Towards wearable blood pressure measurement systems from biosignals: a review
Towards wearable blood pressure measurement systems from biosignals: a review
Blood pressure is the pressure by the blood to the vein wall. High blood pressure, which is called silent death, isthe cause of nearly 13% of mortality all over the world. Blood pressure is not only measured in the medical environment,but the blood pressure measurement is also a need for people in their daily life. Blood pressure estimation systemswith low error rates have been developed besides the new technologies and algorithms. Blood pressure measurementsare differentiated as invasive blood pressure (IBP) measurement and noninvasive blood pressure (NIBP) measurementmethods. Although IBP measurement provides the most accurate results, it cannot be used in daily life because itcan only be performed by qualified medical staff with specialized medical equipment. NIBP measurement is based onmeasuring physiological signals taken from the body and producing results with decision mechanisms. Oscillometric,pulse transit time (PTT), pulse wave velocity, and feature extraction methods are mentioned in the literature as NIBP.In the oscillometric method of the sphygmomanometer, an electrocardiogram is used in PTT methods as a result of thecomparison of signals such as electrocardiography, photoplethysmography, ballistocardiography, and seismocardiography.The increase in the human population and worldwide deaths due to the highly elevated blood pressure makes the needfor precise measurements and technological devices more clear. Today, wearable technologies and sensors have beenfrequently used in the health sector. In this review article, the invasive and noninvasive blood pressure methods,including various biosignals, have been investigated and then compared with each other concerning the measurement ofcomfort and robust estimation.
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- [1] World Health Organization. A Global brief on Hypertension. World Health Day 2013.
- [2] Mills KT, Bundy JD, Kelly TN. Global disparities of hypertension prevalence and control: a systematic analysis of
population-based studies from 90 countries. Circulation 2016; 134(6): 441-450.
- [3] Bhargava M, Ikram MK, Wong TY. How does hypertension affect your eyes? Journal of Human Hypertension 2012;
26: 71-83.
- [4] World Health Organization. World Health Statistics 2015.
- [5] Houlihan SJ, Simpson SH, Cave AJ. Hypertension treatment and control rates. Canadian Family Physician 2009;
55: 735-741.
- [6] Jackson CF, Wenger NK. Cardiovascular disease in the elderly. Revista Espanola de Cardiologia 2011; 64(8): 697-
712.
- [7] American Heart Association. Healthy, and unhealthy blood pressure ranges, 2019.
- [8] Hall ME, Wang Z, do Carmo J, Kamimura D, Hall JE. Obesity and metabolic syndrome hypertension. In: Berbari
A, Mancia G (editors). Disorders of Blood Pressure Regulation. Updates in Hypertension and Cardiovascular
Protection. Switzerland: Springer, Cham 2018; pp. 705-722.
- [9] Cengiz K. Beyaz önlük ( white coat) hipertansiyonu. Offical Journal of the Turkish Nephrology Association 2000;
2: 75-78 (in Turkish).
- [10] O’Brien E, Petrie J, Littler W. The British hypertension society protocol for the evaluation of blood pressure
measuring devices. Journal of Hypertension 1993; 11: 43-63.
- [11] Assocation for the Advancement of Medical Intrumentation. Manual, electronic or automated sphygmomanometers.
Arlington, VA, USA. American National Standard ANSI/AAMI SP10: 2002.
- [12] O’Brien E, Pickering T, Asmar R, Myers M. Working group on pressure monitoring of the European Society of
Hypertension international protocol for validation of blood pressure mesuring devices in adults. Blood Pressure
Monitoring 2002; 7(1): 3-17.
- [13] Alison S. The Harvey experiments. British Medical Journal; London 2018; 360: k346. doi: 10.1136/bmj.k346
- [14] Akbar S, Makati D, Ahmad M, Suleiman H. Exploring the utility of pulse wave analysis in patients with uncontrolled
brachial blood pressures in the routine outpatient setting. Journal of Nephrology Research 2018; 4: 146-152.
- [15] Eknoyan G. Stephen Hales: the contributions of an enlightenment physiologist to the study of the kidney in health
and disease. Giants in Nephrology 2016; 33: 1-7.
- [16] Hall WD. Stephen Hales: theologian, botanist, physiologist, discoverer of hemodynamics. Clinical Cardiology. 1987;
10: 487-489.
- [17] Romagnoli S, Ricci Z, Quattrone D. Accuracy of invasive arterial pressure monitoring in cardiovascular patients:
an observational study. Critical Care, 2014; 18: 644.
- [18] Weems JJ, Chamberland ME. Candida parapsilosis fungemia associated with parenteral nutrition and contaminated
blood pressure transducers. Journal Of Clinical Microbiology, 1987; 25(6): 1029-1032.
- [19] Rader F, Victor RG. The slow evolution of blood pressure monitoring but wait, not so fast! JACC: Basic to
Translational Science 2017; 2(6): 643-645.
