Continuous cuffless and non-invasive measurement of arterial blood pressure—concepts and future perspectives

References

• Williams B, Mancia G, Spiering W, ESC Scientific Document Group, et al. ESC/ESH guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021–3104.
   PubMed Web of Science ®Google Scholar
• Whelton PK, Carey RM, Aronow WS, et al. ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults a report of the American College of Cardiology/American Heart Association Task Force on Clinical practice guidelines. Hypertension 2017;71:E13–115. https://doi.org/10.1161/HYP.0000000000000065.
   Google Scholar
• Agarwal R, Light RP. The effect of measuring ambulatory blood pressure on nighttime sleep and daytime activity - Implications for dipping. CJASN. 2010;5(2):281–285.
   PubMed Web of Science ®Google Scholar
• Sherwood A, Hill LK, Blumenthal JA, et al. The effects of ambulatory blood pressure monitoring on sleep quality in men and women with hypertension: Dipper vs. Nondipper and race differences. Am J Hypertens. 2019;32(1):54–60.
   PubMed Web of Science ®Google Scholar
• Davies RJO, Jenkins NE, Stradling JR. Effect of measuring ambulatory blood pressure on sleep and on blood pressure during sleep. BMJ. 1994;308(6932):820–823.
   PubMed Web of Science ®Google Scholar
• Mancia G, Sega R, Bravi C, et al. Ambulatory blood pressure normality: Results from the PAMELA study. J Hypertens. 1995;13(12):1377–1390.
   PubMed Web of Science ®Google Scholar
• Fagard RH, Celis H, Thijs L, et al. Daytime and nighttime blood pressure as predictors of death and cause-specific cardiovascular events in hypertension. Hypertension. 2008;51(1):55–61.
   PubMed Web of Science ®Google Scholar
• Sega R, Facchetti R, Bombelli M, et al. Prognostic value of ambulatory and home blood pressures compared with office blood pressure in the general population: Follow-up results from the pressioni arteriose monitorate e loro associazioni (PAMELA) study. Circulation. 2005;111(14):1777–1783.
   PubMed Web of Science ®Google Scholar
• Gavriilaki M, Anyfanti P, Nikolaidou B, et al. Nighttime dipping status and risk of cardiovascular events in patients with untreated hypertension: a systematic review and Meta‐analysis. J Clin Hypertens (Greenwich). 2020;22(11):1951–1959.
   PubMed Web of Science ®Google Scholar
• Raymaekers V, Brenard C, Hermans L, et al. How to reliably diagnose arterial hypertension: lessons from 24 h blood pressure monitoring. Blood Press. 2019;28(2):93–98.
   PubMed Web of Science ®Google Scholar
• Stevens SL, Wood S, Koshiaris C, et al. Blood pressure variability and cardiovascular disease: Systematic review and Meta-analysis. BMJ. 2016;354:i4098.
   PubMed Web of Science ®Google Scholar
• Parati G, Stergiou GS, Dolan E, et al. Blood pressure variability: clinical relevance and application. J Clin Hypertens. 2018;20(7):1133–1137.
   Web of Science ®Google Scholar
• Stergiou GS, Mukkamala R, Avolio A, et al. Cuffless blood pressure measuring devices: review and statement by the european society of hypertension working group on blood pressure monitoring and cardiovascular variability. J Hypertens. 2022;40(8):1449–1460.
   PubMed Web of Science ®Google Scholar
• Stergiou GS, Palatini P, Parati G, European Society of Hypertension Council and the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability, et al. 2021 European society of hypertension practice guidelines for office and out-of-office blood pressure measurement. J Hypertens. 2021;39(7):1293–1302.
   PubMed Web of Science ®Google Scholar
• Callaghan FJ, Babbs CF, Bourland JD, et al. The relationship between arterial pulse-wave velocity and pulse frequency at different pressures. J Med Eng Technol. 1984;8(1):15–18.
