Privacy Preserving Heart Rate Estimation from ECG and PPG Signals for Application in Remote Healthcare

Abstract

In this work, we develop a computationally-light and robust neural network model for estimating heart rate for applications in remote healthcare systems. More specifically, we develop a model that can be trained on consumer-grade graphics processing units (GPUs) and can be deployed on edge devices for swift inference. To this end, we propose a hybrid model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architectures for estimating heart rate from ECG and PPG signals. Considering the sensitive nature of the ECG signals, we ensure a formal privacy guarantee – differential privacy, for the model training. We perform a tight accounting of the overall privacy budget of our training algorithm using the Rényi Differential Privacy technique. We demonstrate that our model outperforms state-of-the-art networks on a benchmark dataset for both ECG and PPG signals, despite having much smaller number of trainable parameters, and consequently much smaller training and inference times. Additionally, our CNN-BiLSTM architecture can provide excellent heart rate estimation performance even under strict privacy constraints. Last but not the least, we develop a prototype Arduino-based data collection system that is low-cost and efficient, and therefore, useful for providing access to modern healthcare services to the people living in remote areas.