P2E-LGAN: PPG to ECG Reconstruction Methodology using LSTM based Generative Adversarial Network

Abstract

Cardiovascular diseases (CVDs) are the major cause of global morbidity and mortality. CVDs can be preliminarily diagnosed by analyzing a patient’s electrocardiogram (ECG), which requires long-term continuous ECG monitoring to suitably detect the onset of the disease. However, ECG data acquisition involves multiple lead attachments and requires regular intervention by an expert, thereby making the process cumbersome and inappropriate for continuous health monitoring due to limited portability and discomfort caused to the patients. Nowadays, automated ECG measurement techniques are gaining popularity in wearable health monitoring applications to seamlessly identify cardiac abnormalities even in a home environment. On the other hand, photoplethysmography (PPG) signals can be acquired from the wrist or fingertip of a patient by using a lead-less patch-less set-up that can be easily integrated with smart wearable devices. Therefore, to address the aforementioned demerits associated with ECG devices, a few researchers have fostered the idea of reconstructing ECG from photoplethysmogram (PPG) signals to generate simple yet effective CVD monitoring methodologies. Hence, in this paper, we propose P2E-LGAN, a hybrid generative adversarial network (GAN) based framework for generating ECG from PPG. The proposed network is evaluated on a benchmark database combined with ECG and PPG data. The inclusion of LSTM in the GAN network reduces the root mean square (RMSE), mean absolute error of heart rate (MAE(HR)) and percentage root mean square difference (PRD) by 35.7%, 37.2% and 9.8% respectively. Individual graphical analysis and performance evaluation of different metrics with state-of-the-art methods demonstrate the effectiveness of the proposed framework for reconstructing ECG from PPG.