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Learning and non-learning algorithms for cuffless blood pressure measurement: a review

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Abstract

The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.

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References

  1. Griggs D, Sharma M, Naghibi A, Wallin C, Ho V, Barbosa K, Ghirmai T, Cao H, Krishnan SK (2016) Design and development of continuous cuff-less blood pressure monitoring devices. In: 2016 IEEE Sensors, pp 1–3

  2. Tanveer MS, Hasan MK (2019) Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network. Biomed Signal Process Control 1;51:382–92

    Article  Google Scholar 

  3. Rosendorff C, Lackland DT, Allison M, Aronow WS, Black HR, Blumenthal RS, Cannon CP, De Lemos JA, Elliott WJ, Findeiss L, Gersh BJ (2015) Treatment of hypertension in patients with coronary artery disease: A scientific statement from the American Heart Association, American College of Cardiology, and American Society of Hypertension. J Amer College of Cardiol 12;65(18):1998–2038

    Article  Google Scholar 

  4. Liang Y, Chen Z, Ward R, Elgendi M (2018) Hypertension assessment via ECG and PPG signals: An evaluation using MIMIC database. Diagnostics 8(3):65

    Article  PubMed Central  Google Scholar 

  5. Lin WH, Wang H, Samuel OW, Liu G, Huang Z, Li G (2018) New photoplethysmogram indicators for improving cuffless and continuous blood pressure estimation accuracy. Physiol Meas 39(2):025005

    Article  PubMed  Google Scholar 

  6. Shimazaki S, Bhuiyan S, Kawanaka H, Oguri K (2018) Features extraction for cuffless blood pressure estimation by autoencoder from photoplethysmography. In: 2018 40Th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2857–2860

  7. Song K, Chung KY, Chang JH (2019) Cuff-less deep learning-based blood pressure estimation for smart wristwatches. IEEE Transactions on Instrumentation and Measurement

  8. Wang JJ, Lin CT, Liu SH, Wen ZC (2002) Model-based synthetic fuzzy logic controller for indirect blood pressure measurement. IEEE Trans Syst Man Cybern Part B (Cybernetics) 7;32(3):306–15

    Article  Google Scholar 

  9. Xuan FW (2011) An exploration on real-time cuffless blood pressure estimation for e-home healthcare, Doctoral dissertation, University of Macau

  10. Gao M, Cheng HM, Sung SH, Chen CH, Olivier NB, Mukkamala R (2016) Estimation of pulse transit time as a function of blood pressure using a nonlinear arterial tube-load model. IEEE Trans Biomed Eng 22;64(7):1524–34

    Article  Google Scholar 

  11. Watanabe N, Bando YK, Kawachi T, Yamakita H, Futatsuyama K, Honda Y, Yasui H, Nishimura K, Kamihara T, Okumura T, Ishii H (2017) Development and validation of a novel cuff-less blood pressure monitoring device. JACC: Basic Translat Sci 25;2(6):631–42

    Google Scholar 

  12. Baek J, Kim J, Kim N, Lee D, Park SM (2018) Validation of cuffless blood pressure monitoring using wearable device. TENCON IEEE 0416–0419

  13. Lin WQ, Wu HH, Su CS, Yang JT, Xiao JR, Cai YP, Wu XZ, Chen GZ (2017) Comparison of continuous noninvasive blood pressure monitoring by TL-300 with standard invasive blood pressure measurement in patients undergoing elective neurosurgery. J Neurosurg Anesthesiol 1;29(1):1–7

    Article  Google Scholar 

  14. Kim JY, Cho BH, Im SM, Jeon MJ, Kim IY, Kim SI (2005) Comparative study on artificial neural network with multiple regressions for continuous estimation of blood pressure. In: Engineering in medicine and biology society, 2005. IEEE-EMBS 2005. 27th annual international conference of the IEEE, pp 6942–5

