Clinical Significance of Premature Atrial Contractions: Insights from Automated ECG Detection

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.