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

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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