Computationally Efficient Estimation of Heart Rate from PPG Signals

Abstract

Heart rate (HR) monitoring using photoplethysmographic (PPG) signals is becoming increasingly popular due to the simplicity of device design and the low cost of wearable technology. Measuring HR from PPG signal using battery-powered wearable devices with limited resources requires computationally efficient
algorithms. This paper presents a simple yet accurate heart rate estimation method based on a systolic peak detection algorithm that employs a fractional-order calculus-based filter. The proposed method was validated on the CapnoBase benchmark dataset, demonstrating an overall Average Absolute Error (AAE) of 1.78. These results are comparable to those achieved with state-of-the-art methods, while the proposed method offers the advantage of simpler implementation.