Adaptive Real-Time PPG Signal Qualification and Pulse Segmentation

Abstract

Ever since its invention in the 1930s, photoplethysmography (PPG) is a wide-spread technique used for health-monitoring. Via illumination of the human skin with a light source and capturing the light, an estimate of important physiological properties such as the heart rate can be made. This is commonly done with dedicated medical equipment, but studies from the last decade have shown that the smartphone could also be an adequate sensor, using the flash light and camera as transmitter and receiver respectively. Small, mobile and off-the-shelf, the smartphone provides many advantages over the conventional sensor. However, the quality of the PPG signals measured is a point of concern, as these could lead to incorrect estimates and several challenges need to be addressed. Firstly, user movement disturbs the contact area between the skin and sensor, introducing distortions in the PPG signal. Especially subtle motion artifacts like finger contact pressure impact the PPG signal quality. Secondly, the smartphone camera has a wide range of settings that can be used, but a proper analysis was lacking in literature. Thirdly, recent research has shown that PPG signals from an individual are unique. There is no common morphology. This means that algorithms developed for PPG need to account for unknown characteristics of PPG signals. Fourthly, analysis on PPG signals from the smartphone is mainly done offline and as such, a real-time implementation is desired. This thesis introduces a smartphone application that tackles these challenges and provides PPG signals of high quality. A real-time multi-stage PPG qualification pipeline combined with a pulse segmentation algorithm is proposed. Furthermore, analysis of the camera settings and finger detection algorithm resulted in PPG signals with 12.63% higher quality than a dedicated PPG sensor.