A Simple and Accurate PPG Systolic Peak Detector Based on Fractional Order Calculus

Accurate detection of systolic peaks in photoplethysmogram (PPG) signals using simple algorithms is crucial for many healthcare monitoring systems, especially in wearable devices. This paper presents a simple yet accurate systolic peak detector that employs a fractional-order calculus-based filter, performing both noise removal and inflection/extrema point detection in a single stage. The proposed filter can detect inflection and extrema points even in highly noisy PPG signals. By correctly detecting systolic peaks, other fiducial points can be easily identified and subsequent accurate measurements such as heart rate can be obtained. The proposed method was validated on the CapnoBase benchmark dataset, demonstrating an overall accuracy sensitivity, precision and F1-Score of 99.62%, 99.89%, 99.52%, 99.70% respectively for a tolerance interval of 30 ms. These results are comparable to those achieved with state-of-the-art methods, while the proposed method offers the advantage of simpler implementation.