  15. Ruiz-Rodríguez JC, Ruiz-Sanmartín A, Ribas V, Caballero J, García-Roche A, Riera J, Nuvials X, de Nadal M, de Sola-Morales O, Serra J, Rello J (2013) Innovative continuous non-invasive cuffless blood pressure monitoring based on photoplethysmography technology. Intensive Care Med 1;39(9):1618–25

    Article  Google Scholar 

  16. Kao YH, Chao PC, Wey CL (2018) Design and validation of a new ppg module to acquire high-quality physiological signals for high-accuracy biomedical sensing. IEEE J Select Topics Quantum Electron 24;25(1):1–0

    Article  Google Scholar 

  17. Khalid SG, Zhang J, Chen F, Zheng D (2018) Blood pressure estimation using photoplethysmography only: comparison between different machine learning approaches. Journal of Healthcare Engineering

  18. Şentürk Ü, Yücedağ I, Polat K (2018) Repetitive neural network (RNN) based blood pressure estimation using PPG and ECG signals. In: 2018 2Nd international symposium on multidisciplinary studies and innovative technologies (ISMSIT), pp 1-4

  19. Nye R, Zhang Z, Fang Q (2015) Continuous non-invasive blood pressure monitoring using photoplethysmography: a review. International Symposium on Bioelectronics and Bioinformatics (ISBB) 176–179

  20. Shen Z, Miao F, Meng Q, Li Y (2015) Cuffless and continuous blood pressure estimation based on multiple regression analysis. In: 2015 5th international conference on information science and technology (ICIST), pp 117-120

  21. Lan KC, Raknim P, Kao WF, Huang JH (2018) Toward hypertension prediction based on PPG-derived HRV signals: A feasibility study. J Med Syst 1;42(6):103

    Article  Google Scholar 

  22. Di Lascio N, Gemignani V, Bruno RM, Bianchini E, Stea F, Ghiadoni L, Faita F (2015) Noninvasive assessment of carotid pulse pressure values: an accelerometric-based approach. IEEE Trans Biomed Eng 10;63(4):869–75

    Google Scholar 

  23. Hughes DJ, Babbs CF, Geddes LA, Bourland JD (1979) Measurements of Young’s modulus of elasticity of the canine aorta with ultrasound. Ultrasonic Imag 1;1(4):356–67

    Article  Google Scholar 

  24. Liang Y, Chen Z, Ward R, Elgendi M (2018) Hypertension assessment via ECG and PPG signals: An evaluation using MIMIC database. Diagnostics 8(3):65

    Article  PubMed Central  Google Scholar 

  25. Solà i Carós JM (2011) Continuous non-invasive blood pressure estimation. Doctoral dissertation, ETH Zurich

  26. Chen Y, Wen C, Tao G, Bi M (2012) Continuous and noninvasive measurement of systolic and diastolic blood pressure by one mathematical model with the same model parameters and two separate pulse wave velocities. Ann Biomed Eng 40(4):871–82

    Article  PubMed  Google Scholar 

  27. Geddes LA, Voelz MH, Babbs CF, Bourland JD, Tacker WA (1981) Pulse transit time as an indicator of arterial blood pressure. Psychophysiology 18(1):71–4

    Article  CAS  PubMed  Google Scholar 

  28. Huynh TH, Jafari R, Chung WY (2018) Noninvasive cuffless blood pressure estimation using pulse transit time and impedance plethysmography. IEEE Trans Biomed Eng 17;66(4):967–76

    Article  Google Scholar 

  29. Poon CC, Zhang YT (2005) Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time. IEEE engineering in medicine and biology 27th annual conference 5877–5880

  30. Kachuee M, Kiani MM, Mohammadzade H, Shabany M (2015) Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time. In: 2015 IEEE International symposium on circuits and systems (ISCAS), pp 1006-1009

  31. Obrist PA, Light KC, McCubbin JA, Hutcheson JS, Hoffer JL (1978) Pulse transit time: Relationship to blood pressure. Behav Res Methods Instrument 1;10(5):623–6

    Article  Google Scholar 

  32. Nabeel PM, Raj VK, Joseph J, Abhidev VV, Sivaprakasam M (2019) Local pulse wave velocity: Theory, methods, advancements, and clinical applications. IEEE reviews in biomedical engineering. Jul 29

