Blood Pressure Estimation Based on Blood Flow, ECG and Respiratory Signals Using Recurrent Neural Networks - Publikacja - MOST Wiedzy

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Blood Pressure Estimation Based on Blood Flow, ECG and Respiratory Signals Using Recurrent Neural Networks

Abstrakt

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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Informacje szczegółowe

Kategoria:
Aktywność konferencyjna
Typ:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Tytuł wydania:
2018 11th International Conference on Human System Interaction (HSI) strony 86 - 92
Język:
angielski
Rok wydania:
2018
Opis bibliograficzny:
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:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/hsi.2018.8430971
Weryfikacja:
Politechnika Gdańska

wyświetlono 16 razy

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