TY - GEN
T1 - Significant Improvement in Precision of Real-Time Blood Pressure Prediction Based on Complete Cycles of Measured PPGs
AU - Nguyen, Duc Huy
AU - Chao, Paul C.P.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - A novel approach is presented for accurate and continuous monitoring of blood pressure (BP) using photoplethysmography (PPG) signals. The limitations of previous methodologies in accurately distinguishing between qualified and unqualified PPG waveforms, particularly in terms of complete cycles, have undermined the accuracy of BP estimations. To address this, a two-stage deep learning model combining 1D-CNN and LSTM for PPG quality assessment and 1D-CNN and GRU for BP estimation is proposed. Experimental results show that the 1D-CNN model achieves a high classification accuracy of 98.39% for PPG signal quality assessment. Without PPG quality assessment, the mean error (ME) ± standard deviation (SD) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) is 1.31pm 9.84 mmHg and 0.19pm 5.75 mmHg, respectively. However, with the implementation of PPG quality assessment, the ME pm SD for SBP is reduced to 0.47pm 6.23 mmHg, and for DBP to 0.23pm 3.93 mmHg, respectively. These results highlight the effectiveness of the proposed method, providing a promising strategy for accurate, real-time, and continuous blood pressure monitoring based on complete cycles of measured PPGs.
AB - A novel approach is presented for accurate and continuous monitoring of blood pressure (BP) using photoplethysmography (PPG) signals. The limitations of previous methodologies in accurately distinguishing between qualified and unqualified PPG waveforms, particularly in terms of complete cycles, have undermined the accuracy of BP estimations. To address this, a two-stage deep learning model combining 1D-CNN and LSTM for PPG quality assessment and 1D-CNN and GRU for BP estimation is proposed. Experimental results show that the 1D-CNN model achieves a high classification accuracy of 98.39% for PPG signal quality assessment. Without PPG quality assessment, the mean error (ME) ± standard deviation (SD) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) is 1.31pm 9.84 mmHg and 0.19pm 5.75 mmHg, respectively. However, with the implementation of PPG quality assessment, the ME pm SD for SBP is reduced to 0.47pm 6.23 mmHg, and for DBP to 0.23pm 3.93 mmHg, respectively. These results highlight the effectiveness of the proposed method, providing a promising strategy for accurate, real-time, and continuous blood pressure monitoring based on complete cycles of measured PPGs.
KW - 1-dimensional convolutional neural network (1D-CNN)
KW - Blood pressure (BP)
KW - complete cycles
KW - gated recurrent unit (GRU)
KW - long short-term memory (LSTM)
KW - Photoplethysmography (PPG)
KW - PPG quality assessment
KW - real-time
UR - http://www.scopus.com/inward/record.url?scp=85179762510&partnerID=8YFLogxK
U2 - 10.1109/SENSORS56945.2023.10324952
DO - 10.1109/SENSORS56945.2023.10324952
M3 - Conference contribution
AN - SCOPUS:85179762510
T3 - Proceedings of IEEE Sensors
BT - 2023 IEEE SENSORS, SENSORS 2023 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE SENSORS, SENSORS 2023
Y2 - 29 October 2023 through 1 November 2023
ER -