Reliable Multimodal Heartbeat Classification using Deep Neural Networks

Copyright © 2023 Authors. Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). Heartbeat detection has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate heartbeat classification. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for heartbeat classification, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. Moreover, while many researchers have successfully created methodologies to accurately classify heartbeats including paced beats, none were able to distinguish various sub-classes of paced heartbeats. A more comprehensive distinction is crucial as it not only aids in the identification of pacing settings but also facilitates the detection of inadequate pacing settings, a critical aspect in patient care. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification and for comprehensive paced heartbeats classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on 5 different arrhythmia classes, whereas ResNet34 achieved an accuracy of 93.82% on 12 paced classes. The significant efficiency of utilizing ABP and CVP signals independently for classification, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. For classifying 12 different paced heartbeats, ResNet34 achieved 74.04% accuracy with ABP signals and 74.38% with CVP signals. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.British Heart Foundation for sponsoring this project (No.FS/19/73/34690

The Role of Modern-Era Echocardiography in Identification of Cardiac Risk Factors for Infective Endocarditis

This chapter provides an updated overview of the scientific literature on cardiac pathology predisposing to infective endocarditis and the estimated risk associated with selected lesion-specific abnormalities, in an era of changing epidemiology and advanced echocardiographic imaging. Importantly, with the evolution of modern-era echo, subtle changes in valve structure and function are now easily detectable and a proportion of cases of apparently ‘normal’ valves involved with IE, may in fact have subtle pre-existing pathological and/or haemodynamic abnormalities. The chapter will have a clinical focus with an aim to provide the Physician with up-to-date and practical information on cardiac risk factor identification for infective endocarditis

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Innovations and mechanisms in pacing therapy for heart failure

Despite pharmacological advances, heart failure remains a major cause of mortality and morbidity. Pacing therapy for heart failure was achieved in the 1990s with the advent of biventricular pacing (BVP). BVP shortens ventricular activation time and has thus been referred to as ‘cardiac resynchronization therapy’ (CRT). However BVP has other effects including shortening of atrioventricular delay: the contributions of its effects to its overall benefit have yet to be elucidated. Ventricular activation is not normalised by BVP, indicating scope for more effective resynchronization. This thesis explores mechanisms and innovations in pacing therapy for heart failure through measurement of haemodynamic and electrical parameters with high precision and resolution during BVP, right ventricular pacing (RVP) and His bundle pacing (HBP), where the His-Purkinje conduction system is directly stimulated. HBP offers both an innovation in pacing and a model to study conventional pacing. HBP can deliver physiological CRT by overcoming left bundle branch block (LBBB) to normalise QRS appearances but its performance relative to BVP is not known. When performed proximally, or using lower energy, HBP can preserve intrinsic LBBB. In Chapter 3, the electro-mechanical effects of conventional BVP are compared with LBBB correction by HBP. Chapter 4 uses non-invasive electrical mapping to identify mechanisms and predictors of LBBB correction by HBP, comparing it with narrow QRS. Capture of the His bundle can be alone (selective HBP) or alongside myocardial capture (non-selective): the effect of this on HBP is studied in Chapter 5. In Chapter 6, the haemodynamic effects of proximal/low-energy HBP, where LBBB is preserved but atrioventricular timing can be optimised, is compared to BVP and RVP to measure the contribution of atrioventricular delay shortening to the overall benefit of BVP. By evaluating innovative therapies and improving our understanding of existing therapies, hopefully this thesis will advance pacing therapy for heart failure.Open Acces

Wearable technology and the cardiovascular system: the future of patient assessment

The past decade has seen a dramatic rise in consumer technologies able to monitor a variety of cardiovascular parameters. Such devices initially recorded markers of exercise, but now include physiological and health-care focused measurements. The public are keen to adopt these devices in the belief that they are useful to identify and monitor cardiovascular disease. Clinicians are therefore often presented with health app data accompanied by a diverse range of concerns and queries. Herein, we assess whether these devices are accurate, their outputs validated, and whether they are suitable for professionals to make management decisions. We review underpinning methods and technologies and explore the evidence supporting the use of these devices as diagnostic and monitoring tools in hypertension, arrhythmia, heart failure, coronary artery disease, pulmonary hypertension, and valvular heart disease. Used correctly, they might improve health care and support research

Multimodal Arrhythmia Classification Using Deep Neural Networks

Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). The detection of arrhythmias has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate arrhythmia detection. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for arrhythmia detection, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on five different arrhythmia classes. The significant efficiency of utilizing ABP and CVP signals independently for the classification of arrhythmias, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.British Heart Foundation for sponsoring this project (No.FS/19/73/34690)

Skeletal muscle, exercise and activity in pulmonary hypertension

MD ThesisPulmonary Arterial hypertension (PAH) is a rare and progressive condition presenting with exercise intolerance, leading to right ventricle (RV) failure and death. There has been significant progress in understanding the basic pathophysiology leading to the development of a number of targeted therapies, resulting in improved prognosis. Despite this, patients remain limited in performing exertional activities with a poorer quality of life. Recent research has focused on PAH being a multi-systemic disease with skeletal muscle dysfunction contributing to exercise intolerance. There needs to be greater understanding of the physiological and behavioural mechanisms that limit daily functional capabilities in PAH patients. The aims of the thesis were to study the role of skeletal muscle mitochondrial function, the limitations in central and peripheral haemodynamics on maximum exercise, and develop a greater understanding of whether habitual daily physical activity levels are improved by current pharmaceutical treatments. Using 31Phosphorous-magnetic resonance spectroscopy (31P-MRS), oxygen delivery as opposed to impaired mitochondrial function would explain the abnormal skeletal muscle bioenergetics observed. This is further supported by analysing skeletal muscle biopsy samples demonstrating that mitochondrial protein expression and function was normal, therefore not contributing to impaired exercise capacity. Using continuous non-invasive cardiac output, chronotropic incompetence and reduced peripheral oxygen extraction are the predominant mechanisms leading to impaired peak oxygen consumption. Finally, in a pilot study targeted-therapies failed to change habitual daily physical activity and fatigue levels in PAH patients despite a significant observed change in submaximal exercise capacity. In conclusion, a number of physiological mechanisms that impair exercise capacity and habitual physical activity in PAH are beyond the currently available targeted therapies. Further research is needed into how best to improve exercise capacity, fatigue and activity levels that will directly lead to improvement in quality of life for PAH patients

Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis

An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks