Cuffless Beat-to-Beat Blood Pressure Estimation from Photoplethysmogram Signals

In recent years, many studies have been published on blood pressure (BP) estimation from photoplethysmogram (PPG) signals to overcome limitations of current BP devices, such as high risk of complications in invasive methods or limited accuracy, temporal resolution and comfort for cuff-based systems. However, most of these studies suffer from methodological drawbacks regarding data handling, leading to overly positive evaluation of such methods. We present an approach on beat-to-beat blood pressure estimation from raw PPG signals that is evaluated extensively on data of unseen test subjects. The proposed method obtains an mean absolute error ± standard deviation of 8.07 ± 6.86 mmHg for DBP and 8.73 ± 7.36 mmHg for SBP. To increase transparency of the model's decision-making, we examine layer activation of the employed convolutional neural network. Moreover, we analyze the impact of fine tuning for personalization of the model and derive strategies to enhance the personalization process.