Oscillometric blood pressure estimation using machine learning-based mapping of waveform features

Ovbiagele B, et al. Level of systolic blood pressure within the normal range and risk of recurrent stroke. JAMA. 2011;306(19):2137–44.

Google Scholar 

Caillon A, et al. High systolic blood pressure at hospital admission is an important risk factor in models predicting outcome of COVID-19 patients. Am J Hypertens. 2021;34(3):282–90.

Google Scholar 

Garg S, et al. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019—COVID-NET, 14 States, March 1–30, 2020. Morb Mortal Wkly Rep. 2020;69(15):458.

Google Scholar 

Geddes L, Voelz M, Combs C, Reiner D, Babbs CF. Characterization of the oscillometric method for measuring indirect blood pressure. Ann Biomed Eng. 1982;10(6):271–80.

Google Scholar 

Celler BG, Basilakis J, Goozee K, Ambikairajah E. Non-invasive measurement of blood pressure-Why we should look at BP traces rather than listen to Korotkoff sounds. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2015. pp. 5964–5967.

Celler BG, Le P, Basilakis J, Ambikairajah E. Improving the quality and accuracy of non-invasive blood pressure measurement by visual inspection and automated signal processing of the Korotkoff sounds. Physiol Meas. 2017;38(6):1006.

Google Scholar 

Lee D, et al. Beat-to-beat continuous blood pressure estimation using bidirectional long short-term memory network. Sensors. 2020;21(1):96.

Google Scholar 

Mauck G, Smith C, Geddes L, Bourland J. The meaning of the point of maximum oscillations in cuff pressure in the indirect measurement of blood pressure—part ii. J Biomech Eng. 1980;102(1):28–33.

Google Scholar 

Van Montfrans GA. Oscillometric blood pressure measurement: progress and problems. Blood Press Monit. 2001;6(6):287–90.

Google Scholar 

Moraes JCTDB, Cerulli M, Ng P. A strategy for determination of systolic, mean and diastolic blood pressures from oscillometric pulse profiles. In: Computers in Cardiology 2000. Vol. 27 (Cat. 00CH37163), IEEE; 2000. pp. 211–214.

Henry B, Maxime M, Harry H, et al. Cuffless blood pressure in clinical practice: challenges. Oppor Curr Limits Blood Press. 2024;33(1):2304190.

Google Scholar 

Mahmud S, et al. A shallow U-net architecture for reliably predicting blood pressure (BP) from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. Sensors. 2022;22(3):919.

Google Scholar 

Chowdhury MH, et al. Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors. 2020;20(11):3127.

Google Scholar 

Forouzanfar M, Dajani HR, Groza VZ, Bolic M, Rajan S, Batkin I. Oscillometric blood pressure estimation: past, present, and future. IEEE Rev Biomed Eng. 2015;8:44–63.

Google Scholar 

Baker PD, Orr JA, Westenskow DR, Egbert TP. Method for determining blood pressure utilizing a neural network. Google Patents. 1994.

Narus S, Egbert T, Lee T-K, Lu J, Westenskow D. Non-invasive blood pressure monitoring from the supraorbital artery using an artificial neural network oscillometric algorithm. J Clin Monit. 1995;11(5):289–97.

Google Scholar 

Colak S, Isik C. Blood pressure estimation using neural networks. In: 2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. IEEE; 2004. pp. 21–25.

Forouzanfar M, Dajani H, Groza V, Bolic M, Rajan S. Comparison of feed-forward neural network training algorithms for oscillometric blood pressure estimation. In 4th International Workshop on Soft Computing Applications, 2010: IEEE, pp. 119–123.

Yan J, Cai X, Zhu G, Guo R, Yan H, Wang Y. A non-invasive blood pressure prediction method based on pulse wave feature fusion. Biomed Signal Process Control. 2022;74: 103523.

Google Scholar 

Pal R, Le J, Rudas A, Chiang JN, Williams T, Alexander B, Joosten A, Cannesson M. A review of machine learning methods for non-invasive blood pressure estimation. J Clin Monit Comput. 2024;39:1–12.

Google Scholar 

Lee S, Chang J-H. Deep Boltzmann regression with mimic features for oscillometric blood pressure estimation. IEEE Sens J. 2017;17(18):5982–93.

Google Scholar 

Argha A, Wu J, Su SW, Celler BG. Blood pressure estimation from beat-by-beat time-domain features of oscillometric waveforms using deep-neural-network classification models. IEEE Access. 2019;7:113427–39.

Google Scholar 

Xiang L, et al. The effect of different inflating volume on the measurement accuracy of the modified cuff pressure measurement method. J Clin Monit Comput. 2022;36(2):521–8.

Google Scholar 

Jazbinsek V, Luznik J, Mieke S, Trontelj Z. Influence of different presentations of oscillometric data on automatic determination of systolic and diastolic pressures. Ann Biomed Eng. 2010;38(3):774–87.

