Roth, G. A. et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: Update From the GBD 2019 Study. J. Am. Coll. Cardiol. 76, 2982–3021 (2020).
Mensah George, A. et al. Global burden of cardiovascular diseases and risks, 1990–2022. JACC 82, 2350–2473 (2023).
Reed, G. W., Rossi, J. E. & Cannon, C. P. Acute myocardial infarction. Lancet 389, 197–210 (2017).
Mauro, C. et al. Acute heart failure: diagnostic–therapeutic pathways and preventive strategies—A Real-World Clinician’s Guide. J. Clin. Med. 12, 846 (2023).
Scheitz, J. F. et al. Stroke–heart syndrome: recent advances and challenges. J. Am. Heart Assoc. 11, e026528 (2022).
Fuchs, F. D. & Whelton, P. K. High blood pressure and cardiovascular disease. Hypertension 75, 285–292 (2020).
Schmidt, A. M. Diabetes Mellitus and cardiovascular disease. Arterioscler. Thromb. Vasc. Biol. 39, 558–568 (2019).
Ross, R. & Harker, L. Hyperlipidemia and Atherosclerosis. Science 193, 1094–1100 (1976).
Petrie, J. R., Guzik, T. J. & Touyz, R. M. Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms. Can. J. Cardiol. 34, 575–584 (2018).
Zoccali, C. et al. Cardiovascular complications in chronic kidney disease: a review from the European Renal and Cardiovascular Medicine Working Group of the European Renal Association. Cardiovasc. Res. 119, 2017–2032 (2023).
Buddeke, J. et al. Comorbidity in patients with cardiovascular disease in primary care: a cohort study with routine healthcare data. Br. J. Gen. Pract. 69, e398 (2019).
Chen, S. et al. Flexible wearable sensors for cardiovascular health monitoring. Adv. Healthc. Mater. 10, 2100116 (2021).
Xue, Z., Gai, Y., Wu, Y., liu, Z. & Li, Z. Wearable mechanical and electrochemical sensors for real-time health monitoring. Commun. Mater. 5, 211 (2024).
Lin, J. et al. Wearable sensors and devices for real-time cardiovascular disease monitoring. Cell Rep. Phys. Sci. 2, 100541 (2021).
Fortin, J. et al. A novel art of continuous noninvasive blood pressure measurement. Nat. Commun. 12, 1387 (2021).
Williams, G. J. et al. Wearable technology and the cardiovascular system: the future of patient assessment. Lancet Digit. Health 5, e467–e476 (2023).
Lu, H., Feng, X. & Zhang, J. Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN. BMC Med. Inform. Decis. Mak. 24, 194 (2024).
Hughes, A., Shandhi, M. M. H., Master, H., Dunn, J. & Brittain, E. Wearable Devices in Cardiovascular Medicine. Circ. Res. 132, 652–670 (2023).
Duan, H. et al. Wearable electrochemical biosensors for advanced healthcare monitoring. Adv. Sci. 12, 2411433 (2024).
Chowdhury, A. H., Jafarizadeh, B., Baboukani, A. R., Pala, N. & Wang, C. Monitoring and analysis of cardiovascular pulse waveforms using flexible capacitive and piezoresistive pressure sensors and machine learning perspective. Biosens. Bioelectron. 237, 115449 (2023).
Deng, Y. et al. Large-area stretchable dynamic 18-lead ECG monitoring patch integrated with deep learning for cardiovascular disease diagnosis. Cell Rep. Phys. Sci. 5, 102077 (2024).
Zhu, S., Liu, S., Jing, X., Yang, Y. & She, C. Innovative approaches in imaging photoplethysmography for remote blood oxygen monitoring. Sci. Rep. 14, 19144 (2024).
Mansoor, O. & Garcia, J. Clinical use of blood flow analysis through 4D-flow imaging in aortic valve disease. J. Cardiovasc. Dev. Dis. 10, 251 (2023).
Huang, H., Wu, R. S., Lin, M. & Xu, S. Emerging wearable ultrasound technology. IEEE Trans. Ultrason Ferroelectr. Freq. Control 71, 713–729 (2024).
Tang, L., Yang, J., Wang, Y. & Deng, R. Recent advances in cardiovascular disease biosensors and monitoring technologies. ACS Sens. 8, 956–973 (2023).
Choi, Y. S. et al. Fully implantable and bioresorbable cardiac pacemakers without leads or batteries. Nat. Biotechnol. 39, 1228–1238 (2021).
Ben-Shlomo, Y. et al. Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects. J. Am. Coll. Cardiol. 63, 636–646 (2014).
Kore, A. C. et al. Implications of pulse wave velocity and central pulse pressure in heart failure with reduced ejection fraction. Blood Press. 33, 2359932 (2024).
Kim, H. L. & Kim, S. H. Pulse wave velocity in Atherosclerosis. Front. Cardiovasc. Med. 6, 41 (2019).
Sutton-Tyrrell, K. et al. Elevated aortic pulse wave velocity, a marker of arterial stiffness, predicts cardiovascular events in well-functioning older adults. Circulation 111, 3384–3390 (2005).
Willum Hansen, T. et al. Prognostic value of aortic pulse wave velocity as Index of arterial stiffness in the general population. Circulation 113, 664–670 (2006).
O’Rourke, M. F., Staessen, J. A., Vlachopoulos, C., Duprez, D. & Plante, G. E. Clinical applications of arterial stiffness; definitions and reference values. Am. J. Hypertens. 15, 426–444 (2002).
Pilz, N. et al. Pulse wave velocity: methodology, clinical applications, and interplay with heart rate variability. RCM 25, 266 (2024).
Sacha, J., Sobon, J., Sacha, K. & Barabach, S. Heart rate impact on the reproducibility of heart rate variability analysis. Int J. Cardiol. 168, 4257–4259 (2013).
