Practical optimization of deep learning models for cuffless blood pressure estimation from photoplethysmography
Vikas Khurana, Aejaz Mohammad Pamidi, Bharat Bhushan Joshi, Ganesh Belde, Rajesh Reddy Goutha, Ratan Bajpai, Subodha Kumar, Akram Khan
Summary
205,850 ten-second PPG–ABP segments from the MIMIC waveform database; multiresolution U-Net PPG→ABP. 62.5 Hz beats 125 Hz (SBP 5.24 vs 6.34; DBP 2.87 vs 3.27 mmHg MAE); 31.25 Hz fails (16.09 / 9.10). Averaging the top-4 peaks / bottom-4 troughs beats min–max extraction.
Key facts
- Form Factor
- clinical_finger
- License
- Not stated
Reported benchmarks
Cuffless blood pressure
mimic-waveform-205k · MAE (mmHg) ↓
Segment-level (authors: subject-level validation still needed); 125 Hz = 6.34/3.27
Updated: Oct 11, 2026
Related Research
- Cuffless blood pressure leaderboard
Benchmark results for cuffless blood pressure
- LIMA-Mamba: A lightweight and distribution-robust Mamba network for PPG-based biometrics
Research paper
- A Comparative Study of Signal Representations Methods and Deep Learning Architectures for PPG-Based Cuffless Blood Pressure Estimation
Seven 1D→2D transforms × several CNN variants on the UCI BP database. Best: CWT, errors reported as
- A Decodability-Grounded Audit of Clinical Prediction Claims in PPG Foundation Models
Audits frozen representations of four encoders on 37,784 MC-MED encounters via concept decodability,