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Blood Pressure Estimation Based on Blood Flow, ECG and Respiratory Signals Using Recurrent Neural Networks
Abstract
The estimation of systolic and diastolic blood pressure using artificial neural network is considered in the paper. The blood pressure values are estimated using pulse arrival time, and additionally RR intervals of ECG signal together with respiration signal. A single layer recurrent neural network with hyperbolic tangent activation function was used. The average blood pressure estimation error for the data obtained from 21 subjects from MIMIC database was equal to 2.490 mmHg with standard deviation equal to 1.063 mmHg for systolic blood pressure, and was equal to 1.330 mmHg with standard deviation equal to 0.627 mmHg for diastolic blood pressure using vanilla recurrent neural networks. Similar results were obtained for long short term memory cells. The simulation shows that taking into account pulse arrival time together with RR intervals and respiration signal gave better results than pulse arrival time alone
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2018 11th International Conference on Human System Interaction (HSI) strony 86 - 92
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Poliński A., Czuszyński K., Kocejko T.: Blood Pressure Estimation Based on Blood Flow, ECG and Respiratory Signals Using Recurrent Neural Networks// 2018 11th International Conference on Human System Interaction (HSI)/ : , 2018, s.86-92
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/hsi.2018.8430971
- Verified by:
- Gdańsk University of Technology
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