  33. Nabeel PM, Chilaka V, Joseph J, Sivaprakasam M (2019) Deep Learning for Blood Pressure estimation: an Approach using Local Measure of Arterial Dual Diameter Waveforms. In: 2019 IEEE International symposium on medical measurements and applications (memea), pp 1–6

  34. Esmaili A, Kachuee M, Shabany M (2017) Nonlinear cuffless blood pressure estimation of healthy subjects using pulse transit time and arrival time. IEEE Trans Instrument Measure 12;66(12):3299–308

    Article  Google Scholar 

  35. Sharma M, Barbosa K, Ho V, Griggs D, Ghirmai T, Krishnan SK, Hsiai TK, Chiao JC, Cao H (2017) Cuff-less and continuous blood pressure monitoring: A methodological review. Technologies 5(2):21

    Article  Google Scholar 

  36. Soukup L, Hruskova J, Jurak P, Halamek J, Zavodna E, Viscor I, Matejkova M, Vondra V (2019) Comparison Of noninvasive pulse transit time determined from Doppler aortic flow and multichannel bioimpedance plethysmography. Med Biolog Eng Comput 57(5):1151–1158

    Article  Google Scholar 

  37. Zhang Q, Zhou D, Zeng X (2017) Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed Eng Online 16(1):23

    Article  PubMed  PubMed Central  Google Scholar 

  38. Liu SH, Cheng DC, Su CH (2017) A cuffless blood pressure measurement based on the Impedance plethysmography technique. Sensors 17(5):1176

    Article  Google Scholar 

  39. Ahmad S, Chen S, Soueidan K, Batkin I, Bolic M, Dajani H, Groza V (2012) Electrocardiogram-assisted blood pressure estimation. IEEE Trans Biomed Eng 10;59(3):608–18

    Article  Google Scholar 

  40. Bolea J, Lázaro J, Gil E, Rovira E, Remartínez JM, Laguna P, Pueyo E, Navarro A, Bailón R (2017) Pulse rate and transit time analysis to predict hypotension events after spinal anesthesia during programmed cesarean labor. Annals Biomed Eng 1;45(9):2253–63

    Article  Google Scholar 

  41. Zhang Q, Zeng X, Hu W, 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 24;5:10547–61

    Article  Google Scholar 

  42. Zhang G, Gao M, Xu D, Olivier NB, Mukkamala R (2011) Pulse arrival time is not an adequate surrogate for pulse transit time as a marker of blood pressure. J Appl physiol 111(6):1681–6

    Article  PubMed  Google Scholar 

  43. Chen W, Kobayashi T, Ichikawa S, Takeuchi Y, Togawa T (2000) Continuous estimation of systolic blood pressure using the pulse arrival time and intermittent calibration. Med Biol Eng Comput 38 (5):569–74

    Article  CAS  PubMed  Google Scholar 

  44. Solà J, Proença M, Chételat O, Wearable PWV (2013) Technologies to measure Blood Pressure: eliminating brachial cuffs. In: 2013 35th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 4098–4101

  45. Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif AA (2020) A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods. Appl Acoust 1:164:107279

    Article  Google Scholar 

  46. Solà J, Delgado Gonzalo R (eds) (2019) The Handbook of Cuffless Blood Pressure monitoring: A Practical Guide for Clinicians, Researchers, and Engineers. Springer Nature , Basingstoke

  47. Gavish B, Ben Dov IZ, Bursztyn M (2008) Linear relationship between systolic and diastolic blood pressure monitored over 24 h: assessment and correlates. J Hypertens 1;26(2):199–209

    Article  Google Scholar 

  48. Agham N, Chaskar U (2019) Prevalent Approach of Learning Based Cuffless Blood Pressure Measurement System for Continuous Health-care Monitoring. In: IEEE International symposium on medical measurements and applications (memea), pp 1-5