- [20] Kuhtz-Buschbecka JP, Schaeferb J. Mechanosensitivity: From Aristotle’s sense of touch to cardiac mechano-electric
coupling. Progress in Biophysics and Molecular Biology 2017; 130: 126-131.
- [21] Rook WH, Turner JD. Analysis of damping characteristics of arterial catheter blood pressure monitoring in a large
intensive care unit. Southern African Journal of Critical Care 2017; 33: 8-10.
- [22] Lowe GD, Willshire RJ. Method and apparatus for hemodynamic monitoring using combined blood flow and blood
pressure measurement. United States Patent Patent No: US 9649037B2.
- [23] Muntner P, Carey RM, Jamerson K. Rationale for ambulatory and home blood pressure monitoring thresholds in
the 2017 American college of cardiology/American heart association guideline. Hypertension. 2019; 73: 33-38.
- [24] Kai K, Baker PD. Perioperative noninvasive blood pressure monitoring. Anesthesia & Analgesia 2018; 127: 408-411.
- [25] Filler G, Sharma AP. Methodology of Casual Blood Pressure Measurement. In: Flynn J, Ingelfinger J, Redwine K.
(eds) Pediatric Hypertension, Switzerland: Springer, Cham 2017; pp. 1-17.
- [26] Celler BG, Le P. Improving the quality and accuracy of non-invasive blood pressure measurement by visual
inspection and automated signal processing of the Korotkoff sounds. Institute of Physics and Engineering in Medicine
2017; 38(6): 1006-1022.
- [27] Feenstra RK, Allaart CP. Accuracy of oscillometric blood pressure measurement in atrial fibrillation. Blood Pressure
Monitoring 2018; 23(2): 59-63.
- [28] Duncombe SL, Voss C. Oscillometric and auscultatory blood pressuremeasurement methods in children: a systematic
review and meta-analysis. Journal of Hypertension 2017; 35: 213-224.
- [29] Stergiou GS, Palatini P. Blood pressure monitoring: theory and practice. European Society of Hypertension Working
Group on Blood Pressure Monitoring and Cardiovascular Variability Teaching Course Proceedings. Blood Pressure
Monitoring 2018; 23: 1-8.
- [30] Rotch AL, Dean JO, Kendrach MG. Blood pressure monitoring with home monitors versus mercury sphygmomanometer. Annals of Pharmacotherapy 2011; 35(7-8): 817-822.
- [31] Raja P, Jalali A. Accuracy of oscillometric blood pressure algorithms in healthy adults and in adults with cardiovascular risk factors. Blood Pressure Monitoring 2019; 24: 33-37.
- [32] Šelmytė–Besusparė A, Barysienė J. Auscultatory versus oscillometric blood pressure measurement in patients with
atrial fibrillation and arterial hypertension. BMC Cardiovascular Disorder 2017; 17: 87. doi: 10.1186/s12872-017-
0521-6
- [33] Sun J, Chen H. Continuous blood pressure monitoring via non-invasive radial artery applanation tonometry and
invasive arterial catheter demonstrates good agreement in patients undergoing colon carcinoma surgery. Journal of
Clinical Monitoring Computing 2017; 31: 1189-1195.
- [34] Harju J, Vehkaoja A. Comparison of non-invasive blood pressure monitoring using modified arterial applanation
tonometry with intra-arterial measurement. Journal of Clinical Monitoring and Computing 2018; 32: 13-22.
- [35] Trinkmann F, Benck U. Comparison of non-invasive central blood pressure measurements using applanation tonometry and automated oscillometric radial pulse wave analysis. European Heart Journal 2017; 38. doi: 10.1093/eurheartj/ehx493.P5454
- [36] Jain P, Muthiah K. Invasive validation of the SphygmoCor XCEL oscillometric-tonometric blood pressure system
in patients with heartware HVAD. American Heart Association 2018;138:A11004.
- [37] Kanno YOY, Takenaka T. Estimated aortic blood pressure based on radial artery tonometry underestimates directly
measured aortic blood pressure in patients with advancing chronic kidney disease staging and increasing arterial
stiffness. International Society of Nephrology 2017; 91: 757.
- [38] Greiwe G, Hoffmann S. Comparison of blood pressure monitoring by applanation tonometry and invasively assessed
blood pressure in cardiological patients. Journal of Clinical Monitoring and Computing 2018; 32: 817-823.
- [39] Wenbo GU. Method and device for tonometric blood pressure measurement. United States Patent, US9931076 B2.
- [40] Scalise L, Cosoli G. The measurement of blood pressure without contact: An LDV-based technique. In: IEEE 2017
MeMeA International Symposium on Medical Measurements and Applications; Rochester, MN, USA; 2017. pp.
245-250.
- [41] Mehrotra S, Mikhelson I, Sahakian AV. Tonometry Based Blood Pressure Measurements Using a Two-Dimensional
Force Sensor Array. United States Patent Application Publication, US 2017 / 0367596 A1.
