Blood pressure estimation from two-channel BCG
Estimated blood pressure from two chair BCG channels using a CNN, BiGRU and temporal attention.
01 / RESEARCH DETAILTwo-channel BCG model and real-time service
- Challenge
- Temporal BCG information from the chair back and seat must be connected to real-time blood-pressure display.
- Approach
- Extracted local features from 10-second windows with a CNN, then used a BiGRU and temporal attention for regression.
- Contribution
- Co-developed synchronized acquisition, the blood-pressure model and the real-time analysis service.
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| Final code specification | Implementation |
|---|---|
| Input | 250 Hz, 2 channels, 10 seconds |
| CNN | 10 layers; 64 / 128 / 256 / 512 channels |
| Regression | BiGRU 64 + temporal attention |
Evaluation conditions and scopeFinal code: 10 convolutional layers, 64/128/256/512 channels and a 64-unit BiGRU. The presentation figure shows an earlier configuration.
02 / RESEARCH DETAILBlood-pressure tracking and reproduction conditions
- Experiment
- Recorded resting and post-exercise states for about three hours per participant, with cuff references every two minutes.
- Evaluation
- Evaluated SBP and DBP errors for six participants using within-person temporal splits of 29/14/57%.
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| Mean across 6 participants (mmHg) | SBP | DBP |
|---|---|---|
| MAE | 4.70 | 3.83 |
| RMSE | 6.02 | 4.90 |
Evaluation conditions and scopeResults from the 2021 paper: within-person evaluation with overlapping windows and normalization before splitting.