Exploring the Potential of Artificial Intelligence in Healthcare: ECG Reconstruction from Single Lead ECG Recordings and PPG Signals

Abstract

In the recent years we have seen an enormous increase in the utilisation of Artificial Intelligence (AI), and the healthcare sector has not been exempt from this transformation. Machine learning systems have demonstrated unmatched success in the healthcare sector thanks to recent developments in digitalization and the massive influx of biomedical data. This could be a game changer for the sector. The rise of cardiovascular diseases across the globe has made electrocardiograms (ECGs) a crucial modality for diagnosis, owing to their non-invasive nature and simplicity. However, gathering 12-lead ECG data is an arduous task outside clinical settings. Wearables can collect an ECG with fewer leads than the standard 12 or the Photoplethysmogram (PPG) data, but medical professionals and conventional ECG analysis software find this data challenging to interpret. To address this issue, ECG reconstruction has been proposed. The second chapter provides a summary of the current applications of AI in diagnosis, prognosis, and therapy, along with its implications for combating the COVID-19 pandemic. The chapter also identifies obstacles to AI’s widespread adoption in the healthcare sector and suggests remedies to help usher in a more intelligent medical future. A unique single-lead to multi-lead ECG reconstruction method is suggested in the third chapter of this thesis employing a modified Attention U-Net framework. Our model, which was solely trained on lead II of ECG, is capable of replicating the remaining 11 leads of the conventional 12-lead ECG with a Pearson correlation of 0.805, a mean square error of 0.0122, and an R-squared value of 0.639. Moreover, a single combined model is used to reconstruct all 11 leads simultaneously, improving performance and simultaneously reducing the computational resources needed for training compared to current literature in the field. The model’s ability for real-life use was also demonstrated by training a deep learning model for multi-disease classification using actual 12-lead ECG data and testing it on both original and reconstructed 12-lead ECG signals. Comparable classification accuracies for both original and reconstructed signals suggest that the proposed model can preserve diagnostically relevant artefacts. The fourth chapter proposes a new approach using a Wasserstein generative adversarial network to convert PPG signals into single lead ECG signals. Unlike previous studies, we utilize longer 3-second PPG segments to generate a complete 3-second ECG signal in a single step. We also address a significant issue in earlier studies where same patient’s data was used for both model training and testing. To address this limitation, patient-specific data segmentation for the train and test set was employed to obtain more reliable results. In summary, this thesis proposes solutions for multi-lead ECG reconstruction and PPG to ECG conversion, aiming to bridge the divide between easily collected heart monitoring data and easily interpretable heart monitoring data.