Browse > Article
http://dx.doi.org/10.9723/jksiis.2020.25.3.001

Cuffless Blood Pressure Estimation Based on a Convolutional Neural Network using PPG and ECG Signals for Portable or Wearable Blood Pressure Devices  

Cho, Jinwoo (뷰노 연구원)
Choi, Ahyoung (가천대학교 소프트웨어학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.25, no.3, 2020 , pp. 1-10 More about this Journal
Abstract
In this paper, we propose an algorithm for estimating blood pressure using ECG (Electrocardiogram) and PPG (Photoplethysmography) signals. To estimate the BP (Blood pressure), we generate a periodic input signal, remove the noise according to the differential and threshold methods, and then estimate the systolic and diastolic blood pressures based on the convolutional neural network. We used 49 patient data of 3.1GB in the MIMIC database. As a result, it was found that the prediction error (RMSE) of systolic BP was 5.80mmHg, and the prediction error of diastolic BP was 2.78mmHg. This result confirms that the performance of class A is satisfied with the existing BP monitor evaluation method proposed by the British High Blood Pressure Association.
Keywords
Cuff-less blood pressure estimation; Convolutional neural network; ECG; PPG;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Allen, R., Schneider, J., Davidson, D., Winchester, M., and Taylor, C. (1981). The Covariation of Blood Pressure and Pulse Transit Time in Hypertensive Patients, Psychophysiology, 18(3), 301-306.   DOI
2 Babbs, C. (2012). Oscillometric Measurement of Systolic and Diastolic Blood Pressures Validated in a Physiologic Mathematical Model, BioMedical Engineering OnLine, 11(56), 1-22.   DOI
3 Baker, P. (1990). Neural Network P rocessing of Oscillometric Waveforms and Blood Pressure Estimation from the Superficial Temporal Artery, Ph. D. Thesis, Graduate School of University of Utah, USA.
4 Brien, E., Waeber, B., Parati, G., Staessen, J., and Myers, G. (2001). Blood Pressure Measuring Devices: Recommendations of the European Society of Hypertension, British Medical Journal, 322(7285), 531-536.   DOI
5 Cho, Y., Kim, M., and Yoon, J. (2014). Comparison of Characteristics of P-Wave Detection in ECG with Wireless Patch Electrodes, Korea Society of Industrial Information Systems, 19(1), 43-52.
6 Choi, S., Lee, K., Kim, K., and Kwak, S. (2019). Lane Departure Warning System using Deep Learning, Journal of the Korea Industrial Information Systems Research, 24(2), 25-31.   DOI
7 Chung, M., and Lee. J. (2018). Systemic Analysis of Research Activities and Trends Related to Artificial Intelligence (A.I.) Technology based on Latent Dirichlet Allocation (LDA) Model, Journal of the Korea Industrial Information Systems Research, 23(3), 87-95.   DOI
8 Choi, Y., Kim, K., Hong, W., and Ryu, J. (2009). Development of Blood Pressure Measuring System using Piezoelectric and Photo Sensor, Korea Society of Industrial Information Systems, 14(5), 149-154.
9 Ding, X., Zhao, N., Yang, G., Pettigrew, R., Lo, B., Miao, F., Li, Y., Liu, J., and Zhang, Y. (2016). Continuous Blood Pressure Measurement from Invasive to Unobtrusive: Celebration of 200th Birth Anniversary of Carl Ludwig, IEEE Journal of Biomedical and Health Informatics, 20(6), 1455-1465.   DOI
10 Ding, X., Yan, B., Zhang, Y., Liu, J., Zhao, N., and Tsang, H. (2017). Pulse Transit Time Based Continuous Cufess Blood Pressure Estimation: A New Extension and A Comprehensive Evaluation, Scientific Reports, 7(11554), 1-11.   DOI
11 Forouzanfar, M., Dajani, H., Groza, V., Bolic, M., and Rajan, S. (2011). Feature-based Neural Network Approach for Oscillometric Blood Pressure Estimation, IEEE Transactions on Instrumentation and Measurement, 60(8), 2786-2796.   DOI
12 Chen, W., Kobayashi, T., Ichikawa, S., Takeuchi, Y., and Togawa, T. (2000). Continuous Estimation of Systolic Blood Pressure using the Pulse Arrival Time and Intermittent Calibration, Medical and Biological Engineering and Computing, 38(5), 569-574.   DOI
13 Lee, H., Lee, J., and Shin, M. (2019). Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots, Electronics, 8(2), 192, 1-15.   DOI
14 Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P., Mark, R., Mietus, J., Moody, G., Peng, C., and Stanley, H. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), e215-e220.