Google Scholar 

Lim PK, et al. Improved measurement of blood pressure by extraction of characteristic features from the cuff oscillometric waveform. Sensors. 2015;15(6):14142–61.

Google Scholar 

Forouzanfar M, Dajani HR, Groza VZ, Bolic M, Rajan S. Feature-based neural network approach for oscillometric blood pressure estimation. IEEE Trans Instrum Meas. 2011;60(8):2786–96.

Google Scholar 

El-Hajj C. Machine learning techniques for the prediction of systolic and diastolic blood pressure utilising the photoplethysmogram. London: University of London; 2022.

Google Scholar 

Baker S, Xiang W, Atkinson I. A computationally efficient CNN-LSTM neural network for estimation of blood pressure from features of electrocardiogram and photoplethysmogram waveforms. Knowl-Based Syst. 2022;250:109151.

Google Scholar 

Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif AA. A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods. Appl Acoust. 2020;164:107279.

Google Scholar 

Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif AA. Gaussian process regression (GPR) based non-invasive continuous blood pressure prediction method from cuff oscillometric signals. Appl Acoust. 2020;164:107256.

Google Scholar 

Lee S, Ahmad A, Jeon G. Combining bootstrap aggregation with support vector regression for small blood pressure measurement. J Med Syst. 2018;42(4):1–7.

Google Scholar 

Centracchio J, Davide CD, Paolo B, Emilio A. B3X: a novel efficient algorithm for accurate automated auscultatory blood pressure estimation. Physiol Meas. 2023;44(9):095007.

Google Scholar 

Kyung J, et al. Deep-learning-based blood pressure estimation using multi-channel photoplethysmogram and finger pressure with attention mechanism. Sci Rep. 2023;13:9311.

Google Scholar 

Tarifi B, Aaron F, Adam P, David RM. A machine learning approach to the non-invasive estimation of continuous blood pressure using photoplethysmography. Appl Sci. 2023;13(6):3955.

Google Scholar 

Argha A, Celler BG. Blood pressure estimation from time-domain features of oscillometric waveforms using long short-term memory recurrent neural networks. IEEE Trans Instrum Meas. 2019;69(6):3614–22.

Google Scholar 

Koohi I et al. Method for evaluation of trustworthiness of oscillometric blood pressure measurements. In: 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, IEEE; 2015. pp. 267–272.

Lee S, Rajan S, Dajani HR, Groza VZ, Bolic M. Determination of blood pressure using Bayesian approach. In: 2011 IEEE International Instrumentation and Measurement Technology Conference. IEEE; 2011. pp. 1–5.

Babbs CF. Oscillometric measurement of systolic and diastolic blood pressures validated in a physiologic mathematical model. Biomed Eng Online. 2012;11(1):1–22.

Google Scholar 

Celler BG, Le PN, Argha A, Ambikairajah E. GMM-HMM-based blood pressure estimation using time-domain features. IEEE Trans Instrum Meas. 2019;69(6):3631–41.

Google Scholar 

Shuzan MNI, et al. A novel non-invasive estimation of respiration rate from motion corrupted photoplethysmograph signal using machine learning model. IEEE Access. 2021;9:96775–90.

Google Scholar 

Chawla P, et al. A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features. Biomed Signal Process Control. 2023;1:104116.

Google Scholar 

Roffo G. Feature selection library (MATLAB toolbox). Preprint at arXiv:1607.01327 2016.

Tang J, Alelyani S, Liu H. Feature selection for classification: a review. Data classification: Algorithms and applications; 2014. p. 37.

Hashim BM, Amutha R. Machine learning-based human activity recognition using neighbourhood component analysis. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE; 2021. pp. 1080–1084.

Miao J, Niu L. A survey on feature selection. Procedia Comput Sci. 2016;91:919–26.

Google Scholar 

Fonti V, Belitser E. Feature selection using lasso. In: VU Amsterdam research paper in business analytics, vol. 30. 2017; pp. 1–25.

Rana S. Educational data mining. University of Technology. 2018.

Lee S, Park C-H, Chang J-H. Improved Gaussian mixture regression based on pseudo feature generation using bootstrap in blood pressure estimation. IEEE Trans Industr Inf. 2015;12(6):2269–80.

Google Scholar 

Lee S, et al. Oscillometric blood pressure estimation based on maximum amplitude algorithm employing Gaussian mixture regression. IEEE Trans Instrum Meas. 2013;62(12):3387–9.

Google Scholar 

O’brien E, Waeber B, Parati G, Staessen J, Myers MG. Blood pressure measuring devices: recommendations of the European Society of Hypertension. BMJ. 2001;322(7285):531–6.

Google Scholar 

Association for the Advancement of Medical Instrumentation. American national standards for electronic or automated sphygmomanometers. ANSI/AAMI SP 10-1987; 1987.

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