- [42] Penáz J. Criteria for set point estimation in the volume clamp method of blood pressure measurement. Physiological
Research 1992; 41(1): 5-10.
- [43] Schramm P, Tzanova I, Gööck T. Noninvasive hemodynamic measurements during neurosurgical procedures in
sitting position. Journal of Neurosurgical Anesthesiology 2017; 29: 251-257.
- [44] Meidert AS, Nold JS. The impact of continuous non-invasive arterial blood pressure monitoring on blood pressure
stability during general anaesthesia in orthopaedic patients. European Journal of Anaesthesiology 2017; 34: 716-722.
- [45] Kakuta N, Tsutsumi YM, Murakami C. Effectiveness of using non-invasive continuous arterial pressure monitoring
with ClearSight in hemodynamic monitoring during living renal transplantation in a recipient: a case report. The
Journal of Medical Investigation 2018; 65: 139-141.
- [46] Nicklas JY, Beckmann D, Killat J. Continuous noninvasive arterial blood pressure monitoring using the vascular
unloading technology during complex gastrointestinal endoscopy: a prospective observational study. Journal of
Clinical Monitoring and Computing 2019; 33: 25-30.
- [47] Michard F, Liu N, Kurz A. The future of intraoperative blood pressure management. Journal of Clinical Monitoring
and Computing 2018; 32: 1–4.
- [48] Nitzan M, Slotki I, Shavit L. More accurate systolic blood pressure measurement is required for improved hypertension management: a perspective. Medical Devices 2017; 10: 157-163.
- [49] Wagner JY, Körner A, Schulte-Uentrop L. A comparison of volume clamp method-based continuous noninvasive
cardiac output (CNCO) measurement versus intermittent pulmonary artery thermodilution in postoperative cardiothoracic surgery patients. Journale of Clincal Monitoring and Computing 2018; 32: 235-244.
- [50] Michard F, Sessler DI, Saugel B. Non-invasive arterial pressure monitoring revisited. Intensive Care Medicine 2018;
44: 2213-2215.
- [51] Berkelmans GFN, Kuipers S, Westerhof BE. Comparing volume-clamp method and intra-arterial blood pressure
measurements in patients with atrial fibrillation admitted to the intensive or medium care unit. Journal of Clinical
Monitoring and Computing 2018; 32: 439-446.
- [52] Westerhof N, Stergiopulos N, Noble MIM. Wave travel and pulse wave velocity: An aid for clinical research and
graduate education. Snapshots of Hemodynamics. Switzerland: 2019, pp. 165-173.
- [53] Ma Y, Choi J, Hourlier-Fargette A, Xue Y, Chung HU, Lee JY. Relation between blood pressure and pulse wave
velocity for human arteries. Proceedings of the National Academy of Sciences of the USA 2018; 115: 11144-11149.
- [54] McCombie D, Zhang G. System for calibrating a blood pressure measurement based on vascular transit of a pulse
wave. United States Patent, US10004409 B2.
- [55] Hulpke-Wette M, Göhler A, Hofmann E, Küchler G. Cuff-less blood pressure measurement using the pulse transit
time - a comparison to cuff-based oscillometric 24 hour blood pressure measurement in children. Journal of
Hypertension 2018; 36: 73.
- [56] Narasimhan R. Cuffless Blood Pressure Measurement Using Handheld Device. United States Patent Application
Publication, US 2018 / 0035949 A1.
- [57] Golberg M, Ruiz-Rivas J, Polani S, Beiderman Y, Zalevsky Z. Large-scale clinical validation of noncontact and
continuous extraction of blood pressure via multipoint defocused photonic imaging. Applied Optics 2018; 57: 45-51.
- [58] Ogawa K, Koyama S, Ishizawa H. Simultaneous measurement of heart sound, pulse wave and respiration with
single fiber bragg grating sensor. In: IEEE 2018 MeMeA International Symposium on Medical Measurements and
Applications Rome, Italy; 2018. pp. 1-5.
- [59] Kim CS, Carek AM, Inan OT, Mukkamala R, Hahn JO. Ballistocardiogram-based approach to cuffless blood
pressure monitoring: proof of concept and potential challenges. IEEE Transactions on Biomedical Engineering
2018; 65: 2384-2391.
- [60] Su BY, Enayati M, Ho KC, Skubic M. Monitoring the relative blood pressure using a hydraulic bed sensor system.
IEEE Transactions on Biomedical Engineering 2019; 66: 740-748.
- [61] Rajala S, Ahmaniemi T, Lindholm H, Müller K, Taipalus T. A chair based ballistocardiogram time interval
measurement with cardiovascular provocations. In: 2018 EMBC 40th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society; Honolulu, Hawaii; 2018. pp. 5685-5688.
- [62] Yousefian P, Shin S, Mousavi A. Data mining investigation of the association between a limb ballistocardiogram
and blood pressure. Physiological Measurement 2018;39: 075009.
