REFERENCES
1. ZhangY, Liu Z, Wang X, et al. A blood sampling instrument integrating biomarker detection and physiological signal monitoring. 2023. Publication number CN115969374A. (In Chinese). Available from https://patents.google.com/patent/CN115969374A/zh?q=%E4%B8%80%E7%A7%8D%E9%9B%86%E7%94%9F%E7%89%A9%E6%A0%87%E5%BF%97%E7%89%A9%E6%A3%80%E6%B5%8B%E5%92%8C%E7%94%9F%E7%90%86%E4%BF%A1%E5%8F%B7%E7%9B%91%E6%B5%8B%E4%B8%80%E4%BD%93%E5%8C%96%E7%9A%84%E9%87%87%E8%A1%80%E4%BB%AA [Last accessed on 26 Sep 2023].
2. Jiang X, Lillehoj PB. Microneedle-based skin patch for blood-free rapid diagnostic testing. Microsyst Nanoeng 2020;6:96.
3. Cheng Z, Wang R, Xing Y, Zhao L, Choo J, Yu F. SERS-based immunoassay using gold-patterned array chips for rapid and sensitive detection of dual cardiac biomarkers. Analyst 2019;144:6533-40.
4. Gao R, Chen F, Yang D, et al. Simultaneous SERS-based immunoassay of dual cardiac markers on pump-free hybrid microfluidic chip. Sensors and Actuators B: Chemical 2022;369:132378.
5. Khlebtsov BN, Bratashov DN, Byzova NA, Dzantiev BB, Khlebtsov NG. SERS-based lateral flow immunoassay of troponin I by using gap-enhanced Raman tags. Nano Res 2019;12:413-20.
6. Blicharz TM, Gong P, Bunner BM, et al. Microneedle-based device for the one-step painless collection of capillary blood samples. Nat Biomed Eng 2018;2:151-7.
7. Liu Z, Meng D, Su G, et al. Ultrafast early warning of heart attacks through plasmon-enhanced raman spectroscopy using collapsible nanofingers and machine learning. Small 2023;19:e2204719.
8. Huang X, Liu B, Guo S, et al. SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis. Bioeng Transl Med 2023;8:e10420.
9. Dixon K, Bonon R, Ivander F, et al. Using machine learning and silver nanoparticle-based surface-enhanced raman spectroscopy for classification of cardiovascular disease biomarkers. ACS Appl Nano Mater 2023;6:15385-96.
10. Temko A. Accurate heart rate monitoring during physical exercises using PPG. IEEE Trans Biomed Eng 2017;64:2016-24.
11. Han D, Bashar SK, Lázaro J, et al. A real-time PPG peak detection method for accurate determination of heart rate during sinus rhythm and cardiac arrhythmia. Biosensors (Basel) 2022;12:82.
12. Pribadi EF, Pandey RK, Chao PC. Optimizing a novel PPG sensor patch via optical simulations towards accurate heart rates. Microsyst Technol 2020;26:3409-20.
13. Passler S, Müller N, Senner V. In-ear pulse rate measurement: a valid alternative to heart rate derived from electrocardiography? Sensors (Basel) 2019;19:3641.
14. Elgendi M, Fletcher R, Liang Y, et al. The use of photoplethysmography for assessing hypertension. NPJ Digit Med 2019;2:60.
15. Haddad S, Boukhayma A, Caizzone A. Continuous PPG-based blood pressure monitoring using multi-linear regression. IEEE J Biomed Health Inform 2022;26:2096-105.
16. Ibtehaz N, Mahmud S, Chowdhury MEH, et al. PPG2ABP: translating photoplethysmogram (PPG) signals to arterial blood pressure (ABP) waveforms. Bioengineering (Basel) 2022;9:692.
17. Mohan PM, Nisha AA, Nagarajan V, Jothi ESJ. Measurement of arterial oxygen saturation (SpO2) using PPG optical sensor. In: 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India, 2016, pp. 1136-1140.
18. Koteska B, Mitrova H, Bogdanova AM, Lehocki F. Machine learning based SpO2 prediction from PPG signal's characteristics features. In: 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Messina, Italy, 2022, pp. 1-6.
19. Gastel M, Stuijk S, de Haan G. New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring. Sci Rep 2016;6:38609.
20. Zhang G, Mei Z, Zhang Y, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf 2020;16:7209-18.
21. Zheng YL, Ding XR, Poon CC, et al. Unobtrusive sensing and wearable devices for health informatics. IEEE Trans Biomed Eng 2014;61:1538-54.
22. Weng S, Yuan H, Zhang X, et al. Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy. Analyst 2020;145:4827-35.
23. Wang X, Yin Y, Chen S, et al. 2023. Methods, devices, and equipment for intelligent protein detection based on Raman spectral signals. Publication number CN116297400A. (In Chinese). Available from https://patents.google.com/patent/CN116297400A/zh?q=%E5%9F%BA%E4%BA%8E%E6%8B%89%E6%9B%BC%E5%85%89%E8%B0%B1%E4%BF%A1%E5%8F%B7%E7%9A%84%E8%9B%8B%E7%99%BD%E6%99%BA%E8%83%BD%E6%A3%80%E6%B5%8B%E6%96%B9%E6%B3%95%E3%80%81%E8%A3%85%E7%BD%AE%E5%8F%8A%E8%AE%BE%E5%A4%87#similarDocuments [Last accessed on 26 Sep 2023].
24. Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. Precision Medicine. Elsevier; 2022. pp. 57-100.