Schiweck, C., Piette, D., Berckmans, D., Claes, S. & Vrieze, E. Heart rate and high frequency heart rate variability during stress as biomarker for clinical depression. A systematic review. Psychol. Med. 49, 200–211 (2019).
Kemp, A. H. & Quintana, D. S. The relationship between mental and physical health: insights from the study of heart rate variability. Int J. Psychophysiol. 89, 288–296 (2013).
Mather, M. & Thayer, J. How heart rate variability affects emotion regulation brain networks. Curr. Opin. Behav. Sci. 19, 98–104 (2018).
Tai, Y. L., Marshall, E., Glasgow, A. & Kingsley, J. D. Bench Press With and Without Blood Flow Restriction on Hemodynamics and Pulse Wave Reflection: 292 Board #113 May 31 11: 00 AM – 12: 30 PM. Med. Sci. Sports Exerc. 49, 64–65 (2017).
Castaneda, D., Esparza, A., Ghamari, M., Soltanpur, C. & Nazeran, H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int. J. Biosens. Bioelectron. 4, 195–202 (2018).
Tamura, T., Maeda, Y., Sekine, M. & Yoshida, M. Wearable photoplethysmographic sensors—past and present. Electronics 3, 282–302 (2014).
Huang, J. et al. A high-performance solution-processed organic photodetector for near-infrared sensing. Adv. Mater. 32, e1906027 (2020).
Song, L. et al. High-Barrier-Height Ti3C2Tx/Si microstructure Schottky junction-based self-powered photodetectors for photoplethysmographic monitoring. Adv. Mater. Technol. 7, 2200555 (2022).
Arberet, S. et al. in 40th Annual Meeting on Computing in Cardiology (CinC), Vol. 40 935-938 (Zaragoza, SPAIN; 2013).
Turakhia, M. P. et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am. Heart J. 207, 66–75 (2019).
Yokota, T. et al. Ultraflexible organic photonic skin. Sci. Adv. 2, e1501856 (2016).
Wu, W., Li, L., Li, Z., Sun, J. & Wang, L. Extensible integrated system for real-time monitoring of cardiovascular physiological signals and limb health. Adv. Mater. 35, e2304596 (2023).
Li, S., Liu, L., Wu, J., Tang, B. & Li, D. Comparison and noise suppression of the transmitted and reflected photoplethysmography signals. Biomed. Res. Int. 2018, 4523593 (2018).
Khan, Y. et al. A flexible organic reflectance oximeter array. Proc. Natl. Acad. Sci. 115, E11015–E11024 (2018).
Ray, D., Collins, T., Woolley, S. & Ponnapalli, P. V. S. A review of wearable multi-wavelength Photoplethysmography. Ieee Rev. Biomed. Eng. 16, 136–151 (2023).
Brüser, C., Antink, C. H., Wartzek, T., Walter, M. & Leonhardt, S. Ambient and unobtrusive cardiorespiratory monitoring techniques. IEEE Rev. Biomed. Eng. 8, 30–43 (2015).
Lee, J., Kim, M., Park, H.-K. & Kim, I. Y. Motion artifact reduction in wearable Photoplethysmography based on multi-channel sensors with multiple wavelengths. Sensors 20, 1493 (2020).
Park, J., Seok, H. S., Kim, S. S. & Shin, H. Photoplethysmogram analysis and applications: an integrative review. Front. Physiol. 12, 808451 (2022).
Tamura, T. Current progress of photoplethysmography and SPO2 for health monitoring. Biomed. Eng. Lett. 9, 21–36 (2019).
Sen, C. K. Wound healing essentials: let there be oxygen. Wound Repair Regen. 17, 1–18 (2009).
Boas, D. A. & Franceschini, M. A. Haemoglobin oxygen saturation as a biomarker: the problem and a solution. Philos. Trans. A Math. Phys. Eng. Sci. 369, 4407–4424 (2011).
Yudovsky, D., Nouvong, A., Schomacker, K. & Pilon, L. Assessing diabetic foot ulcer development risk with hyperspectral tissue oximetry. J. Biomed. Opt. 16, 026009 (2011).
Lee, H. et al. Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch. Sci. Adv. 4, eaas9530 (2018).
Lee, H. S. et al. Fiber-based quantum-dot pulse oximetry for wearable health monitoring with high wavelength selectivity and photoplethysmogram sensitivity. npj Flex. Electron. 7, 15 (2023).
Franklin, D. et al. Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography. Nat. Biomed. Eng. 7, 1229–1241 (2023).
Jinno, H. et al. Self-powered ultraflexible photonic skin for continuous bio-signal detection via air-operation-stable polymer light-emitting diodes. Nat. Commun. 12, 2234 (2021).
Sun, L. et al. All-solution-processed ultraflexible wearable sensor enabled with universal trilayer structure for organic optoelectronic devices. Sci. Adv. 10, eadk9460 (2024).
Lovinsky, L. S. Urgent problems of metrological assurance of optical pulse oximetry. IEEE Trans. Instrum. Meas. 55, 869–875 (2006).
Kaur, B., Kumar, S. & Kaushik, B. K. Novel wearable optical sensors for vital health monitoring systems-a review. Biosensors 13, 181 (2023).
Preejith, S. P., Alex, A., Joseph, J. & Sivaprakasam, M. in 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 1-6 (2016).
Xutian, S., Zhang, J. & Louise, W. New exploration and understanding of traditional Chinese medicine. Am. J. Chin. Med. 37, 411–426 (2009).
Meng, K. et al. Wearable pressure sensors for pulse wave monitoring. Adv. Mater. 34, 2109357 (2022).
Xie, H. et al. Dual-band laser selective etching for stretchable and strain interference-free pressure sensor arrays. Adv. Funct. Mater. 34, 2401532 (2024).
Amjadi, M., Kyung, K.-U., Park, I. & Sitti, M. Stretchable, skin-mountable, and wearable strain sensors and their potential applications: a review. Adv. Funct. Mater. 26, 1678–1698 (2016).