- [63] Yee SY, Peters C, Rocznik T, Henrici F, Laermer F. Blood Pressure and Cardiac Monitoring System and Method
Thereof. United States Patent Application Publication, US 2018 / 0192888 A1.
- [64] Lee J, Sohn JJ, Park J, Yang SM, Lee S, Kim HC. Novel blood pressure and pulse pressure estimation based on
pulse transit time and stroke volume approximation. Biomedical Engineering OnLine 2018; 17: 81.
- [65] Peng Y-J, Prabhakar NR. Measurement of sensory nerve activity from the carotid body. Hypoxia 2018; 1742:
115-124.
- [66] Liu J, Yan BP, Zhang Y-T, Ding X-R, Su P, Zhao N. Multi-wavelength photoplethysmography enabling continuous
blood pressure measurement with compact wearable electronics. IEEE Transactions on Biomedical Engineering
2019; 66(6): 1514-1525.
- [67] 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. Physiological Measurement 2018; 39(2): 025010.
- [68] Berzigotti A, Bosch J. Hepatic Venous Pressure Measurement and Other Diagnostic Hepatic Hemodynamic Techniques. In: Berzigotti A, Bosch J (editors). Diagnostic Methods for Cirrhosis and Portal Hypertension. Cham,
Switzerland: Springer, 2018, pp. 33-48.
- [69] Liu SH, Zhu ZY, Lai SH, Huang TS. Using the photoplethysmography technique to improve the accuracy of LVET
measurement in the ICG technique. In: Pan JS, Ito A, Tsai PW, Jain L (editors). Recent Advances in Intelligent
Information Hiding and Multimedia Signal Processing. IIH-MSP 2018. Smart Innovation, Systems and Technologies,
vol 110. Springer, Cham Switzerland, 2018, pp. 183-190.
- [70] Liu SH, Wang JJ, Su CH, Cheng DC. Improvement of left ventricular ejection time measurement in the impedance
cardiography combined with the reflection photoplethysmography. Sensors 2018; 18(9): 3036.
- [71] Wang C, Li X, Hu H. Monitoring of the central blood pressure waveform via a conformal ultrasonic device. Nature
Biomedical Engineering 2018; 2: 687-695.
- [72] Masunishi K, Fukuzawa H, Fuji Y, Yuzawa A, Okamoto K. Pressure sensor, microphone, ultrasonic sensor, blood
pressure sensor, and touch panel. United States Patent, US 9952112 B2.
- [73] Verster A, Tung N, Ong WK, Sieu B. Development of an ultrasonic tourniquet system for surgical applications.
In: 2014 CMBEC37 Canadian Medical and Biological Engineering Society. Vancouver, British Columbia, Canada;
2014. pp. 1-4.
- [74] Szaluś-Jordanow O, Czopowicz M, Moroz A. Comparison of oscillometric, Doppler and invasive blood pressure
measurement in anesthetized goats. PLOS ONE May 2018; 13(5): e0197332.
- [75] France L, Vermillion M, Garrett CM. Comparison of direct and indirect methods of measuring arterial blood pressure
in healthy male Rhesus Macaques (Macaca mulatta). Journal of the American Association for Laboratory Animal
Science 2018; 57: 64-69.
- [76] Kao YH, Paul C, Wey CL. Towards maximizing the sensing accuracy of an cuffless, optical blood pressure sensor
using a high-order front-end fitler. Microsystem Technologies 2018; 24: 4621-4630.
- [77] Schönle PC. A Power efficient spectrophotometry & PPG integrated circuit for mobile medical instruments. PhD
Zürich, Switzerland, 2017.
- [78] Tu TY, Paul C, Chao P. Continuous blood pressure measurement based on a neural network scheme applied with
a cuffless sensor. Microsystem Technologies 2018; 24: 4539-4549.
- [79] Şentürk Ü, Yücedağ İ, Polat K. Cuff-less continuous blood pressure estimation from Electrocardiogram(ECG) and
Photoplethysmography (PPG) signals with artificial neural network. In: IEEE 2018 SIU 26th Signal Processing and
Communications Applications Conference; İzmir, Turkey; 2018. pp. 1-4.
- [80] Lo PWF, Li TXC, Wang J, Cheng J, Meng QHM. Continuous systolic and diastolic blood pressure estimation
utilizing Long Short-Term Memory Network. In: IEEE 2017 EMBC 39th Annual International Conference of the
IEEE Engineering in Medicine and Biology Society; Jeju Island, Korea; 2017. pp. 1853-1856.
- [81] Nabeel PM, Karthik S, Joseph J, Sivaprakasam M. Arterial blood pressure estimation from local pulse wave velocity
using dual-element photoplethysmograph probe. IEEE Transactions on Instrumentation and Measurement 2018; 67:
1399-1408.
