Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training - Yoshizawa, Rikuto; Yamamoto, Kohei; Ohtsuki, Tomoaki (IEEE International Conference on Communications (2003), 2023)
Blood pressure (BP) estimation methods using photoplethysmogram (PPG) based on deep learning models have been actively studied. These methods are also the basis of non-contact BP estimation methods using a camera or a Doppler radar. However, most previous studies are under data leakage, where subjects are not separated between training and test data. In this paper, we propose a method for BP estimation from PPG under the condition that subjects are separated between training and test data (inter-subject paradigm) using a subject-distinguishable large public dataset. Our BP estimation method estimates an 8-second BP waveform called arterial BP (ABP) from an 8-second PPG segment using U-Net. From the estimated ABP, systolic BP (SBP), diastolic BP (DBP), and mean BP (MBP) are calculated, which are the discrete single values for the 8-second ABP. In addition, we apply domain adversarial training, which facilitates the BP estimation model to extract subject-invariant features to improve the BP estimation accuracy under the inter-subject paradigm. Moreover, we use a considerably larger amount of training data than the previous studies and evaluate our model with smaller model sizes for better generalizability. Our experimental results showed that our method can estimate BP with moderate accuracy under the inter-subject paradigm, particularly MBP. The mean absolute errors for the estimated SBP, DBP, MBP, and ABP were 15.21, 7.12, 8.20, and 10.14 mmHg, respectively. The Pearson's correlation coefficients between the true and estimated values were 0.49, 0.39, 0.54, and 0.84, respectively.