Li, G., Chen, D., Li, C., Liu, W. & Liu, H. Engineered microstructure derived hierarchical deformation of flexible pressure sensor induces a supersensitive piezoresistive property in broad pressure range. Adv. Sci. 7, 2000154 (2020).
Peng, S. H., Blanloeuil, P., Wu, S. Y. & Wang, C. H. Rational design of ultrasensitive pressure sensors by tailoring microscopic features. Adv. Mater. Interfaces 5, 1800403 (2018).
Gao, Y. et al. Laser micro-structured pressure sensor with modulated sensitivity for electronic skins. Nanotechnology 30, 325502 (2019).
Lee, Y. et al. Bioinspired gradient conductivity and stiffness for ultrasensitive electronic skins. ACS Nano 15, 1795–1804 (2021).
Baek, S. et al. Spatiotemporal measurement of arterial pulse waves enabled by wearable Active-Matrix Pressure Sensor Arrays. ACS Nano 16, 368–377 (2022).
Qin, J. et al. Flexible and stretchable capacitive sensors with different microstructures. Adv. Mater. 33, 2008267 (2021).
Yang, J. C. et al. Microstructured porous pyramid-based ultrahigh sensitive pressure sensor insensitive to strain and temperature. ACS Appl. Mater. Interfaces 11, 19472–19480 (2019).
Maniraman, P. & Chitra, L. in 2014 IEEE National Conference on Emerging Trends In New & Renewable Energy Sources And Energy Management (NCET NRES EM) 195−199 (2014).
Lv, C. et al. Ultrasensitive linear capacitive pressure sensor with wrinkled microstructures for tactile perception. Adv. Sci. 10, e2206807 (2023).
Su, Y. et al. Muscle fibers inspired high-performance piezoelectric textiles for wearable physiological monitoring. Adv. Funct. Mater. 31, 2010962 (2021).
Ruth, S. R. A. et al. Rational design of capacitive pressure sensors based on pyramidal microstructures for specialized monitoring of biosignals. Adv. Funct. Mater. 30, 1903100 (2020).
Yang, R. et al. Iontronic pressure sensor with high sensitivity over ultra-broad linear range enabled by laser-induced gradient micro-pyramids. Nat. Commun. 14, 2907 (2023).
Ausanio, G. et al. Magnetoelastic sensor application in civil buildings monitoring. Sens. Actuators A: Phys. 123-124, 290–295 (2005).
Zhao, X. et al. Soft fibers with magnetoelasticity for wearable electronics. Nat. Commun. 12, 6755 (2021).
Wu, Y. L., Ma, Y. L., Zheng, H. Y. & Ramakrishna, S. Piezoelectric materials for flexible and wearable electronics: A review. Mater. Design 211, 110164 (2021).
Chu, Y. et al. Human pulse diagnosis for medical assessments using a wearable piezoelectret sensing system. Adv. Funct. Mater. 28, 1803413 (2018).
Huang, Q. et al. Ion gradient induced self-powered flexible pressure sensor. Chem. Eng. J. 490, 151660 (2024).
Zhou, Y. K. et al. Triboelectric nanogenerator based self-powered sensor for artificial intelligence. Nano Energy 84, 105887 (2021).
Meng, K. et al. Kirigami-Inspired pressure sensors for wearable dynamic cardiovascular monitoring. Adv. Mater. 34, e2202478 (2022).
Fang, Y. et al. Ambulatory cardiovascular monitoring via a machine-learning-assisted textile triboelectric sensor. Adv. Mater. 33, e2104178 (2021).
Wang, C. et al. An advanced strategy to enhance TENG output: reducing triboelectric charge decay. Adv. Mater. 35, 2209895 (2023).
Liu, X., Wang, H., Li, Z. & Qin, L. Deep learning in ECG diagnosis: A review. Knowl. -Based Syst. 227, 107187 (2021).
Kaplan Berkaya, S. et al. A survey on ECG analysis. Biomed. Signal Process. Control 43, 216–235 (2018).
Acharya, U. R. et al. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci. 405, 81–90 (2017).
Elhaj, F. A., Salim, N., Harris, A. R., Swee, T. T. & Ahmed, T. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput. Methods Prog. Biomed. 127, 52–63 (2016).
Sannino, G. & De Pietro, G. A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Future Gener. Comput. Syst. – Int. J. Escience 86, 446–455 (2018).
Fakhri, Y. et al. Electrocardiographic scores of severity and acuteness of myocardial ischemia predict myocardial salvage in patients with anterior ST-segment elevation myocardial infarction. J. Electrocardiol. 51, 195–202 (2018).
Quesnel, P. X., Chan, A. D. C., Yang, H. & Ieee in 9th IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA) 308−312 (Univ Lisbon, ISCTE, Lisbon, PORTUGAL; 2014).
ter Haar, C. C. & Swenne, C. A. Post hoc labeling an acute ECG as ischemic or non-ischemic based on clinical data: A necessary challenge. J. Electrocardiol. 81, 75–79 (2023).
Acharya, U. R. et al. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415, 190–198 (2017).
Jahmunah, V., Ng, E. Y. K., Tan, R. S., Oh, S. L. & Acharya, U. R. Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals. Comput. Biol. Med. 146, 105550 (2022).
Al-Khatib, S. M. et al. 2017 AHA/ACC/HRS Guideline for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. Circulation 138, E272–E391 (2018).
Yao, Q. H., Wang, R. X., Fan, X. M., Liu, J. K. & Li, Y. Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network. Inf. Fusion 53, 174–182 (2020).
Rosenberg, E. S. et al. Association of Treatment With Hydroxychloroquine or Azithromycin With In-Hospital Mortality in Patients With COVID-19 in New York State. J. Am. Med. Assoc. 323, 2493–2502 (2020).
Hutchins, L. M., Temple, J. D. & Hilmas, E. Impact of Pharmacist Intervention on Electrocardiogram Monitoring of Pediatric Patients on Multiple QTc Interval–Prolonging Medications. J. Pediatr. Pharmacol. Ther. 22, 399–405 (2017).
