Health Data Science EPSRC CDT

房性早搏(PAC)是常见的心律失常，虽然通常被认为是良性的，但可以提示潜在的心血管疾病(CVD)，并可能导致严重的后果。目前，对其严重性和临床意义的了解有限。运动测试可以用来暴露不太常见的心律失常，如PAC，在标准的10秒静息心电测试中可能会遗漏这些心律失常。利用广泛的英国生物库(UKB)数据集[5]，我的目标是开发和评估一个机器学习模型，用于检测运动中的PAC，并调查运动诱导的PAC、主要心血管疾病和其他健康状况之间的关联。目前，还没有研究在像UKB运动队列这样的大型数据集中量化单个PAC节拍(N=95,071)。解决这一差距可以促进生物医学研究，研究PAC负担与心血管和整体健康的关系。这一知识对于改善临床实践中的早期诊断、风险分层和预防干预至关重要。本项目属于EPSRC医疗保健技术主题。主要目的是加强对PAC的检测和了解及其对心血管和整体健康的影响。我假设，使用机器学习模型可以准确地检测到运动诱导的PAC，并且这些PAC与主要的心血管疾病和其他健康状况显著相关。我提出了三个目标：1.开发和验证一个机器学习模型，以使用UKB数据集检测练习中的PAC。该模型将使用运动心电队列(112名参与者，79,113个每次心跳标签)的预先注释的子集进行训练和验证。使用卷积神经网络的初步结果表明，在PAC类上具有良好的性能，准确率为0.81，召回率为0.87，这将作为本研究的一部分进行微调。然后将在外部练习数据集上进行测试，以确保其健壮性和通用性，并作为开放源码工具提供给更广泛的研究社区。运动性PAC与主要心血管疾病的相关性研究。使用经过验证的PAC检测模型，将检查运动期间PAC发病率与英国Biobank队列中主要心血管疾病患病率的关系。这项研究将扩展Duijvenboden等人对室性早搏和心血管疾病的关联分析。这反映了拟议研究的可能力量。以无假设的方式探讨运动性PAC与其他各种健康状况之间的关系。将使用英国生物库数据集进行探索性分析，以确定运动中的PAC与广泛的健康状况之间的潜在关联。这项工作将遵循瓦茨等人的研究中所说明的无假设的形式。这项研究项目将产生一个开放源码的机器学习模型，专门设计用于在演习期间检测单个PAC，这是一种解决当前诊断能力重大差距的新方法。如果时间允许，该模型也可以被改装用于静息心电数据。此外，无假说地探索PAC与各种疾病的联系是一种创新策略，可能会揭示以前未知的健康影响。衍生的表型将与英国生物库社区共享，以促进对未被探索的心律失常的开放研究。通过加强对运动中PAC的检测和了解，这项工作将有助于改进心血管和其他疾病的早期诊断和预防策略。最终，这将导致更好的患者结果，并减少与未诊断或管理不善的心律失常及相关疾病相关的医疗负担。

Premature atrial contractions (PACs) are common cardiac arrhythmias which, although usually considered benign, can indicate underlying cardiovascular disease (CVD) and potentially lead to severe outcomes. Currently, there is limited understanding of their severity and clinical implications. Exercise testing can be used to expose less frequent arrhythmias, such as PACs, which might be missed during a standard 10-second resting ECG test. Leveraging the extensive UK Biobank (UKB) dataset [5], I aim to develop and evaluate a machine learning model for detecting PACs during exercise, and to investigate the associations between exercise-induced PACs, major CVDs, and other health conditions. Currently, there are no studies quantifying individual PAC beats in such a large dataset as the UKB exercise cohort (N=95,071). Addressing this gap can facilitate biomedical studies investigating the associations of PAC burdens with cardiovascular and overall health. This knowledge is crucial for improving early diagnosis, risk stratification, and preventive interventions in clinical practice.This project falls within the EPSRC Healthcare Technologies theme. The main aim is to enhance the detection and understanding of PACs and their implications for cardiovascular and overall health. I hypothesise that exercise-induced PACs can be accurately detected using machine learning models and that these PACs are significantly associated with major CVDs and other health conditions. I propose three objectives:1. Development and validation of a machine learning model to detect PACs during exercise using the UKB dataset. The model will be trained and validated using a pre-annotated subset of the exercise ECG cohort (112 participants, 79,113 per-heartbeat labels). Preliminary results using a convolutional neural network show promising performance on the PAC class with precision of 0.81 and recall of 0.87 which will be fine-tuned as part of this study. It will then be tested on an external exercise dataset to ensure its robustness and generalisability, and made available to the wider research community as an open-source tool.2. Investigation of the association between exercise-induced PACs and major CVDs. Using the validated PAC detection model, PAC incidence during exercise in relation to the prevalence of major CVDs within the UK Biobank cohort will be examined. The study will expand on the associative analysis of premature ventricular contractions and CVDs by Duijvenboden et al. which reflects the likely power of the proposed study.3. Exploration of the relationship between exercise-induced PACs and various other health conditions in a hypothesis-free manner. An exploratory analysis using the UK Biobank dataset will be carried out to identify potential associations between PACs during exercise and a wide range of health conditions. This work will follow the hypothesis-free format which is illustrated in the study by Watts et al. This research project will result in an open-source machine learning model specifically designed to detect individual PACs during exercise, a novel approach that addresses a significant gap in current diagnostic capabilities. If time permits, the model could also be adapted for use on resting ECG data. Additionally, the hypothesis-free exploration of PACs' associations with various diseases is an innovative strategy that may uncover previously unknown health implications. The derived phenotypes will be shared with the UK Biobank community in order to promote open research into underexplored arrhythmias. By enhancing the detection and understanding of PACs during exercise, this work will contribute to improved early diagnosis and preventive strategies for cardiovascular and other diseases. Ultimately, this will lead to better patient outcomes and reduced healthcare burdens associated with undiagnosed or poorly managed arrhythmias and related conditions.