15 Kachuee, M., Kiani, M., Mohammadzade, H., and Shabany, M. (2016). Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring, IEEE Transactions on Biomedical Engineering, 64(4), 859-869.   DOI
16 Kim, M., Kim, Y., and Cho, Y. (2015). Electrocardiographic Characteristics of Significant Factors of Detected Atrial Fibrillation using WEMS, Korea Society of Industrial Information Systems, 20(6), 37-46.
17 Lee, M., Nam, K., and Lee, C. (2019). Crack Detection on the Road in Aerial Image using Mask R-CNN, Journal of the Korea Industrial Information Systems Research, 24(3), 23-29.   DOI
18 Lee, S., and Chang, J. (2017). Oscillometric Blood Pressure Estimation Based on Deep Learning, IEEE Transactions on Industrial Informatics, 13(2), 461-472.   DOI
19 Moody, G., and Mark, R. (1996). A Database to Support Development and Evaluation of Intelligent Intensive Care Monitoring, Computers in Cardiology, 23, 657-660.
20 Naschitz, J., Bezobchuk, S., Mussafia-Priselac, R., Sundick, S., Dreyfuss, D., Khorshidi, I., Karidis, A., Manor, H., Nagar M., Peck, E., Peck, S., Storch, S., Rosner, I., and Gaitini, L. (2004). Pulse Transit Time by R-wave-gated Infrared photoplethysmography: Review of the Literature and Personal Experience, Journal of Clinical Monitoring and Computing. 8(5), 333-342.
21 Sun, J., Reisner, A., and Mark, R. (2006). A Signal Abnormality Index for Arterial Blood Pressure Waveforms, Proceedings on Computers in Cardiology, Valencia, Spain, pp. 13-16.
22 Ruiz-Rodriguez, J., Ruiz-Sanmartin, A., Ribas, A., Caballero, J., Garcia-Roche, A., Riera, J., Nuvials, X., Nadal, M., SolaMorales, O., and Serra, J. (2013). Innovative Continuous Non-invasive Cuffless bBood Pressure Monitoring based on Photoplethysmography Technology, Intensive Care Medician, 39(9), 1618-1625.   DOI
23 Rundo F., Conoci, S., Ortis A., and Battiato, S. (2018). An Advanced Bio-Inspired PhotoPlethysmoGraphy(PPG) and ECG Pattern Recognition System for Medical Assessment, Sensors, 18(2), 405, 1-22.   DOI
24 Sola, J., Proenca, M., Ferrario, D., Porchet, J., Falhi, A, Grossenbacher, O, Allemann, Y., Rimoldi, S., and Sartori, C. (2013). Noninvasive and Nonocclusive Blood Pressure Estimation Via a Chest Sensor, IEEE Transactions on Biomedical Engineering, 60(12), 3505-3513.   DOI
25 Teng, X., and Zhang, Y. (2013). Continuous and Noninvasive Estimation of Arterial Blood Pressure using a Photoplethysmographic Approach, The 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Sep. 17-21, Cancun, Mexico, pp. 3153-3156.
26 Wang, L., Zhou, W., Xing, Y., and Zhou, X. (2018). A Novel Neural Network Model for Blood Pressure Estimation using Photoplethesmography without Electrocardiogram, Journal of Healthcare Engineering, 2018(7804243), 1-9.
27 Xing, X., and Sun, M. (2016). Optical Blood Pressure Estimation with Photoplethysmography and FFT-based Neural Networks, Biomedical Optics Express, 7(8), 3007-3020.   DOI
28 Wippermann, C., Schranz, D., and Huth, R. (1995). Evaluation of the Pulse Wave Arrival Time as a Marker for Blood Pressure Changes in Critically Ill Infants and Children, Journal of Clinical Monitoring, 11(5), 324-328.   DOI
29 Payne, R., Symeonides, C., Webb, D., and Maxwell, S. (2006). Pulse Transit Time Measured from the ECG: an Unreliable Marker of Beat-to-beat Blood Pressure, Jpurnal of Applied Physiology, 100(1), 136-141.   DOI
30 Zhang, Q., Zeng, X., Hu, W., and Zhou, D. (2017). A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG, IEEE Access, 5, 10547-10561.   DOI