Ribeiro, A. H. et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat. Commun. 11, 1760 (2020).
Zhang, T. et al. Flexible electronics for cardiovascular healthcare monitoring. Innovation 4, 100485 (2023).
Cheng, S. et al. Ultrathin Hydrogel Films toward Breathable Skin-Integrated Electronics. Adv. Mater. 35, e2206793 (2023).
Buzzacott, P., Edelson, C., Bennett, C. M. & Denoble, P. J. Risk factors for cardiovascular disease among active adult US scuba divers. Eur. J. Prev. Cardiol. 25, 1406–1408 (2018).
Ji, S. et al. Water-resistant conformal hybrid electrodes for aquatic endurable electrocardiographic monitoring. Adv. Mater. 32, e2001496 (2020).
Lu, Y. et al. Stretchable graphene–hydrogel interfaces for wearable and implantable bioelectronics. Nat. Electron. 7, 51–65 (2023).
Zhang, L. et al. Fully organic compliant dry electrodes self-adhesive to skin for long-term motion-robust epidermal biopotential monitoring. Nat. Commun. 11, 4683 (2020).
Kim, D. W. et al. Highly Permeable Skin Patch with Conductive Hierarchical Architectures Inspired by Amphibians and Octopi for Omnidirectionally Enhanced Wet Adhesion. Adv. Funct. Mater. 29, 1807614 (2019).
Wannenburg, J., Malekian, R. & Hancke, G. P. Wireless capacitive-based ECG sensing for feature extraction and mobile health monitoring. IEEE Sens. J. 18, 6023–6032 (2018).
Pallas-Areny, R. Comments on “Capacitive Biopotential Measurement for Electrophysiological Signal Acquisition: A review. IEEE Sens. J. 17, 2607–2609 (2017).
Wang, T.-W., Zhang, H. & Lin, S.-F. Influence of capacitive coupling on high-fidelity non-contact ECG measurement. IEEE Sens. J. 20, 9265–9273 (2020).
Lopez, A. & Richardson, P. C. Capacitive electrocardiographic and bioelectric electrodes. IEEE Trans. Biomed. Eng. BME-16, 99–99 (1969).
Wang, K. et al. Noncontact in bed measurements of electrocardiogram using a capacitively coupled electrode array based on flexible circuit board. IEEE Trans. Instrum. Meas. 72, 1–14 (2023).
Chen, H.-Y., Lin, B.-S., Yang, S.-R., Chang, W.-T. & Lin, B.-S. Design of automatic adjustment noncontact sensing smart clothing. IEEE Sens. J. 24, 26378–26387 (2024).
Muntner, P. et al. Measurement of blood pressure in humans: A scientific statement from the American Heart Association. Hypertension 73, E35–E66 (2019).
Burnier, M. & Egan, B. M. Adherence in Hypertension. Circ. Res. 124, 1124–1140 (2019).
Unger, T. et al. 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension 75, 1334–1357 (2020).
Kjeldsen, S. E. Hypertension and cardiovascular risk: General aspects. Pharmacol. Res. 129, 95–99 (2018).
Whelton, P. K. et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NM CNA 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 71, E13–E115 (2018).
Lurbe, E. et al. 2016 European Society of Hypertension guidelines for the management of high blood pressure in children and adolescents. J. Hypertens. 34, 1887–1920 (2016).
Williams, B. et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur. Heart J. 39, 3021–302 (2018).
Chen, W. W. et al. China cardiovascular diseases report 2015: a summary. J. Geriatr. Cardiol. 14, 1–10 (2017).
Drzewiecki, G., Hood, R. & Apple, H. Theory of the oscillometric maximum and the systolic and diastolic detection ratios. Ann. Biomed. Eng. 22, 88–96 (1994).
Alpert, B. S., Quinn, D. & Gallick, D. Oscillometric blood pressure: a review for clinicians. J. Am. Soc. Hypertens. 8, 930–938 (2014).
Yamakoshi, K. I., Shimazu, H. & Togawa, T. Indirect measurement of instantaneous arterial blood pressure in the human finger by the vascular unloading technique. IEEE Trans. Bio-Med. Eng. 27, 150–155 (1980).
Fortin, J. et al. Continuous non-invasive blood pressure monitoring using concentrically interlocking control loops. Comput. Biol. Med. 36, 941–957 (2006).
Lin, W. H. et al. Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features. Biomed. Signal Process. Control 63, 102198 (2021).
Elgendi, M. et al. The use of photoplethysmography for assessing hypertension. Npj Digit. Med. 2, 60 (2019).
Safar, M. E. et al. Central pulse pressure and mortality in end-stage renal disease. Hypertension 39, 735–738 (2002).
McEniery, C. M., Cockcroft, J. R., Roman, M. J., Franklin, S. S. & Wilkinson, I. B. Central blood pressure: current evidence and clinical importance. Eur. Heart J. 35, 1719–1725 (2014).
Ding, F. H. et al. Validation of the noninvasive assessment of central blood pressure by the SphygmoCor and Omron devices against the invasive catheter measurement. Am. J. Hypertens. 24, 1306–1311 (2011).
Wang, C. et al. Monitoring of the central blood pressure waveform via a conformal ultrasonic device. Nat. Biomed. Eng. 2, 687–695 (2018).
Zhou, S. et al. Clinical validation of a wearable ultrasound sensor of blood pressure. Nat. Biomed. Eng. 9, 865–881 (2024).
Rodriguez-Molares, A., Fatemi, A., Lovstakken, L. & Torp, H. Specular Beamforming. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 64, 1285–1297 (2017).
Mauldin, F. W., Owen, K., Hossack, J. A. & Ieee in IEEE International Ultrasonics Symposium (IUS) 366-369 (Dresden, GERMANY; 2012).