- [82] Pflugradt M, Geissdoerfer K, Goernig M, Orglmeister R. A fast multimodal ectopic beat detection method applied
for blood pressure estimation based on pulse wave velocity measurements in wearable sensors. Sensors 2017; 17:
158.
- [83] Nathan V, Thomas SS, Jafari R. Smart watches for physiological monitoring: a case study on blood pressure
measurement. In: Nadin M (editors) Anticipation and Medicine. Cham, Switzerland: Springer, 2016, pp. 231-252.
- [84] Morris D, Saponas TS, Villar N. Wearable sensing band. United States Patent, US9848 825 B2.
- [85] Zhang Q, Zhou D, Zeng X. Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm
electrocardiogram and photoplethysmogram signals. BioMedical Engineering OnLine 2017; 16(1): 23.
- [86] Banet M, Dhillon M, McCombie D. Body-worn system for measuring continuous non-invasive blood pressure(cNIBP). United States Patent, US9668656B2.
- [87] Arakawa T, Sakakibara N, Kondo S. Development Of non-invasive steering-type blood pressure sensor for driver
state detection. International Journal of Innovative Computing 2018; 14: 1301–1310.
- [88] Arakawa T. Recent research and developing trends of wearable sensors for detecting blood pressure. Sensors 2018;
18: 2772.
- [89] Axelrod BW, Siemons AH. Blood pressure measurement device wearable by a patient. United States Patent
Application Publication, US 2018 / 0289271 A1.
- [90] Tang Z, Tamura T, Sekine M, Huang M. A chair–based Unobtrusive cuffless blood pressure monitoring system
based on pulse arrival time. IEEE Journal of Biomedical and Health Informatics 2017; 21: 1194–1205.
- [91] Tallgrena P, Vanhataloab S, Kailaa K, Voipio J. Evaluation of commercially available electrodes and gels for
recording of slow EEG potentials. Clinical Neurophysiology 2005; 116: 799-806.
- [92] Chi YM, Jung T P, Cauwenberghs G. Dry-contact and noncontact biopotential electrodes: Methodological review.
Biomedical Engineering 2010; 3: 106-119.
- [93] Griss P, Tolvanen-Laakso HK, Meriläinen P, Stemme G. Characterization of micromachined spiked biopotential
electrodes. IEEE Transactions On Biomedical Engineering 2002; 49: 597-604.
- [94] Meziane N, Webster JG, Attari M, Nimunkar AJ. Dry electrodes for electrocardiography. Physiological Measurement
2013; 34(9): 47-69.
- [95] Diker A, Cömert Z, Avcı E. A diagnostic model for identification of myocardial infarction from electrocardiography
signals. Journal of Science and Technology 2017; 7(2): 132-139.
- [96] Pola T, Vanhalai J. Textile electrodes in ECG measurement. In: 3rd International Conference on Intelligent Sensors
Sensor Networks and Information; Melbourne, Australia; 2007. pp. 635-639.
- [97] Marozas V, Petrenas A, Daukantas S, Lukosevicius A. A comparison of conductive textile-based and silver/silver
chloride gel electrodes in exercise electrocardiogram recordings. Journal of Electrocardiology 2011; 44: 189-194.
- [98] Roggan A, Friebel M, Dörschel K, Hahn A, Müller G. Optical properties of circulating human blood in the
wavelength range 400–2500 nm. Journal Of Biomedical Optics 1999; 4: 36-46.
- [99] Wood BR, McNaughton D. Raman excitation wavelength investigation of single red blood cells in vivo. Journal Of
Raman Spectroscopy 2002; 33: 517-523.
- [100] Foroughian F, Bauder CJ, Fathy AE, Theilmann PT. The wavelength selection for calibrating non-contact detection
of blood oxygen saturation using imaging photoplethysmography. In: 2018 USNC-URSI NRSM United States
National Committee of URSI National Radio Science Meeting; Colorado, USA; 2018. pp. 1-2.
- [101] Kao YH, Chao P, Hung Y, Wey CL. A new reflective PPG LED-PD sensor module for cuffless blood pressure
measurement at wrist artery. In: 2017 IEEE Sensors; Glasgow, UK; 2017. pp. 1-3.
- [102] Moço AV, Stuijk S, de Haan G. New insights into the origin of remote PPG signals in visible light and infrared.
Scientific Reports 2018; 8(1): 8501.
- [103] Chu CT, Ho CC, Chang CH, Ho MC. Non-invasive optical heart rate monitor base on one chip integration
microcontroller solution. In: 2017 ISNE 6th International Symposium on Next Generation Electronics; Keelung,
Taiwan; 2017. pp. 1-4.
- [104] Kalantar G, Mukhopadhyay SK, Marefat F, Mohseni P, Mohammadi A. Wake-Bpat: Wavelet-based adaptive
kalman filtering for blood pressure estimation via fusion of pulse arrival times. In: IEEE 2018 ICASSP International
Conference on Acoustics, Speech and Signal Processing; Calgary, Alberta, Canada; 2018. pp. 945-949.