  49. Sannino G, De Falco I, De Pietro G (2018) A continuous noninvasive arterial pressure (CNAP) approach for health 4.0 systems. IEEE Trans Indust Informat 1;15(1):498–506

    Article  Google Scholar 

  50. Kachuee M, Kiani MM, Mohammadzade H, Shabany M (2016) Cuffless blood pressure estimation algorithms for continuous health-care monitoring. IEEE Trans Biomed Eng 14;64(4):859–69

    Article  Google Scholar 

  51. Mousavi SS, Firouzmand M, Charmi M, Hemmati M, Moghadam M, Ghorbani Y (2019) Blood pressure estimation from appropriate and inappropriate PPG signals using a whole-based method. Biomed Signal Process Control 47:196–206

    Article  Google Scholar 

  52. Miao F, Fu N, Zhang YT, Ding XR, Hong X, He Q, Li Y (2017) A novel continuous blood pressure estimation approach based on data mining techniques. IEEE J Biomed Health Inform 28;21 (6):1730–40

    Article  Google Scholar 

  53. Jain M, Kumar N, Deb S, Majumdar A (2016) A sparse regression based approach for cuff-less blood pressure measurement. In: Acoustics, speech and signal processing (ICASSP), 2016 IEEE international conference on. IEEE, pp 789–93

  54. Pan J, Zhang Y (2017) Improved blood pressure estimation using photoplethysmography based on ensemble method. In: Pervasive systems, algorithms and networks & 2017 11th international conference on frontier of computer science and technology & 2017 third international symposium of creative computing (ISPAN-FCST-ISCC), 2017 14th international symposium on. IEEE, pp 105–11

  55. Fujita D, Suzuki A, Ryu K (2019) PPG-Based systolic blood pressure estimation method using PLS and level-crossing feature. Appl Sci 9(2):304

    Article  Google Scholar 

  56. Lin WH, Wang H, Samuel OW, Li G (2017) Using a new PPG indicator to increase the accuracy of PTT-based continuous cuffless blood pressure estimation. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 738-741

  57. Gaddum NR, Alastruey J, Beerbaum P, Chowienczyk P, Schaeffter T (2013) A technical assessment of pulse wave velocity algorithms applied to non-invasive arterial waveforms. Annals Biomed Eng 1;41 (12):2617–29

    Article  Google Scholar 

  58. Shobitha S, Amita P, Krupa BN, Beng GK (2017) Cuffless blood pressure prediction from PPG using relevance vector machine. In: Electrical, electronics, communication, computer, and optimization techniques (ICEECCOT), 2017 international conference on. IEEE, pp 75–8

  59. Xu Y, Ping P, Wang D, Zhang W Analysis for the influence of abr sensitivity on PTT-based cuff-less blood pressure estimation before and after exercise. J Healthcare Eng 2018

  60. Zhang B, Ren J, Cheng Y, Wang B, Wei Z (2019) Health data driven on continuous blood pressure prediction based on gradient boosting decision tree algorithm. IEEE Access 7;7:32423–33

    Article  Google Scholar 

  61. Zhang B, Ren H, Huang G, Cheng Y, Hu C (2019) Predicting blood pressure from physiological index data using the SVR algorithm. BMC bioinformatics 1;20(1):109

    Article  Google Scholar 

  62. Zhang Y, Feng Z (2017) A SVM method for continuous blood pressure estimation from a PPG signal. In: Proceedings of the 9th international conference on machine learning and computing, pp 128–132

  63. Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A (2018) Non-invasive blood pressure estimation from ECG using machine learning techniques. Sensors 18(4):1160

    Article  Google Scholar 

  64. Garcia Carretero R, Vigil-Medina L, Barquero-Perez O, Ramos-Lopez J (2020) Pulse wave velocity and machine learning to predict cardiovascular outcomes in prediabetic and diabetic populations. J Med Syst 1;44(1):16

    Article  Google Scholar 

  65. Zhang B, Wei Z, Ren J, Cheng Y, Zheng Z (2018) An empirical study on predicting blood pressure using classification and regression trees. IEEE Access 10;6:21758–68