Chun, K.-Y., Son, Y. J., Jeon, E.-S., Lee, S. & Han, C.-S. A Self-Powered Sensor Mimicking Slow- and Fast-Adapting Cutaneous Mechanoreceptors. Adv. Mater. 30, 1706299 (2018).
Min, S. et al. Clinical validation of a wearable piezoelectric blood-pressure sensor for continuous health monitoring. Adv. Mater. 35, e2301627 (2023).
Dai, L., Zhang, J., Li, C., Zhou, C. & Li, S. Multi-label feature selection with application to TCM state identification. Concurr. Comput.: Pract. Exp. 31, e4634 (2019).
Zhao, L. et al. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula. J. Ethnopharmacol. 309, 116306 (2023).
Yi, Z. et al. Piezoelectric dynamics of arterial pulse for wearable continuous blood pressure monitoring. Adv. Mater. 34, e2110291 (2022).
Wang, X. et al. Traditional Chinese Medicine (TCM)-inspired fully printed soft pressure sensor array with self-adaptive pressurization for highly reliable individualized long-term pulse diagnostics. Adv. Mater. 37, 2410312 (2024).
Mukkamala, R. et al. Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE Trans. Biomed. Eng. 62, 1879–1901 (2015).
Cartlidge, P. H., Fox, P. E. & Rutter, N. The scars of newborn intensive care. Early Hum. Dev. 21, 1–10 (1990).
Lund, C. Medical adhesives in the NICU. Newborn Infant Nurs. Rev. 14, 160–165 (2014).
Chung, H. U. et al. Binodal, wireless epidermal electronic systems with in-sensor analytics for neonatal intensive care. Science 363, 6430 (2019).
Xu, H. et al. Flexible Organic/Inorganic Hybrid Near-Infrared Photoplethysmogram Sensor for Cardiovascular Monitoring. Adv. Mater. 29, 1700975 (2017).
Payne, R. A., Symeonides, C. N., Webb, D. J. & Maxwell, S. R. J. Pulse transit time measured from the ECG: an unreliable marker of beat-to-beat blood pressure. J. Appl. Physiol. 100, 136–141 (2006).
Patil, S. & Choudhari, P. in Proceedings of the Third International Symposium on Women in Computing and Informatics 406–411 (Association for Computing Machinery, Kochi, India; 2015).
Huynh, T. H., Jafari, R. & Chung, W. Y. Noninvasive Cuffless Blood Pressure Estimation Using Pulse Transit Time and Impedance Plethysmography. IEEE Trans. Biomed. Eng. 66, 967–976 (2019).
Saugel, B., Bendjelid, K., Critchley, L. A. H. & Scheeren, T. W. L. Journal of Clinical Monitoring and Computing 2017 end of year summary: cardiovascular and hemodynamic monitoring. J. Clin. Monit. Comput. 32, 189–196 (2018).
Bissell, M. M. et al. 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update. J. Cardiovasc. Magn. Reson. 25 (2023).
Green, D. J., Hopman, M. T. E., Padilla, J., Laughlin, M. H. & Thijssen, D. H. J. Vascular Adaptation to Exercise in Humans: Role of Hemodynamic Stimuli. Physiol. Rev. 97, 495–528 (2017).
Neyra, N. R. et al. Change in access blood flow over time predicts vascular access thrombosis. Kidney Int. 54, 1714–1719 (1998).
Hope, M. D., Sedlic, T. & Dyverfeldt, P. Cardiothoracic magnetic resonance flow imaging. J. Thorac. Imaging 28, 217–230 (2013).
Swartz, W. M., Jones, N. F., Cherup, L. & Klein, A. Direct monitoring of microvascular anastomoses with the 20-MHz ultrasonic Doppler probe: an experimental and clinical study. Plast. Reconstr. Surg. 81, 149–161 (1988).
Bill, T. J., Foresman, P. A., Rodeheaver, G. T. & Drake, D. B. Fibrin sealant: a novel method of fixation for an implantable ultrasonic microDoppler probe. J. Reconstr. Microsurg 17, 257–262 (2001).
Smit, J. M. et al. Post operative monitoring of microvascular breast reconstructions using the implantable Cook-Swartz doppler system: a study of 145 probes & technical discussion. J. Plast. Reconstr. Aesthet. Surg. 62, 1286–1292 (2009).
Chad Webb, R., Krishnan, S. & Rogers, J. A. in Stretchable Bioelectronics for Medical Devices and Systems. (eds. J. A. Rogers, R. Ghaffari & D.-H. Kim) 117-132 (Springer International Publishing, Cham; 2016).
Thalayasingam, S. & Delpy, D. T. Thermal clearance blood flow sensor–sensitivity, linearity and flow depth discrimination. Med Biol. Eng. Comput 27, 394–398 (1989).
Webb, R. C. et al. Epidermal devices for noninvasive, precise, and continuous mapping of macrovascular and microvascular blood flow. Sci. Adv. 1, e1500701 (2015).
Webb, R. C. et al. Ultrathin conformal devices for precise and continuous thermal characterization of human skin. Nat. Mater. 12, 938–944 (2013).
Krishnan, S. R. et al. Epidermal electronics for noninvasive, wireless, quantitative assessment of ventricular shunt function in patients with hydrocephalus. Transl. Med. 10, 465 (2018).
Tian, Y. et al. Self-adaptive epidermal blood flow sensor for high-flux vascular access monitoring of hemodialysis patients. npj Flexible Electron. 8, 62 (2024).
Wang, F. et al. Flexible Doppler ultrasound device for the monitoring of blood flow velocity. Sci. Adv. 7, eabi9283 (2021).
Jin, H. et al. A flexible optoacoustic blood ‘stethoscope’ for noninvasive multiparametric cardiovascular monitoring. Nat. Commun. 14, 4692 (2023).
Awan, M. A., Ahmed, M. & Wang, B. A Laser Doppler blood flow measurement system with a 151.4 dBΩ gain and 0.05% nonlinearity for PAD patients. IEEE Sens. J. 22, 14196–14204 (2022).