   PubMed Web of Science ®Google Scholar
• Gesche H, Grosskurth D, Küchler G, et al. Continuous blood pressure measurement by using the pulse transit time: Comparison to a cuff-based method. Eur J Appl Physiol. 2012;112(1):309–315.
   PubMed Web of Science ®Google Scholar
• Wang R, Jia W, Mao ZH, et al. Cuff-free blood pressure estimation using pulse transit time and heart rate. International Conference on Signal Processing Proceedings, ICSP, 2015 January, Institute of Electrical and Electronics Engineers Inc.; 2014. p. 115–8. https://doi.org/10.1109/ICOSP.2014.7014980.
   Google Scholar
• Chen Y, Shi S, Liu YK, et al. Cuffless blood-pressure estimation method using a heart-rate variability-derived parameter. Physiol. Meas. 2018;39(9):095002.
   PubMed Web of Science ®Google Scholar
• Zheng YL, Yan BP, Zhang YT, et al. An armband wearable device for overnight and cuff-less blood pressure measurement. IEEE Trans Biomed Eng. 2014;61(7):2179–2186.
   PubMed Web of Science ®Google Scholar
• Block RC, Yavarimanesh M, Natarajan K, et al. Conventional pulse transit times as markers of blood pressure changes in humans. Sci Rep. 2020;10(1):16373.
   PubMed Web of Science ®Google Scholar
• Newlin DB. Relationships ol pulse transmission times to pre‐ejection period and blood pressure. Psychophysiology. 1981;18(3):316–321.
   PubMed Web of Science ®Google Scholar
• Beutel F, van Hoof C, Rottenberg X, et al. Pulse arrival time segmentation into cardiac and vascular Intervals - Implications for pulse wave velocity and blood pressure estimation. IEEE Trans Biomed Eng. 2021;68(9):2810–2820.
   PubMed Web of Science ®Google Scholar
• Yousefian P, Shin S, Mousavi A, et al. The potential of wearable limb ballistocardiogram in blood pressure monitoring via pulse transit time. Sci Rep. 2019;9(1):10666.
   PubMedGoogle Scholar
• Wang Y, Liu Z, Ma S. Cuff-less blood pressure measurement from dual-channel photoplethysmographic signals via peripheral pulse transit time with singular spectrum analysis. Physiol Meas. 2018;39(2):025010.
   PubMed Web of Science ®Google Scholar
• Chan G, Cooper R, Hosanee M, et al. Multi-Site photoplethysmography technology for blood pressure assessment: Challenges and recommendations. J Clin Med. 2019;8:1827.
   Web of Science ®Google Scholar
• DeMarzo AP. Clinical use of impedance cardiography for hemodynamic assessment of early cardiovascular disease and management of hypertension. High Blood Press Cardiovasc Prev. 2020;27(3):203–213.
   PubMed Web of Science ®Google Scholar
• Zhang G, Cottrell AC, Henry IC, et al. Assessment of pre-ejection period in ambulatory subjects using seismocardiogram in a wearable blood pressure monitor. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016 October, Institute of Electrical and Electronics Engineers Inc.; 2016. p. 3386–9. https://doi.org/10.1109/EMBC.2016.7591454.
   Google Scholar
• Muehlsteff J, Aubert XL, Schuett M. Cuffless estimation of systolic blood pressure for short effort bicycle tests: the prominent role of the pre-ejection period. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2006. p. 5088–92. https://doi.org/10.1109/IEMBS.2006.260275.
   Google Scholar
• Zhang Q, Zhou D, Zeng X. Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed Eng OnLine. 2017;16(1):23.
   Google Scholar
• Scalise F, Margonato D, Sole A, et al. Ambulatory blood pressure monitoring by a novel cuffless device: a pilot study. Blood Press. 2020;29(6):375–381.
