TY - GEN
T1 - Photoplethysmography-Based Blood Pressure Estimation Using Deep Learning
AU - Wang, Weinan
AU - Zhu, Li
AU - Marefat, Fatemeh
AU - Mohseni, Pedram
AU - Kilgore, Kevin
AU - Najafizadeh, Laleh
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Blood pressure (BP) measurement is an important measure of health status, yet simple and accurate measurement techniques have remained elusive. In this paper, we present a novel transfer learning-based blood pressure estimation algorithm that requires only few seconds of the photoplethysmography (PPG) signal as input. The proposed algorithm utilizes visibility graph to create images embedded with features related to the waveform morphology. The algorithm is evaluated using the data from the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) II database. Results show that the difference between the estimated and reference BP for the systolic BP (SBP) and for the diastolic BP (DBP) are -0.080 ± 10.097 mmHg and 0.057 ±4.814 mmHg, respectively, demonstrating the effectiveness of the proposed approach for estimating BP.
AB - Blood pressure (BP) measurement is an important measure of health status, yet simple and accurate measurement techniques have remained elusive. In this paper, we present a novel transfer learning-based blood pressure estimation algorithm that requires only few seconds of the photoplethysmography (PPG) signal as input. The proposed algorithm utilizes visibility graph to create images embedded with features related to the waveform morphology. The algorithm is evaluated using the data from the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) II database. Results show that the difference between the estimated and reference BP for the systolic BP (SBP) and for the diastolic BP (DBP) are -0.080 ± 10.097 mmHg and 0.057 ±4.814 mmHg, respectively, demonstrating the effectiveness of the proposed approach for estimating BP.
UR - http://www.scopus.com/inward/record.url?scp=85107769029&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107769029&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF51394.2020.9443447
DO - 10.1109/IEEECONF51394.2020.9443447
M3 - Conference contribution
AN - SCOPUS:85107769029
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 945
EP - 949
BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Y2 - 1 November 2020 through 5 November 2020
ER -