Iwasaki, W. et al. Detection of site-specific blood flow variation in humans during running by a wearable Laser Doppler Flowmeter. Sensors 15, 25507–25519 (2015).
Wang, C. et al. Continuous monitoring of deep-tissue haemodynamics with stretchable ultrasonic phased arrays. Nat. Biomed. Eng. 5, 749–758 (2021).
Gupta, S., Haiat, G., Laporte, C. & Belanger, P. Effect of the acoustic impedance mismatch at the bone-soft tissue interface as a function of frequency in transcranial ultrasound: a simulation and in vitro experimental study. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 68, 1653–1663 (2021).
Jin, Y. Y., Yin, Y. G., Li, C. Y., Liu, H. Y. & Shi, J. H. Non-invasive monitoring of human health by photoacoustic spectroscopy. Sensors 22, 1155 (2022).
Park, B., Oh, D., Kim, J. & Kim, C. Functional photoacoustic imaging: from nano- and micro- to macro-scale. Nano Converg. 10, 29 (2023).
Saeed, M., Van, T. A., Krug, R., Hetts, S. W. & Wilson, M. W. Cardiac MR imaging: current status and future direction. Cardiovasc. Diagn. Ther. 5, 290–310 (2015).
Di Carli, M. F., Geva, T. & Davidoff, R. The future of cardiovascular imaging. Circulation 133, 2640–2661 (2016).
Wang, Y.-R. et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat. Med. 30, 1471–1480 (2024).
Gulsin, G. S., McVeigh, N., Leipsic, J. A. & Dodd, J. D. Cardiovascular CT and MRI in 2020: Review of Key Articles. Radiology 301, 263–277 (2021).
Kyung, D., Jo, K., Choo, J., Lee, J. & Choi, E. in ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1–5 (2023).
van der Wall, E. E., Vliegen, H. W., de Roos, A. & Bruschke, A. V. G. Magnetic resonance imaging in coronary artery disease. Circulation 92, 2723–2739 (1995).
Aly, I. et al. Cardiac ultrasound: An anatomical and clinical review. Transl. Res. Anat. 22, 100083 (2021).
Levine, B. D., Motamedi, K. & Seeger, L. L. Imaging of the shoulder: a comparison of MRI and ultrasound. Curr. Sports Med. Rep. 11, 239–243 (2012).
Hu, H. et al. A wearable cardiac ultrasound imager. Nature 613, 667–675 (2023).
Hu, H. et al. Stretchable ultrasonic arrays for the three-dimensional mapping of the modulus of deep tissue. Nat. Biomed. Eng. 7, 1321–1334 (2023).
Wang, C. et al. Bioadhesive ultrasound for long-term continuous imaging of diverse organs. Science 377, 517–523 (2022).
Iribarren, C. et al. Breast Arterial Calcification: a Novel Cardiovascular Risk Enhancer Among Postmenopausal Women. Circulation: Cardiovasc. Imaging 15, e013526 (2022).
Tegnér, J., Skogsberg, J. & Björkegren, J. Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits. J. Lipid Res. 48, 267–277 (2007).
Zhou, S. et al. Transcranial volumetric imaging using a conformal ultrasound patch. Nature 629, 810–818 (2024).
Du, W. et al. Conformable ultrasound breast patch for deep tissue scanning and imaging. Sci. Adv. 9, 30, eadh5325 (2023).
Zhang, L. et al. A conformable phased-array ultrasound patch for bladder volume monitoring. Nat. Electron. 7, 77–90 (2024).
Lin, M. et al. A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects. Nat. Biotechnol. 42, 448–457 (2024).
Di Mario, C. et al. Role of continuous glucose monitoring in diabetic patients at high cardiovascular risk: an expert-based multidisciplinary Delphi consensus. Cardiovasc Diabetol. 21, 164 (2022).
Picconi, F., Di Flaviani, A., Malandrucco, I., Giordani, I. & Frontoni, S. Impact of glycemic variability on cardiovascular outcomes beyond glycated hemoglobin. Evidence and clinical perspectives. Nutr., Metab. Cardiovasc. Dis. 22, 691–696 (2012).
Belli, M. et al. Glucose variability: a new risk factor for cardiovascular disease. Acta Diabetol. 60, 1291–1299 (2023).
Priyadarshini R, G. & Narayanan, S. Analysis of blood glucose monitoring – a review on recent advancements and future prospects. Multimed. Tools Appl. 83, 58375–58419 (2024).
Bellido, V., Freckman, G., Pérez, A. & Galindo, R. J. Accuracy and potential interferences of continuous glucose monitoring sensors in the hospital. Endocr. Pract. 29, 919–927 (2023).
Galant, A. L., Kaufman, R. C. & Wilson, J. D. Glucose: Detection and analysis. Food Chem. 188, 149–160 (2015).
Dinh, A., Miertschin, S., Young, A. & Mohanty, S. D. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med. Inform. Decis. Mak. 19, 211 (2019).
Ying, L. P. et al. Continuous glucose monitoring data for artificial intelligence-based predictive glycemic event: A potential aspect for diabetic care. Int. J. Diabetes Dev. Countr. 45, 272–287(2024).
Funtanilla, V. D., Candidate, P., Caliendo, T. & Hilas, O. Continuous Glucose Monitoring: A Review of Available Systems. P t 44, 550–553 (2019).
Zeng, Y. et al. Colloidal crystal microneedle patch for glucose monitoring. Nano Today 35, 100984 (2020).
Lee, H., Hong, Y. J., Baik, S., Hyeon, T. & Kim, D.-H. Enzyme-based glucose sensor: from invasive to wearable device. Adv. Healthc. Mater. 7, 1701150 (2018).
Chen, C. et al. 3D-printed flexible microfluidic health monitor for in situ sweat analysis and biomarker detection. ACS Sens. 9, 3212–3223 (2024).