- [105] Zhang Q, Chen X, Fang Z. Cuff-less blood pressure measurement using pulse arrival time and a Kalman fitler.
Journal of Micromechanics and Microengineering 2017; 27: 1-5.
- [106] Saleem S, Vucina D, Sarafis Z. Wavelet decomposition analysis is a clinically relevant strategy to evaluate cerebrovascular buffering of blood pressure after spinal cord injury. American Journal Physiology Heart Circulation
Physiology 2018; 314: 1108-1114.
- [107] Abderahman HN, Dajani HR, Bolic M, Groza VZ. An integrated blood pressure measurement system for suppression of motion artifacts. Computer Methods and Programs in Biomedicine 2017; 145: 1-10.
- [108] Mills E, O’Brien TK, Fortin J, Maier K. Device and method for the continuous non-invasive measurement of blood
pressure. United States Patent, US9615756B2.
- [109] Allen J, Murray A. Age-related changes in peripheral pulse timing characteristics at the ears, fingers and toes.
Journal of Human Hypertension 2002; 16: 711–717.
- [110] Cömert Z, Kocamaz AF. Open-access software for analysis of fetal heart rate signals. Biomedical Signal Processing
and Control 2018; 45: 98-108.
- [111] Diker A, Cömert Z, Avci E, Velappan S. Intelligent system based on Genetic Algorithm and support vector
machine for detection of myocardial infarction from ECG signals. In: IEEE 2018 SIU 26th Signal Processing
and Communications Applications; İzmir, Turkey; 2018; pp. 1-4.
- [112] Lee S, Park CH, Chang JH. Improved gaussian mixture regression based on pseudo feature generation using
bootstrap in blood pressure estimation. IEEE Transactions on Industrial Informatics 2016; 12: 2269-2280.
- [113] Miao F, Fu N, Zhang YT, Ding XR. A novel continuous blood pressure estimation approach based on data mining
techniques. IEEE Journal Of Biomedical And Health Informatics 2017; 21: 1730-1740.
- [114] Cömert Z, Kocamaz AF, Subha V. Prosnostic model based on imagebased time frequency features and genetic
algorithm for fetal hypoxia assessment. Computers in Biology and Medicine 2018; 99: 85-97.
- [115] Sanuki H, Fukui R, Inajima T, Warisawa S. Cuff-less calibration-free blood pressure estimation under ambulatory
environment using pulse wave velocity and photoplethysmogram signals. In: 2017 BIOSTEC 10th International
Joint Conference on Biomedical Engineering Systems and Technologies; Porto, Portugal; 2017. pp. 42-48.
- [116] Yoshioka M, Bounyong S. Regression-forests-based estimation of blood pressure using the pulse transit time
obtained by facial photoplethysmogram. In: 2017 IJCNN International Joint Conference on Neural Networks;
Anchorage, Alaska; 2017. pp. 3248-3253.
- [117] Januário LH, Ramos ACB, Souza PO. Relationship between upper arm muscle index and upper arm dimensions in
blood pressure measurement in symmetrical upper arms: Statistical and classification and regression tree analysis.
In: Rocha Á, Adeli H, Reis L, Costanzo S (editors) Trends and Advances in Information Systems and Technologies.
WorldCIST’18 2018. Advances in Intelligent Systems and Computing. Switzerland: Springer, Cham 2018, pp. 1178-
1187.
- [118] Kachuee M, Kiani MM, Mohammadzade H, Shabany M. Cuffless blood pressure estimation algorithms for continuous health-care monitoring. IEEE Transactions on Biomedical Engineering 2017; 64: 859–869.
- [119] Radha M, de Groot K, Rajaniz N, Wong CCP. Estimating blood pressure trends and the nocturnal dip from
photoplethysmography. Physiological measurement 2019; 40: 025006.
- [120] IEEE Standard for Wearable Cuffless Blood Pressure Measuring Devices, IEEE Std. 1708-2014, 2014.
- [121] Liu J, Cheng HM, Chen CH, Sung SH. Patient-specific oscillometric blood pressure measurement. IEEE Transactions on Biomedical Engineering 2016; 63: 1220-1228.
- [122] Hung CH, Bai YW, Tsai RY. Design of blood pressure measurement with a health management system for the
aged. IEEE Transactions on Consumer Electronics 2012; 58: 619-625.
- [123] Tanaka S, Gao S, Nogawa M, Yamakoshi KI. Noninvasive measurement of instantaneous, radial artery blood
pressure. IEEE Engineering in Medicine and Biology Magazine 2005; 24: 32-37.
- [124] Colak S, Isik C. Blood pressure estimation using neural networks. In: IEEE 2004 CIMSA lntenational Conference
an Computational lntelligence for Measurement Systems and Applications; Boston, MA, USA; 2004. pp. 21-25.
