Heart rate estimation based on joint convolutional neural network and conditional generative adversarial network via heart rate variabilities and other features extracted from photoplethysmograms

Accurate heart rate estimation is crucial for diagnosing and preventing cardiovascular diseases. Traditional methods rely on electrocardiograms (ECGs), which require attaching multiple electrodes to the body, making the process cumbersome and limiting its practicality. In contrast, photoplethysmograms (PPGs) offer a simpler and more convenient way to capture cardiovascular information, including heart rate. However, PPG-based heart rate measurements often differ from ECG-based ones due to timing differences in signal peaks and heart rate variability (HRV). To address these challenges, this paper proposes a novel approach combining Convolutional Neural Networks (CNNs) and Conditional Generative Adversarial Networks (CGANs) for heart rate estimation using HRV and other features extracted from PPGs. The CNN first estimates the heart rate, and the CGAN refines this estimation. The CGAN’s generator uses conditional information to produce more accurate heart rates, while the discriminator, equipped with residual blocks and a self-attention mechanism, classifies differences between actual and generated heart rates through a multi-layer convolutional network. This design mitigates gradient vanishing and enhances model stability, allowing the system to capture complex relationships between CNN-estimated heart rates and ECG-measured ones. To further improve accuracy, a perceptual loss function based on conditional information is used to minimize errors between estimated and actual heart rates. Simulation results show significant improvements in Pearson’s correlation coefficient (ρ), Frechet distance (FD), root mean square error (RMSE), and mean absolute distortion (MAD), demonstrating the method’s effectiveness and reliability. This approach has practical applications in wearable health devices, enabling continuous and non-invasive heart rate monitoring for early detection and management of cardiovascular conditions.