Niu, J. et al. A fully elastic wearable electrochemical sweat detection system of tree-bionic microfluidic structure for real-time monitoring. Small 20, 2306769 (2024).
Bandodkar, A. J. et al. Battery-free, skin-interfaced microfluidic/electronic systems for simultaneous electrochemical, colorimetric, and volumetric analysis of sweat. Sci. Adv. 5, eaav3294 (2019).
Ko, A. & Liao, C. Salivary glucose measurement: A holy ground for next generation of non-invasive diabetic monitoring. Hybrid. Adv. 3, 100052 (2023).
Giaretta, J. et al. Glucose Sensing in Saliva. Adv. Sens. Res. 3, 2400065 (2024).
Kim, J., Campbell, A. S. & Wang, J. Wearable non-invasive epidermal glucose sensors: A review. Talanta 177, 163–170 (2018).
Arakawa, T. et al. Mouthguard biosensor with telemetry system for monitoring of saliva glucose: A novel cavitas sensor. Biosens. Bioelectron. 84, 106–111 (2016).
Arakawa, T. et al. A wearable cellulose acetate-coated mouthguard biosensor for in vivo salivary glucose measurement. Anal. Chem. 92, 12201–12207 (2020).
Park, W. et al. In-depth correlation analysis between tear glucose and blood glucose using a wireless smart contact lens. Nat. Commun. 15, 2828 (2024).
Bachhuber, F., Huss, A., Senel, M. & Tumani, H. Diagnostic biomarkers in tear fluid: from sampling to preanalytical processing. Sci. Rep. 11, 10064 (2021).
Kim, S.-K. et al. Bimetallic nanocatalysts immobilized in nanoporous hydrogels for long-term robust continuous glucose monitoring of smart contact lens. Adv. Mater. 34, 2110536 (2022).
Keum, D. H. et al. Wireless smart contact lens for diabetic diagnosis and therapy. Sci. Adv. 6, eaba3252 (2020).
Choi, H., Luzio, S., Beutler, J. & Porch, A. in 2018 IEEE International Microwave Biomedical Conference (IMBioC) 52-54 (2018).
Kandwal, A. et al. A comprehensive review on electromagnetic wave based non-invasive glucose monitoring in microwave frequencies. Heliyon 10, e37825 (2024).
Choi, H. et al. Design and In Vitro Interference Test of Microwave Noninvasive Blood Glucose Monitoring Sensor. IEEE Trans. Microw. Theory Tech. 63, 3016–3025 (2015).
Omer, A. E. et al. Low-cost portable microwave sensor for non-invasive monitoring of blood glucose level: novel design utilizing a four-cell CSRR hexagonal configuration. Sci. Rep. 10, 15200 (2020).
Baghelani, M., Abbasi, Z., Daneshmand, M. & Light, P. E. Non-invasive continuous-time glucose monitoring system using a chipless printable sensor based on split ring microwave resonators. Sci. Rep. 10, 12980 (2020).
Hanna, J. et al. Noninvasive, wearable, and tunable electromagnetic multisensing system for continuous glucose monitoring, mimicking vasculature anatomy. Sci. Adv. 6, eaba5320 (2020).
Yin, X. et al. Protein biomarkers of new-onset cardiovascular disease. Arterioscler. Thromb. Vasc. Biol. 34, 939–945 (2014).
Duan, Y. et al. Regulation of cholesterol homeostasis in health and diseases: from mechanisms to targeted therapeutics. Signal Transduct. Target. Ther. 7, 265 (2022).
Ouyang, J., Wang, H. & Huang, J. The role of lactate in cardiovascular diseases. Cell Commun. Signal. 21, 317 (2023).
Hayden, M. R. & Tyagi, S. C. Uric acid: A new look at an old risk marker for cardiovascular disease, metabolic syndrome, and type 2 diabetes mellitus: The urate redox shuttle. Nutr. Metab. 1, 10 (2004).
Gidding, S. S. & Allen, N. B. Cholesterol and atherosclerotic cardiovascular disease: a lifelong problem. J. Am. Heart Assoc. 8, e012924 (2019).
Badimon, L. & Vilahur, G. LDL-cholesterol versus HDL-cholesterol in the atherosclerotic plaque: inflammatory resolution versus thrombotic chaos. Ann. N. Y Acad. Sci. 1254, 18–32 (2012).
Baumer, Y., Irei, J. & Boisvert, W. A. Cholesterol crystals in the pathogenesis of atherosclerosis. Nat. Rev. Cardiol. (2024).
Borén, J. et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur. Heart J. 41, 2313–2330 (2020).
Pownall, H. J., Rosales, C., Gillard, B. K. & Gotto, A. M. High-density lipoproteins, reverse cholesterol transport and atherogenesis. Nat. Rev. Cardiol. 18, 712–723 (2021).
Li, X. et al. Lactate metabolism in human health and disease. Signal Transduct. Target. Ther. 7, 305 (2022).
Chang, X. et al. Association of lactate with risk of cardiovascular diseases: a two-sample mendelian randomization study. Vasc. Health Risk Manag. 20, 541–551 (2024).
Liao, Z., Chen, B., Yang, T., Zhang, W. & Mei, Z. Lactylation modification in cardio-cerebral diseases: A state-of-the-art review. Ageing Res. Rev. 104, 102631 (2025).
Arwani, R. T. et al. Stretchable ionic–electronic bilayer hydrogel electronics enable in situ detection of solid-state epidermal biomarkers. Nat. Mater. 23, 1115–1122 (2024).
Stalder, T. & Kirschbaum, C. in Encyclopedia of Behavioral Medicine. (ed. M. D. Gellman) 561–567 (Springer International Publishing, Cham; 2020).
Sbardella, E. & Tomlinson, J. W. In The Hypothalamic-Pituitary-Adrenal Axis in Health and Disease: Cushing’s Syndrome and Beyond. (ed. E. B. Geer) 271-301 (Springer International Publishing, Cham; 2017).