- [125] Forouzanfar M, Dajani HR, Groza VZ, Bolic M. Oscillometric blood pressure estimation using principal component
analysis and neuraln networks. In: IEEE 2009 TIC-STH Toronto International Conference Science and Technology
for Humanity; Toronto, Canada; 2009. pp. 981-986.
- [126] Forouzanfar M, Dajani HR, Groza VZ, Bolic M. Adaptive neuro-fuzzy inference system for oscillometric blood
pressure estimation. In: IEEE 2010 International Workshop on Medical Measurements and Applications; Bari,
Italy; 2010. pp. 125-129.
- [127] Forouzanfar M, Dajani HR, Groza VZ, Bolic M. Comparison of feed-forward neural network training algorithms
for oscillometric blood pressure estimation. In: 4th International Workshop on Soft Computing Applications; Arad,
Romenia; 2010. pp. 119-123.
- [128] Forouzanfar M, Dajani HR, Groza VZ, Bolic M. Feature-based neural network approach for oscillometric blood
pressure estimation. IEEE Transactions on Instrumentation and Measurement 2011; 60: 2786-2796.
- [129] Forouzanfar M, Dajani HR, Groza VZ, Bolic M. Model-based oscillometric blood pressure estimation. In: IEEE
2014 MeMeA International Symposium on Medical Measurements and Applications; Lisbon, Portugal; 2014. pp.
1-6.
- [130] Lee S, Chang JH. Oscillometric blood pressure estimation based on deep learning. IEEE Transactions On Industrial
Informatics 2017; 13: 461-472.
- [131] Pan F, He P, Liu C, Li T, Murray A et al. Variation of the Korotkoff stethoscope sounds during blood pressure
measurement: Analysis using a convolutional neural network. IEEE Journal Of Biomedical And Health Informatics
2017; 21: 1593-1598.
- [132] Lee S, Chang JH. Deep Boltzmann regression with mimic features for oscillometric blood pressure estimation.
IEEE Sensors Journal 2017; 17: 5982-5993.
- [133] Anisimov AA, Skorobogatova AI, Sutyagina AD. Implementation of neural networks for blood pressure measurement. In: IEEE 2018 EIConRus Conference of Russian Young Researchers in Electrical and Electronic Engineering;
Moscow and St. Petersburg, Russia; 2018. pp. 1190-1194.
- [134] Lee S, Rajan S, Jeon G, Chang JH, Dajani HR et al. Oscillometric blood pressure estimation by combining
nonparametric bootstrap with Gaussian mixture model. Computers in Biologyand Medicine 2017; 85: 112–124.
- [135] Narus S, Egbert T, Lee TK, Lu J, Westenskow D. Noninvasive blood pressure monitoring from the supraorbital
artery using an artificial neural network oscillometric algorithm. Journal of Clinical Monitoring and Computing
1995; 11: 289-297.
- [136] Lee CM, Zhang YT. Cuffless and noninvasive estimation of blood pressure based on a wavelet transform approach.
In: IEEE 2003 EMBS Asian-Pacific Conference on Biomedical Engineering; Kyoto, Japan; 2003. pp. 148-149.
- [137] Sola J, Proenca M, Ferrario D, Porchet JA. Noninvasive and nonocclusive blood pressure estimation via a chest
sensor. IEEE Transactions on Biomedical Engineering 2013; 60: 3505-3513.
- [138] Atomi K, Kawanaka H, Bhuiyan S, Oguri K. Cuffless blood pressure estimation based on data-oriented continuous
health monitoring system. Hindawi Computational and Mathematical Methods in Medicine 2017; 2017: 10. doi:
10.1155/2017/1803485
- [139] Esmaili A, Kachuee M, Shabany M. Nonlinear cuffless blood pressure estimation of healthy subjects using pulse
transit time and arrival time. IEEE Transactions on Instrumentation and Measurement 2017; 66: 3299-3308.
- [140] Lin WH, Wang H, Samuel OW, Li G. Using a new ppg indicator to increase the accuracy of ptt-based continuous
cuffless blood pressure estimation. In: IEEE 2017 EMBC 2017 39th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society; Jeju Island, Korea; 2017. pp. 738-741.
- [141] Dastjerdi AE, Kachuee M, Shabany M. Non-invasive blood pressure estimation using phonocardiogram. In: IEEE
2017 ISCAS International Symposium on Circuits and Systems; Maryland, USA; 2017. pp. 1-4.
- [142] Yoon YZ, Kang JM, Kwon Y, Park S. Cuff-less blood pressure estimation using pulse waveform analysis and pulse
arrival time. IEEE Journal of Biomedical and Health Informatics 2018; 22: 1068-1074.
- [143] Almahouzi A, Alnaser T, Tiraei S, Athavale Y, Krishnan S. An integrated biosignals wearable system for lowcost blood pressure monitoring. In: IEEE 2017 IHTC Canada International Humanitarian Technology Conference,
Toronto, Canada; 2017. pp. 16-20.
