 to each arm. In the ablation arm, at 12 month follow up, 88% maintained SR, with a single procedure success of 69%. In the rate control arm, rate criteria were achieved in 96% at 12 months. At 12 months, peak VO2 had increased by 2.13 (95%CI -0.1 to 4.36) ml/kg/min in the ablation arm, compared with a decrease (-0.94ml/kg/min, 95%CI -2.21 to 0.32) under rate control: mean benefit of ablation +3.07ml/kg/min, 95% CI 0.56-5.59, p=0.018. The change appeared progressive, with a difference of only 0.79ml/kg/min at 3 months (95% CI -1.01 to 2.60, p=0.38). Compared with rate control, ablation reduced 12-month Minnesota score (p=0.019) and BNP (p=0.045), and showed trends toward increased 6 min walk distance (p=0.095) and EF (p=0.055). LA size fell significantly after ablation (p=0.001). Catheter ablation of persistent AF in patients with HF, with the ablation strategy achieving sinus rhythm in the majority, improves prognostically important objective cardiopulmonary exercise performance, symptoms and neurohormonal status. The effects are clear at 1 year but less distinct earlier, suggesting a period of cardiac remodelling and recovery

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Electronic devices and systems for monitoring of diabetes and cardiovascular diseases

Diabetes is a serious chronic disease which causes a high rate of morbidity and mortality all over the world. In 2007, more than 246 million people suffered from diabetes worldwide and unfortunately the incidence of diabetes is increasing at alarming rates. The number of people with diabetes is expected to double within the next 25 years due to a combination of population ageing, unhealthy diets, obesity and sedentary lifestyles. It can lead to blindness, heart disease, stroke, kidney failure, amputations and nerve damage. In women, diabetes can cause problems during pregnancy and make it more likely for the baby to be born with birth defects. Moreover, statistical analysis shows that 75% of diabetic patients die prematurely of cardiovascular disease (CVD). The absolute risk of cardiovascular disease in patients with type 1 (insulin-dependent) diabetes is lower than that in patients with type 2 (non-insulin-dependent) diabetes, in part because of their younger age and the lower prevalence of CVD risk factors, and in part because of the different pathophysiology of the two diseases. Unfortunately, about 9 out of 10 people with diabetes have type 2 diabetes. For these reasons, cardiopathes and diabetic patients need to be frequently monitored and in some cases they could easily perform at home the requested physiological measurements (i.e. glycemia, heart rate, blood pressure, body weight, and so on) sending the measured data to the care staff in the hospital. Several researches have been presented over the last years to address these issues by means of digital communication systems. The largest part of such works uses a PC or complex hardware/software systems for this purpose. Beyond the cost of such systems, it should be noted that they can be quite accessible by relatively young people but the same does not hold for elderly patients more accustomed to traditional equipments for personal entertainment such as TV sets. Wearable devices can permit continuous cardiovascular monitoring both in clinical settings and at home. Benefits may be realized in the diagnosis and treatment of a number of major 15 diseases. In conjunction with appropriate alarm algorithms, they can increase surveillance capabilities for CVD catastrophe for high-risk subjects. Moreover, they could play an important role in the wireless surveillance of people during hazardous operations (military, fire-fighting, etc.) or during sport activities. For patients with chronic cardiovascular disease, such as heart failure, home monitoring employing wearable device and tele-home care systems may detect exacerbations in very early stages or at dangerous levels that necessitate an emergency room visit and an immediate hospital admission. Taking into account mains principles for the design of good wearable devices and friendly tele-home care systems, such as safety, compactness, motion and other disturbance rejection, data storage and transmission, low power consumption, no direct doctor supervision, it is imperative that these systems are easy to use and comfortable to wear for long periods of time. The aim of this work is to develop an easy to use tele-home care system for diabetes and cardiovascular monitoring, well exploitable even by elderly people, which are the main target of a telemedicine system, and wearable devices for long term measuring of some parameters related to sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis. Since set-top boxes for Digital Video Broadcast Terrestrial (DVB-T) are in simple computers with their Operating System, a Java Virtual Machine, a modem for the uplink connection and a set of standard ports for the interfacing with external devices, elderly, diabetics and cardiopathes could easily send their self-made exam to the care staff placed elsewhere. The wearable devices developed are based on the well known photopletysmographic method which uses a led source/detector pair applied on the skin in order to obtain a biomedical signal related to the volume and percentage of oxygen in blood. Such devices investigate the possibility to obtain more information to those usually obtained by this technique (heart rate and percentage of oxygen saturation) in order to discover new algorithms for the continuous and remote or in ambulatory monitoring and screening of sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis