Towards Machine Learning-based Mapping of * Low-cost BCG to ECG

Cardiovascular diseases (CVD) have emerged as the leading cause of death globally. High cost Electrocardiogram (ECG) equipment challenges CVD diagnosis in developing countries, especially in rural, underserved areas. This paper presents a novel approach for mapping patient-friendly Ballistocardiogram (BCG) waveforms to ECG waveforms needed for CVD diagnosis, using machine learning. This is inspired by the inherent corre-lation between the two waveforms originating from the same person's heart. More specifically, we propose a Deep Learning (DL) model composed of Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM) for BCG-to-ECG signal mapping. To train and test the proposed model, a bed-based ballistocardiography public dataset is employed. For performance evaluation, we employ the Pearson Correlation Coefficient (PCC) as a widely-accepted metric for quantitatively assessing signals' correlation. Our proposed approach is proven effective as evidenced by achieving a PCC of 0.97, on average, when correlating our deep learning-generated ECG waveform to the real, measured ECG waveform (groundtruth). BCG-to-ECG signal mapping opens an ample room for the integration of ECG's rich diagnostic knowledge and the BCG's continuous monitoring for early diagnosis of cardiovascular diseases, in a cost-effective manner.