- [144] Li Y, Chen X, Zhang Y, Deng N. Noninvasive continuous blood pressure estimation with peripheral pulse transit
time. In: IEEE 2016 BioCAS Biomedical Circuits and Systems Conference; Shanghai, China; 2016. pp. 66-69.
- [145] Chen Y, Cheng S, Wang T, Ma T. Novel blood pressure estimation method using single photoplethysmography
feature. In: IEEE 2017 EMBC 39th Annual International Conference of the IEEE Engineering in Medicine and
Biology Society; Jeju Islan, Korea; 2017. pp. 1712-1715.
- [146] Xiao H, Butlin M, Qasem A. N-point moving average: a special generalized transfer function method for estimation
of central aortic blood pressure. IEEE Transactions on Biomedical Engineering 2018; 65: 1226-1234.
- [147] Miao F, Fu N, Ting Y. Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Techniques.
IEEE Journal of Biomedical and Health Informatics 2017; 21: 1730-1740.
- [148] Kachuee M, Kiani MM, Mohammadzade H, Shabany M. Cuff-less high-accuracy calibration-free blood pressure
estimation using pulse transit time. In: IEEE 2015 ISCAS International Symposium on Circuits and Systems;
Lisbon, Portugal; 2015. pp. 1006-1009.
- [149] Pan J, Zhang Y. Improved blood pressure estimation using photoplethysmography based on ensemble method. In:
ISPAN-FCST-ISCC 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th
International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium
of Creative Computing; Exeter, UK; 2017. pp. 105-111.
- [150] Xu J, Jiang J, Zhou H, Yan Z. A novel blood pressure estimation method combing pulse wave transit time model
and neural network model. In: IEEE 2017 EMBC 39th Annual International Conference of the IEEE Engineering
in Medicine and Biology Society; Jeju Island, Korea; 2017. pp. 2130-2133.
- [151] Kurylyak Y, Lamonaca F, Grimaldi D. A neural network-based method for continuous blood pressure estimation
from a ppg signal. In: IEEE 2013 I2MTC International Instrumentation and Measurement Technology Conference;
Minneapolis, MN, USA; 2013. pp. 208-283.
- [152] Sideris C, Kalantarian H, Nemati E, Sarrafzadeh M. Building continuous arterial blood pressure prediction models
using recurrent networks. In: IEEE 2016 SMARTCOMP International Conference on Smart Computing; Washington DC, USA; 2016. pp. 1-5.
- [153] Xiao H, Butlin M, Tanb I, Qasem A, Avolio AP. Estimation of pulse transit time from radial pressure waveform
alone by artificial neural network. IEEE Journal of Biomedical and Health Informatics 2018; 22: 1140-1147.
- [154] Pytel K, Nawarycz T, Drygas W. Anthropometric predictors and artificial neural networks in the diagnosis of
hypertension. In: 2015 FedCSIS Federated Conference on Computer Science and Information Systems; Lodz, Poland;
2015. pp. 287-290.
- [155] Wang L, Zhou W, Xing Y, Zhou X. A novel neural-network model for blood pressure estimation using photoplethesmography without electrocardiogram. Journal of Healthcare Engineering 2018; 2018: 1-9.
- [156] Lo FPW, Li CXT, Wang J. Continuous systolic and diastolic blood pressure estimation utilizing long short-term
memory network. In: IEEE 2017 EMBC 39th Annual International Conference of the IEEE Engineering in Medicine
and Biology Society; Jaju Island, Korea; 2017. pp. 1853-1856.
- [157] Li X, Wu S, Wang L. Blood pressure prediction via recurrent models with contextual layer. In: 2017 26th
International Conference on World Wide Web; Perth, Australia; 2017. pp. 685-693.
- [158] Şentürk Ü, Yücedağ İ, Polat K. Repetitive neural network (RNN) based blood pressure estimation using PPG and
ECG signals. In: IEEE 2018 ISMSIT 2nd International Conference on Multidisciplinary Studies and Innovative
Technologies; Ankara, Türkiye; 2018. pp. 1-4.
- [159] Liu M, Po LM, Fu H. Cuffless blood pressure estimation based on photoplethysmography signal and its second
derivative. International Journal of Computer Theory and Engineering 2017; 9: 202-206.
- [160] Xuab Z, Liuc J, Chenab X, Wangc Y, Zhao Z. Continuous blood pressure estimation based on multiple parameters
from eletrocardiogram and photoplethysmogram by back-propagation neural network. Computers in Industry 2017;
89: 50-59.
- [161] Schönle PC. Power efficient spectrophotometry & PPG integrated circuit for mobile medical instruments. PhD,
Eidgenössische Technische Hochschule, Zürich, Switzerland, 2017.
- [162] Tanveer S, Hasan K. Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using
waveform based ANN-LSTM network. Biomedical Signal Processing and Control 2019; 51: 382-392.