A Robust PPG Onset and Systolic Peak Detection Algorithm Based On Hilbert Transform

Recent advances in cardiovascular diseases (CVDs) have inspired the modern population to adopt different easy-to-use personal health monitoring devices that employ easy-to-acquire biosignal such as Photoplethysmogram (PPG) for regular and long-term monitoring of vital signs. However, the detection of clinically significant PPG fiducial points such as onset and the systolic peak is still an open area of research. In the present work, a robust and simple PPG onset and systolic peak detection algorithm is proposed based on the Hilbert Transform. The methodology uses signal derivative, Hilbert Transform, amplitude thresholding and slope-reversal based approaches. In contrast to other techniques, the proposed algorithm exhibits high efficiency with average sensitivity and positive predictivity of 99.83% and 100% respectively, as tested with PPG records collected from the MIMIC database, institutional laboratory and hospital respectively.