    Article  Google Scholar 

  66. He R, Huang ZP, Ji LY, Wu JK, Li H, Zhang ZQ (2016) Beat-to-beat ambulatory blood pressure estimation based on random forest. In: Wearable and implantable body sensor networks (BSN), 2016 IEEE 13th international conference on. IEEE, pp 194–8

  67. Pauly O (2012) Random forests for medical applications. Doctoral dissertation, Technische Universität München

  68. Nimmala S, Ramadevi Y, Sahith R, Cheruku R (2018) High blood pressure prediction based on AAA++ using machine-learning algorithms, vol 1;5, p 1497114

  69. Tan X, Ji Z, Zhang Y (2018) Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm. Technol Health Care 26(S1):87–101

    Article  PubMed  PubMed Central  Google Scholar 

  70. Wang L, Zhou W, Xing Y, Zhou X (2018) A novel neural network model for blood pressure estimation using photoplethesmography without electrocardiogram. J Healthcare Eng 2018

  71. Wu TH, Pang GK, Kwong EW (2014) Predicting systolic blood pressure using machine learning. In: 7th international conference on information and automation for sustainability, pp 1–6

  72. Fang YF, Huang PW, Chung ML, Wu BFA (2018) Feature selection method for Vision-Based blood pressure measurement. In: 2018 IEEE international conference on systems man, and cybernetics (SMC), pp 2158–2163

  73. Lee S, Chang JH (2016) Oscillometric blood pressure estimation based on deep learning. IEEE Trans Indust Inform 26;13(2):461–72

    Google Scholar 

  74. Argha A, Celler BG (2019) Blood pressure estimation from time-domain features of oscillometric waveforms using long short-term memory recurrent neural networks. IEEE Trans Instrument Measure 12;69 (6):3614–22

    Article  Google Scholar 

  75. Argha A, Wu J, Su SW, Celler BG (2019) Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models. IEEE Access 6;7:113427–39

    Article  Google Scholar 

  76. Lee S, Lee G (2020) Ensemble methodology for confidence interval in oscillometric blood pressure measurements. J Med Syst 44(5):1–9

    Article  CAS  Google Scholar 

  77. Ertuğrul ÖF, Sezgin N (2018) A noninvasive time-frequency-based approach to estimate cuffless arterial blood pressure. Turkish J Electric Eng Comput Sci 28;26(5):2260–74

    Article  Google Scholar 

  78. Lee S, Chang JH, Nam SW, Lim C, Rajan S, Dajani HR, Groza VZ (2013) Oscillometric blood pressure estimation based on maximum amplitude algorithm employing Gaussian mixture regression. IEEE Trans Instrument Measure 29;62(12):3387–9

    Article  Google Scholar 

  79. Poliñski A, Czuszyñski K, Kocejko T (2018) Blood pressure estimation based on blood flow, ECG and respiratory signals using recurrent neural networks. In: 2018 11th international conference on human system interaction (HSI). IEEE, pp 86–92

  80. Ghosh S, Banerjee A, Ray N, Wood PW, Boulanger P, Padwal R (2018) Using accelerometric and gyroscopic data to improve blood pressure prediction from pulse transit time using recurrent neural network. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 935–9

  81. Radha M et al (2018) Wrist-worn blood pressure tracking in healthy free-living individuals using neural networks. arXiv:1805.09121

  82. Su P, Ding X, Zhang Y, Miao F, Zhao N (2017) Learning to Predict Blood Pressure with Deep Bidirectional LSTM Network. arXiv:1705.04524

  83. Kim SH, Song JG, Park JH, Kim JW, Park YS, Hwang GS (2013) Beat-to-beat tracking of systolic blood pressure using noninvasive pulse transit time during anesthesia induction in hypertensive patients. Anesthes Analges 1;116(1):94–100

    Article  Google Scholar 

  84. Kurylyak Y, Lamonaca F, Grimaldi D (2013) A Neural Network-based method for continuous blood pressure estimation from a PPG signal. In: 2013 IEEE International instrumentation and measurement technology conference (i2MTC), pp 280-283

