Generalization Capability of a Neural Network for Blood Pressure Estimation from Photoplethysmography

Photoplethysmography $(PPG)$ is one of the most promising alternatives for non-invasive and cuffless blood pressure $(BP)$ monitoring. In recent years, several machine learning approaches have been considered for this task: either feature engineering-based to map PPG-derived features into $BP$ values or feature learning-based with an automated feature extraction process. The generalization capability, namely the ability of a model to adapt to unseen data, is an important aspect of such data-driven models due to the heterogeneity of PPG waveforms. However, in published studies, this point is generally omitted. Therefore, we propose to assess the generalization capability of a feature learning model built to estimate $BP$ from $PPG$ signals by comparing the model accuracy (bias) and precision (standard deviation of the error) on two datasets with different recording protocols. On unseen subjects from the training dataset, the proposed model achieved mean and standard deviation errors of - $0.88\pm 10.29$ mmHg for systolic $BP(SBP)and-0.60\pm 5.76$ mmHg for diastolic $BP(DBP)$. Whereas, on the other dataset, the same metrics were $1.13\pm 12.86$ mmHg for $SBP$ and $-0.44\pm 6.99$ mmHg for $DBP$ Taken together, these results show that a feature learning model can extract feature representation that are generalizable over different populations and different $PPG$ sensors.