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Conference Paper
Deep Learning Model for Blood Pressure Estimation from PPG Signal
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- Authors
- Minseong Kim, Hyeonjeong Lee, Kwang-Yong Kim, Kyu-Hyung Kim
- Issue Date
- 2022-10
- Citation
- International Conference on Metrology for eXtended Reality, Artificial Intelligence, and Neural Engineering (MetroXRAINE) 2022, pp.1-5
- Language
- English
- Type
- Conference Paper
- Abstract
- In this study, we propose a deep learning-based framework to estimate blood pressure using photoplethysmogram (PPG) signals. We also propose a calibration method that applies the initial blood pressure information to the estimated results. To evaluate our approach, we used the PPG and blood pressure signals of 4200 patients sampled from the MIMIC-III Waveform Database. The resulting mean absolute error and standard deviation were 4.876mmHg and 5.257mmHg, respectively. Compared to the case of not calibrating using initial blood pressure information, we achieved the performance improvement of mean absolute error of 1.899mmHg and standard deviation of 2.933mmHg.
- KSP Keywords
- Blood pressure estimation, Calibration method, Learning model, Learning-based, Mean Absolute Error, PPG signal, Pressure signals, Standard deviation(STD), Waveform Database(WFDB), deep learning(DL), performance improvement