  85. Choudhury AD, Banerjee R, Sinha A, Kundu S (2014) Estimating blood pressure using Windkessel model on photoplethysmogram. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, pp 4567–4570

  86. Suzuki A, Ryu K (2013) Feature selection method for estimating systolic blood pressure using the Taguchi method. IEEE Trans Indust Inform 4;10(2):1077–85

    Google Scholar 

  87. Duan K, Qian Z, Atef M, Wang G (2016) A feature exploration methodology for learning based cuffless blood pressure measurement using photoplethysmography. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 6385-6388

  88. Gaurav A, Maheedhar M, Tiwari VN, Narayanan R (2016) Cuff-less PPG based continuous blood pressure monitoring - a smartphone based approach. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 607-610

  89. Datta S, Banerjee R, Choudhury AD, Sinha A, Pal A (2016) Blood pressure estimation from photoplethysmogram using latent parameters. In: 2016 IEEE International conference on communications (ICC), pp 1–7

  90. Sideris C, Kalantarian H, Nemati E, Sarrafzadeh M (2016) Building continuous arterial blood pressure prediction models using recurrent networks. In: 16 IEEE International conference on smart computing (SMARTCOMP), pp 1–5

  91. Xing X, Sun M (2016) Optical blood pressure estimation with photoplethysmography and FFT-based neural networks. Biomed Opt Express 1;7(8):3007–20

    Article  Google Scholar 

  92. Gao SC, Wittek P, Zhao L, Jiang WJ (2016) Data-driven estimation of blood pressure using photoplethysmographic signals. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 766-769

  93. Atomi K, Kawanaka H, Bhuiyan M, Oguri K (2017) Cuffless blood pressure estimation based on data-oriented continuous health monitoring system. Computational and Mathematical Methods in Medicine

  94. Lee S, Chang JH (2017) Deep belief networks ensemble for blood pressure estimation. IEEE Access 6;5:9962–72

    Article  Google Scholar 

  95. Matsumura K, Rolfe P, Toda S, Yamakoshi T (2018) Cuffless blood pressure estimation using only a smartphone. Scientif Rep 8;8(1):1–9

    Google Scholar 

  96. Su P, Ding XR, Zhang YT, Liu J, Miao F, Zhao N (2018) Long-term blood pressure prediction with deep recurrent neural networks. In: 2018 IEEE EMBS International conference on biomedical & health informatics (BHI), pp 323-328

  97. Ripoll VR, Vellido A (2019) Blood pressure assessment with differential pulse transit time and deep learning: A proof of concept. Kidney Dis 5(1):23–7

    Article  Google Scholar 

  98. Pan Fan, He Peiyu, Chen Fei, Xiaobo P u, Zhao Qijun, Zheng Dingchang (2019) Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement. Annals Med 51:7-8:397–403

    Article  Google Scholar 

  99. Fan X, Wang H, Xu F, Zhao Y, Tsui KL (2019) Homecare-oriented intelligent long-term monitoring of blood pressure using electrocardiogram signals. IEEE Transactions on Industrial Informatics

  100. Slapničar G, Mlakar N, Luštrek M. (2019) Blood pressure estimation from photoplethysmogram using a Spectro-Temporal deep neural network. Sensors 2019(15):3420

    Article  Google Scholar 

  101. Chen S, Ji Z, Wu H, Xu Y (2019) A Non-Invasive continuous blood pressure estimation approach based on machine learning. Sensors 19(11):2585

    Article  Google Scholar 

  102. Mohebbian MR, Dinh A, Wahid K, Alam MS (2020) Blind, Cuff-less, calibration-free and continuous blood pressure estimation using optimized inductive group method of data handling. Biomed Signal Process Control 1;57:101682

    Article  Google Scholar 

  103. Mahmood U, Al-jumaily A (2007) Type-2 fuzzy classification of blood pressure parameters. In: 2007 3rd international conference on intelligent sensors sensor networks and information, pp 595–600