   PubMed Web of Science ®Google Scholar
• Ding XR, Zhang YT, Liu J, et al. Continuous cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio. IEEE Trans Biomed Eng. 2016;63(5):964–972.
   PubMed Web of Science ®Google Scholar
• Ding X, Yan BP, Zhang YT, et al. Pulse transit time based continuous cuffless blood pressure estimation: a new extension and A comprehensive evaluation. Sci Rep. 2017;7(1):11554.
   Web of Science ®Google Scholar
• Lin WH, Wang H, Samuel OW, et al. Using a new PPG indicator to increase the accuracy of PTT-based continuous cuffless blood pressure estimation. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, Institute of Electrical and Electronics Engineers Inc.; 2017. p. 738–41. https://doi.org/10.1109/EMBC.2017.8036930.
   Google Scholar
• Ori Z, Monir G, Weiss J, et al. Heart rate variability: Frequency domain analysis. Cardiol Clin. 1992;10(3):499–537.
   PubMedGoogle Scholar
• Akselrod S, Gordon D, Ubel FA, et al. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science. 1981;213(4504):220–222.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Zhou B, Qiu J, et al. Heart rate variability changes in patients with panic disorder. J Affect Disord. 2020;267:297–306.
   PubMed Web of Science ®Google Scholar
• Bilo G, Zorzi C, Ochoa Munera JE, et al. Validation of the Somnotouch-NIBP noninvasive continuous blood pressure monitor according to the european society of hypertension international protocol revision 2010. Blood Press Monit. 2015;20(5):291–294.
   PubMed Web of Science ®Google Scholar
• Socrates T, Krisai P, Vischer AS, et al. Improved agreement and diagnostic accuracy of a cuffless 24-h blood pressure measurement device in clinical practice. Sci Rep. 2021;11(1):1143.
   PubMed Web of Science ®Google Scholar
• Moharram MA, Wilson LC, Williams MJ, et al. Beat-to-beat blood pressure measurement using a cuffless device does not accurately reflect invasive blood pressure. Int J Cardiol Hypertens. 2020;5:100030.
   PubMedGoogle Scholar
• Patzak A. Measuring blood pressure by a cuffless device using the pulse transit time. Int J Cardiol Hypertens. 2021;8:100072.
   PubMedGoogle Scholar
• Bothe TL, Patzak A, Pilz N. The B-Score is a novel metric for measuring the true performance of blood pressure estimation models. Sci Rep. 2022;12(1):12173.
   PubMed Web of Science ®Google Scholar
• Chen SC, Huang JC, Su HM, et al. Prognostic cardiovascular markers in chronic kidney disease. Kidney Blood Press Res. 2018;43(4):1388–1407.
   PubMed Web of Science ®Google Scholar
• Ulusoy RE, Demiralp E, Kirilmaz A, et al. Aortic elastic properties in young pregnant women. Heart Vessels. 2006;21(1):38–41.
   PubMed Web of Science ®Google Scholar
• Batzias K, Antonopoulos AS, Oikonomou E, et al. Effects of newer antidiabetic drugs on endothelial function and arterial stiffness: a systematic review and meta-analysis. Journal of Diabetes Research. 2018;2018:1–10.
   Web of Science ®Google Scholar
• Burnier M, Kjeldsen SE, Narkiewicz K, et al. Cuff-less measurements of blood pressure: are we ready for a change? Blood Press. 2021;30(4):205–207.
   PubMed Web of Science ®Google Scholar
• Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–1930.
   PubMed Web of Science ®Google Scholar
• Goecks J, Jalili V, Heiser LM, et al. How machine learning will transform biomedicine. Cell. 2020;181(1):92–101.
   PubMed Web of Science ®Google Scholar
• Fernández-Delgado M, Sirsat MS, Cernadas E, et al. An extensive experimental survey of regression methods. Neural Netw. 2019;111:11–34.
