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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2502.03731 |
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| _version_ | 1866909589595750400 |
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| author | Zhang, Yaowen Fresiello, Libera Veltink, Peter H. Donker, Dirk W. Wang, Ying |
| author_facet | Zhang, Yaowen Fresiello, Libera Veltink, Peter H. Donker, Dirk W. Wang, Ying |
| contents | Continuous blood pressure (BP) estimation via photoplethysmography (PPG) remains a significant challenge, particularly in providing comprehensive cardiovascular insights for hypertensive complications. This study presents a novel physiological model-based neural network (PMB-NN) framework for BP estimation from PPG signals, incorporating the identification of total peripheral resistance (TPR) and arterial compliance (AC) to enhance physiological interpretability. Preliminary experimental results, obtained from a single healthy participant under varying activity intensities, demonstrated promising accuracy, with a median standard deviation of 6.88 mmHg for systolic BP and 3.72 mmHg for diastolic BP. The median error for TPR and AC was 0.048 mmHg*s/ml and -0.521 ml/mmHg, respectively. Consistent with expectations, both estimated TPR and AC exhibited a reduction as activity intensity increased. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03731 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals Zhang, Yaowen Fresiello, Libera Veltink, Peter H. Donker, Dirk W. Wang, Ying Medical Physics Continuous blood pressure (BP) estimation via photoplethysmography (PPG) remains a significant challenge, particularly in providing comprehensive cardiovascular insights for hypertensive complications. This study presents a novel physiological model-based neural network (PMB-NN) framework for BP estimation from PPG signals, incorporating the identification of total peripheral resistance (TPR) and arterial compliance (AC) to enhance physiological interpretability. Preliminary experimental results, obtained from a single healthy participant under varying activity intensities, demonstrated promising accuracy, with a median standard deviation of 6.88 mmHg for systolic BP and 3.72 mmHg for diastolic BP. The median error for TPR and AC was 0.048 mmHg*s/ml and -0.521 ml/mmHg, respectively. Consistent with expectations, both estimated TPR and AC exhibited a reduction as activity intensity increased. |
| title | A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2502.03731 |