  104. Zong W, Moody GB, Mark RG (2004) Reduction of false arterial blood pressure alarms using signal quality assessement and relationships between the electrocardiogram and arterial blood pressure. Med Biol Eng Comput 1;42(5):698–706

    Article  Google Scholar 

  105. Abdullah AA, Zakaria Z, Mohamad NF (2011) Design and development of fuzzy expert system for diagnosis of hypertension. In: 2011 Second international conference on intelligent systems, modelling and simulation, pp 113-117

  106. Morsi I, El Gawad YZ (2013) Fuzzy logic in heart rate and blood pressure measuring system. In: 2013 IEEE sensors applications symposium proceedings, pp 113–117

  107. Das S, Ghosh PK, Kar S (2013) Hypertension diagnosis: A comparative study using fuzzy expert system and neuro fuzzy system. In: 2013 IEEE International conference on fuzzy systems (FUZZ-IEEE), pp 1–7

  108. Mahfouf M, Abbod MF, Linkens DA (2001) A survey of fuzzy logic monitoring and control utilisation in medicine. Artif Intell Med 1;21(1-3):27–42

    Article  Google Scholar 

  109. Peter L, Noury N, Cerny M (2014) A review of methods for non-invasive and continuous blood pressure monitoring: Pulse transit time method is promising? Irbm 1;35(5):271–82

    Article  Google Scholar 

  110. Töreyin H, Javaid AQ, Ashouri H, Ode O, Inan OT (2015) Towards ubiquitous blood pressure monitoring in an armband using pulse transit time. IEEE Biomed Circ Syst Conf (BioCAS) 1–4

  111. Li J, Sawanoi Y (2017) The history and innovation of home blood pressure monitors. IEEE HISTory of ELectrotechnolgy CONference (HISTELCON) 82–86

  112. Lopez G, Shuzo M, Ushida H, Hidaka K, Yanagimoto S, Imai Y, Kosaka A, Delaunay JJ, Yamada I (2010) Continuous blood pressure monitoring in daily life. J Adv Mechani Design Syst Manufact 4(1):179–86

    Article  Google Scholar 

  113. Zheng YL, Yan BP, Zhang YT, Poon CC (2014) An armband wearable device for overnight and cuff-less blood pressure measurement. IEEE Trans Biomed Eng 18;61(7):2179–8

    Article  Google Scholar 

  114. Hung CH, Bai YW, Tsai RY (2012) Design of blood pressure measurement with a health management system for the aged. IEEE Trans Consumer Electron 5;58(2):619–25

    Article  Google Scholar 

  115. Takano M, Ueno A (2018) Noncontact in-bed measurements of physiological and behavioral signals using an integrated fabric-sheet sensing scheme. IEEE J Biomed Health Inform 7;23(2):618–30

    Article  Google Scholar 

  116. Taleyarkhan PR, Geddes LA, Kemeny AE, Vitter JS (2009) Loose cuff hypertension. Cardiovascular Eng 1;9(3):113–8

    Article  Google Scholar 

  117. Sun S, Bezemer R, Long X, Muehlsteff J, Aarts R (2016) Systolic blood pressure estimation using PPG and ECG during physical exercise. Physiol Meas 37(12):2154

    Article  CAS  PubMed  Google Scholar 

  118. Garcia-Carretero R, Barquero-Perez O, Mora-Jimenez I, Soguero-Ruiz C, Goya-Esteban R, Ramos-Lopez J (2019) Identification Of clinically relevant features in hypertensive patients using penalized regression: A case study of cardiovascular events. Med Biol Eng Comput 57(9):2011–2026

    Article  PubMed  Google Scholar 

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  1. College of Engineering Pune, Wellesely Rd, Pune, 411005, India

    Nishigandha Dnyaneshwar Agham & Uttam M. Chaskar

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Agham, N.D., Chaskar, U.M. Learning and non-learning algorithms for cuffless blood pressure measurement: a review. Med Biol Eng Comput 59, 1201–1222 (2021). https://doi.org/10.1007/s11517-021-02362-6

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