[2502.19167] Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study

Computer Science > Machine Learning arXiv:2502.19167 (cs) [Submitted on 26 Feb 2025 (v1), last revised 1 Mar 2026 (this version, v2)] Title:Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study Authors:Mohammad Moulaeifard, Peter H. Charlton, Nils Strodthoff View a PDF of the paper titled Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study, by Mohammad Moulaeifard and 2 other authors View PDF HTML (experimental) Abstract:Photoplethysmography (PPG)-based blood pressure (BP) estimation represents a promising alternative to cuff-based BP measurements. Recently, an increasing number of deep learning models have been proposed to infer BP from the raw PPG waveform. However, these models have been predominantly evaluated on in-distribution test sets, which immediately raises the question of the generalizability of these models to external datasets. To investigate this question, we trained five deep learning models on the recently released PulseDB dataset, provided in-distribution benchmarking results on this dataset, and then assessed out-of-distribution performance on several external datasets. The best model (XResNet1d101) achieved in-distribution MAEs of 9.4 and 6.0 mmHg for systolic and diastolic BP respectively on PulseDB (with subject-specific calibration), and 14.0 and 8.5 mmHg respectively without calibration. Equivalent MAEs on external test datasets without ca...