The use of wearable electrocardiogram sensors to improve the prediction of cardiovascular disease

NHS已将心血管疾病(CVD)确定为未来10年可挽救生命的最大单一领域1。预防心血管疾病的一项关键战略是利用风险预测模型针对高危人群进行预防性干预2。然而，许多人被识别得太晚了，超过50%的重大心脏事件发生在没有被归类为高风险3的患者中。这意味着，尽管潜在疾病经过多年的发展，但仍失去了预防的机会。可穿戴传感器，如心电贴片，有可能在患者的日常生活中连续、非侵入性和无痛地测量心血管疾病的危险因素。在使用这项技术的健康人群中，显然有必要更好地评估用于心血管风险预测的ECG。该项目旨在通过开发可重复的机器学习方法来弥合这一差距。使用英国生物库数据集，该数据集收集了96,000名参与者的临床心电图，他们的健康结果得到了纵向跟踪8。这还不包括对可穿戴式心电数据的独特访问，这些数据收集在约30,000名参与者中(n=已收集到约10,000名)。本项目将开发深度学习方法，为参与队列生成更新的风险评分。目的和目标：1)建立用于临床心电测量的深度学习模型，以预测未来的心血管疾病：研究将从学习与心血管疾病预测相关的心电特征的过程开始。在2009年至2010年间测量数据的79,209名参与者和2012年至20137年间的20,218名参与者中，使用4导联心电设备以500赫兹的频率记录数据。这些参与者中已经发生了5000多起事件CVD事件。候选方法包括在心电诊断研究中开发卷积神经网络。2)检查临床心电测量相对于当前临床标准的实用性。这将包括与QRISK3模型的比较，QRISK3模型在英格兰和华盛顿的日常临床实践中使用。这一目标将探索除了在深度学习环境中使用Qrisk3特征之外，将学习的心电特征与现有的Qrisk3模型相结合的可能性。在统计分析之后，这将允许在发展到可穿戴传感器时识别最优方法。3)研究添加可穿戴心电测量是否可以改善CVT的预测。他的目标是建立转移学习模型，将早期隐含层从临床训练模型转移。这是为了帮助从可穿戴心电数据预测事件心血管疾病。这将使用来自2万名英国生物库参与者的数据，这些参与者在2018年至2023年期间参加了一家成像评估诊所，并进行了为期两周的可穿戴式心电监测。由于该领域的大部分成功来自于使用多个心电导联的研究，该项目的目标是将这种成功复制到可穿戴设备上。这将通过转移学习来从AIMS 1和2.4中使用的多个临床心电导联中捕获关键特征来实现)探索潜在心血管疾病的检测机会，以及临床医生如何从时间序列心电数据中解释深入学习的特征深度学习模型，特别是时间序列数据，通常难以解释，这限制了它们最终临床应用的潜力。这一目标将调查检测心电数据中潜在异常的机会。目的是将这些信息输入到目标1中概述的风险预测指标中。检测此类情况将帮助临床医生确定风险的潜在原因，并可与一系列可解释的方法相结合。这些方法将有助于确定哪些心电特征导致了相关决定。该项目属于EPSRC医疗技术和人工智能(AI)主题，目的是应用最先进的深度学习方法来

The NHS has identified cardiovascular disease (CVD) as the single biggest area where lives can be saved over the next 10 years1. A key strategy in the prevention of CVD is the use of risk prediction models to target preventative interventions for people at higher risk2. However, many people are identified too late and over 50% of major cardiac events are in patients who were not classified as high-risk3. This means lost opportunities for prevention despite the underlying disease developing over many years. Wearable sensors, such as electrocardiogram (ECG) patches, have the potential to measure CVD risk factors continuously, noninvasively, and painlessly in patient's everyday lives4. There is a clear need to better evaluate ECGs for CVD risk prediction in a longitudinal cohort of healthy individuals using this technology.This project aims to bridge this gap via the development of reproducible machine learning methods. Using the UK Biobank dataset, which has collected in-clinic ECGs in 96,000 participants whose health outcomes are longitudinally followed-up8. This is in addition to unique access of wearable ECG data, collected in ~30,000 participants (n=~10,000 already collected). This project will develop deep learning methods to produce updated risk scores for the participating cohort.Aims and objectives:1) Establish a deep learning model for in-clinic ECG measurements to predict future CVD:The investigation will start with the process of learning ECG features relevant to the prediction of CVD. Data is recorded at 500Hz with a 4-lead ECG device in 79,209 participants who had data measured between 2009-2010, and 20,218 participants between 2012-20137. Over 5,000 incident CVD events have already occurred in these participants. Candidate methods include the development of a convolutional neural network in ECG diagnostic studies.2) Examine the utility of in-clinic ECG measurements over current in-clinic standards.This will include a comparison to the QRISK3 model which is used in day-to-day clinical practice in England and Wales10. This aim will explore the potential of combining learned ECG features with the existing Qrisk3 model, in addition to the use of Qrisk3 features in a deep learning setting. Following statistical analysis, this will allow for the optimal approach to be identified, when progressing to wearable sensors.3) Investigate if the addition of wearable ECG measurements can improve the prediction of CVDThis aim proposes the building of transfer learning models, with the early hidden layers transferred from the in-clinic trained model. This is to help predict incident CVD from wearable ECG data. This will utilise data from 20,000 UK Biobank participants, with wearable ECG monitoring for 2 weeks, who attended an imaging assessment clinic between 2018-2023. With much of the success in the field coming from studies using multiple ECG leads, this project aims to replicate this success to wearable devices. This will be achieved using transferring learning to capture the key features from the multiple in-clinic ECG leads used in aims 1 and 2.4) Explore detection opportunities for underlying cardiovascular conditions and how clinicians can interpret deep learned features from time-series ECG dataDeep learning models, particularly for time-series data, are often difficult to interpret, which limits their potential for eventual clinical use. This aim will investigate the opportunity to detect underlying abnormalities in the ECG data. With the goal of feeding these into the risk predictor outlined in aim 1. Detection of such conditions will aid clinicians in identifying the underlying cause of the risk and can be combined with a range of interpretable methods. Such methods will help to to identify which ECG features led to the relevant decision.This project falls within the EPSRC Healthcare technologies and Artificial intelligence (AI) themes, with the aim of applying state-of-the-art deep learning methods to advan