Faresjö, T. et al. Elevated levels of cortisol in hair precede acute myocardial infarction. Sci. Rep. 10, 22456 (2020).
Torrente-Rodríguez, R. M. et al. Investigation of Cortisol dynamics in human sweat using a graphene-based wireless mhealth system. Matter 2, 921–937 (2020).
Saito, Y., Tanaka, A., Node, K. & Kobayashi, Y. Uric acid and cardiovascular disease: A clinical review. J. Cardiol. 78, 51–57 (2021).
Lytvyn, Y., Perkins, B. A. & Cherney, D. Z. Uric acid as a biomarker and a therapeutic target in diabetes. Can. J. Diab 39, 239–246 (2015).
Kanbay, M. et al. The role of uric acid in the pathogenesis of human cardiovascular disease. Heart 99, 759–766 (2013).
Spiga, R. et al. Uric acid is associated with inflammatory biomarkers and induces inflammation via activating the NF-κB Signaling Pathway in HepG2 Cells. Arterioscler. Thromb. Vasc. Biol. 37, 1241–1249 (2017).
Gherghina, M. E. et al. Uric acid and oxidative stress-relationship with cardiovascular, metabolic, and renal impairment. Int. J. Mol. Sci. 23, 3188 (2022).
Li, G. et al. Wearable hydrogel SERS chip utilizing plasmonic trimers for uric acid analysis in sweat. Nano Lett. 24, 13447–13454 (2024).
Tu, J. et al. A wireless patch for the monitoring of C-reactive protein in sweat. Nat. Biomed. Eng. 7, 1293–1306 (2023).
Davis, N., Heikenfeld, J., Milla, C. & Javey, A. The challenges and promise of sweat sensing. Nat. Biotechnol. 42, 860–871 (2024).
Huang, C. et al. Flexible/regenerative nanosensor with automatic sweat collection for cytokine storm biomarker detection. ACS Nano 18, 21198–21210 (2024).
Liu, X. et al. A dynamic risk-based early warning monitoring system for population-based management of cardiovascular disease. Fundam. Res. 1, 534–542 (2021).
Nguyen, H.-H. & Vo, T.-T. Enhancing cardiovascular health monitoring through iot and deep learning technologies. SN Comput. Sci. 5, 608 (2024).
Li, X. et al. Cardiovascular risk factors in China: a nationwide population-based cohort study. Lancet Public Health 5, e672–e681 (2020).
Khoja, A. et al. Risk Factors for Early-Onset Versus Late-Onset Coronary Heart Disease (CHD): Systematic Review and Meta-Analysis. Heart, Lung Circ. 32, 1277–1311 (2023).
Mohd Faizal, A. S., Thevarajah, T. M., Khor, S. M. & Chang, S.-W. A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Comput. Methods Prog. Biomed. 207, 106190 (2021).
Cowie Martin, R., Linz, D., Redline, S., Somers Virend, K. & Simonds Anita, K. Sleep Disordered Breathing and Cardiovascular Disease. J. Am. Coll. Cardiol. 78, 608–624 (2021).
Drager, L. F., McEvoy, R. D., Barbe, F., Lorenzi-Filho, G. & Redline, S. Sleep apnea and cardiovascular disease. Circulation 136, 1840–1850 (2017).
Qian, X. et al. A review of methods for sleep arousal detection using polysomnographic signals. Brain Sci. 11, 1274 (2021).
Sun, J. et al. Multifunctional wearable humidity and pressure sensors based on biocompatible graphene/bacterial cellulose bioaerogel for wireless monitoring and early warning of sleep apnea syndrome. Nano Energy 108, 108215 (2023).
Klug, J., Leclerc, G., Dirren, E. & Carrera, E. Machine learning for early dynamic prediction of functional outcome after stroke. Commun. Med. 4, 232 (2024).
Lee, S. P. et al. Highly flexible, wearable, and disposable cardiac biosensors for remote and ambulatory monitoring. npj Digit. Med. 1, 2 (2018).
Khodaei, M. J., Candelino, N., Mehrvarz, A. & Jalili, N. Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation. IEEE Access 8, 23965–24005 (2020).
Ski, C. F. et al. Integrated care in cardiovascular disease: a statement of the Association of Cardiovascular Nursing and Allied Professions of the European Society of Cardiology. Eur. J. Cardiovasc. Nurs. 22, e39–e46 (2023).
Baum, T. E. & Brown, E. N. In 2021 American Control Conference (ACC) 672-677 (2021).
Kadiyala, N., Hovorka, R. & Boughton, C. K. Closed-loop systems: recent advancements and lived experiences. Expert Rev. Med. Devices 21, 927–941 (2024).
Boughton, C. K. & Hovorka, R. New closed-loop insulin systems. Diabetologia 64, 1007–1015 (2021).
Quirós, C. et al. The Medtronic 780G advanced hybrid closed-loop system achieves and maintains good glycaemic control in type 1 diabetes adults despite previous treatment. Endocrinol. Diab Nutr. (Engl. Ed.) 70, 130–135 (2023).
Kumar, S. & Shukla, R. Advancements in microneedle technology: current status and next-generation innovations. J. Microencapsul. 41, 782–803 (2024).
Li, X. et al. A Fully Integrated Closed-Loop System Based on Mesoporous Microneedles-Iontophoresis for Diabetes Treatment. Adv. Sci. 8, 2100827 (2021).
Yang, J. et al. Masticatory system–inspired microneedle theranostic platform for intelligent and precise diabetic management. Sci. Adv. 8, eabo6900 (2022).
Luo, X., Yu, Q., Yang, L. & Cui, Y. Wearable, Sensing-Controlled, Ultrasound-Based Microneedle Smart System for Diabetes Management. ACS Sens. 8, 1710–1722 (2023).
Liu, Y., Yang, L. & Cui, Y. A wearable, rapidly manufacturable, stability-enhancing microneedle patch for closed-loop diabetes management. Microsyst. Nanoeng. 10, 112 (2024).