Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study
This addresses the need for realistic medical time series data to improve cardiac disease pre-screening and patient privacy, but it is incremental as it applies existing methods to a specific domain.
The study tackled generating synthetic phonocardiogram (PCG) signals for healthcare by comparing three generative models (WaveNet, DoppelGANger, DiffWave) on a PhysioNet dataset, showing that the generated data closely resembles the original with metrics like mean absolute error and maximum mean discrepancy.
The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signals for this specific application received limited attention in research. In this study, we employ and compare three state-of-the-art generative models from different categories - WaveNet, DoppelGANger, and DiffWave - to generate high-quality PCG data. We use data from the George B. Moody PhysioNet Challenge 2022. Our methods are evaluated using various metrics widely used in the previous literature in the domain of time series data generation, such as mean absolute error and maximum mean discrepancy. Our results demonstrate that the generated PCG data closely resembles the original datasets, indicating the effectiveness of our generative models in producing realistic synthetic PCG data. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs, in order to address the current scarcity of abnormal data. We hope to improve the robustness and accuracy of diagnostic tools in cardiology, enhancing their effectiveness in detecting heart murmurs.