   PubMed Web of Science ®Google Scholar
• Marill KA. Advanced statistics: Linear regression, part II: Multiple linear regression. Acad Emerg Med. 2004;11(1):94–102.
   PubMed Web of Science ®Google Scholar
• Khalid SG, Zhang J, Chen F, et al. Blood pressure estimation using photoplethysmography only: Comparison between different machine learning approaches. J Healthc Eng. 2018;2018:1548647.
   PubMed Web of Science ®Google Scholar
• Seeger M. Gaussian processes for machine learning. Int J Neural Syst. 2004;14(2):69–106.
   PubMedGoogle Scholar
• Banerjee M, Reynolds E, Andersson HB, et al. Tree-based analysis: a practical approach to create clinical decision-making tools. Circ Cardiovasc Qual Outcomes. 2019;12(5):e000056.
   PubMed Web of Science ®Google Scholar
• Chowdhury MH, Shuzan MNI, Chowdhury MEH, et al. Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors (Switzerland). 2020;20(11):3127.
   Web of Science ®Google Scholar
• Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–29.
   PubMed Web of Science ®Google Scholar
• Zaharchuk G, Gong E, Wintermark M, et al. Deep learning in neuroradiology. AJNR Am J Neuroradiol. 2018;39(10):1776–1784.
   PubMed Web of Science ®Google Scholar
• Moen E, Bannon D, Kudo T, et al. Deep learning for cellular image analysis. Nat Methods. 2019;16(12):1233–1246.
   PubMed Web of Science ®Google Scholar
• Zou J, Huss M, Abid A, et al. A primer on deep learning in genomics. Nat Genet. 2019;51(1):12–18.
   PubMed Web of Science ®Google Scholar
• Munir K, Elahi H, Ayub A, et al. Cancer diagnosis using deep learning: a bibliographic review. Cancers (Basel). 2019;11(9):1235.
   Web of Science ®Google Scholar
• Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118.
   PubMed Web of Science ®Google Scholar
• Yamashita R, Nishio M, Do RKG, et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9(4):611–629.
   PubMed Web of Science ®Google Scholar
• Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: a review. J Med Syst. 2018;42:1–13.
   PubMedGoogle Scholar
• Cao C, Liu F, Tan H, et al. Deep learning and its applications in biomedicine. Genom Proteom Bioinformat. 2018;16(1):17–32.
   PubMed Web of Science ®Google Scholar
• Yan C, Li Z, Zhao W, et al. Novel deep convolutional neural network for cuff-less blood pressure measurement using ECG and PPG signals. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc.; 2019. p. 1917–20. https://doi.org/10.1109/EMBC.2019.8857108.
   Google Scholar
• Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput. 2019;31(7):1235–1270.
   PubMed Web of Science ®Google Scholar
• Maragatham G, Devi S. LSTM model for prediction of heart failure in big data. J Med Syst. 2019;43(5):111.
   PubMed Web of Science ®Google Scholar
• Lee D, Kwon H, Son D, et al. Beat-to-beat continuous blood pressure estimation using bidirectional long short-term memory network. Sensors. 2020;21(1):96.
   Web of Science ®Google Scholar
• Li YH, Harfiya LN, Purwandari K, et al. Real-time cuffless continuous blood pressure estimation using deep learning model. Sensors. 2020;20(19):5606–5619.
   Web of Science ®Google Scholar
• Kim SH, Lilot M, Sidhu KS, et al. Accuracy and precision of continuous noninvasive arterial pressure monitoring compared with invasive arterial pressure: a systematic review and Meta-analysis. Anesthesiology. 2014;120(5):1080–1097.
   PubMed Web of Science ®Google Scholar
• Abbott TEF, Howell S, Pearse RM, et al. Mode of blood pressure monitoring and morbidity after noncardiac surgery A prospective multicentre observational cohort study. Eur J Anaesthesiol. 2021;38(5):468–476.
   PubMed Web of Science ®Google Scholar