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...