|
[1] Stevens SL, Wood S, Koshiaris C, et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis[J]. BMJ, 2016, 354: i4098.
[2] Blanco PJ, Feijóo RA. A dimensionally-heterogeneous closed-loop model for the cardiovascular system and its applications[J]. Medical Engineering & Physics, 2013, 35(5): 652-667.
[3] Saugel B, Hoppe P, Nicklas JY, et al. Continuous noninvasive pulse wave analysis using finger cuff technologies for arterial blood pressure and cardiac output monitoring in perioperative and intensive care medicine: a systematic review and meta-analysis[J]. British Journal of Anaesthesia, 2020, 125(1): 25-37.
[4] Nachman D, Gepner Y, Goldstein N, et al. Comparing blood pressure measurements between a photoplethysmography-based and a standard cuff-based manometry device[J]. Scientific Reports, 2020, 10(1): 16116.
[5] Song K, Chung K, Chang J. Cuffless deep learning-based blood pressure estimation for smart wristwatches[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(7): 4292-4302.
[6] Li YH, Harfiya LN, Purwandari K, et al. Real-time cuffless continuous blood pressure estimation using deep learning model[J]. Sensors, 2020, 20(19): 5606.
[7] Hsu YC, Li YH, Chang CC, et al. Generalized deep neural network model for cuffless blood pressure estimation with photoplethysmogram signal only[J]. Sensors, 2020, 20(19): 5668.
[8] EI-Hajj C, Kyriacou PA. Deep learning models for cuffless blood pressure monitoring from PPG signals using attention mechanism[J]. Biomedical Signal Processing and Control, 2021, 65: 102301.
[9] Lee D, Kwon H, Son D, et al. Beat-to-beat continuous blood pressure estimation using bidirectional long short-term memory network[J]. Sensors, 2021, 21(1): 96.
[10] Eom H, Lee D, Han S, et al. End-to-end deep learning architecture for continuous blood pressure estimation using attention mechanism[J]. Sensors, 2020, 20(8): 2338.
[11] Panwar M, Gautam A, Biswas D, et al. PP-Net: a deep learning framework for PPG-based blood pressure and heart rate estimation[J]. IEEE Sensors Journal, 2020, 20(17): 10000-10011.
[12] Athaya T, Choi S. An estimation method of continuous non-invasive arterial blood pressure waveform using photoplethysmography: a U-Net architecture-based approach[J]. Sensors, 2021, 21(5): 1867.
[13] Aguirre N, Grall-Ma?s E, Cymberknop LJ, et al. Blood pressure morphology assessment from photoplethysmogram and demographic information using deep learning with attention mechanism[J]. Sensors, 2021, 21(6): 2167.
[14] Westenberg JJM, van Poelgeest EP, Steendijk P, et al. Bramwell-Hill modeling for local aortic pulse wave velocity estimation: a validation study with velocity-encoded cardiovascular magnetic resonance and invasive pressure assessment[J]. Journal of Cardiovascular Magnetic Resonance, 2012, 14: 2.
[15] Suzuki S, Oguri K. Cuffless blood pressure estimation by error-correcting output coding method based on an aggregation of AdaBoost with a photoplethysmograph sensor[J]. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009: 6765-6768.
[16] Kurylyak Y, Lamonaca F, Grimaldi D. A neural network-based method for continuous blood pressure estimation from a PPG signal[C]// 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Minneapolis, MN, USA: IEEE, 2013: 280-283.
[17] Duan K, Qian Z, Atef M, et al. A feature exploration methodology for learning based cuffless blood pressure measurement using photoplethysmography[C]// 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando, FL, USA: IEEE, 2016: 6385-6388.
[18] Kachuee M, Kiani MM, Mohammadzade H, et al. Cuffless blood pressure estimation algorithms for continuous health-care monitoring[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(4): 859-869.
[19] Miao F, Fu N, Zhang Y, et al. A novel continuous blood pressure estimation approach based on data mining techniques[J]. IEEE Journal of Biomedical and Health Informatics, 2017, 21(6): 1730-1740.
[20] Khalid SG, Zhang J, Chen F, et al. Blood pressure estimation using photoplethysmography only: comparison between different machine learning approaches[J]. Journal of Healthcare Engineering, 2018, 2018: 1548647.
[21] Priyanka KNG, Chao PC, Tu T, et al. Estimating blood pressure via artificial neural networks based on measured photoplethysmography waveforms[C]// 2018 IEEE Sensors. New Delhi, India: IEEE, 2018: 1-4.
[22] Wang L, Zhou W, Xing Y, et al. A novel neural network model for blood pressure estimation using photoplethesmography without electrocardiogram[J]. Journal of Healthcare Engineering, 2018, 2018: 7804243.
[23] Mousavi SS, Firouzmand M, Charmi M, et al. Blood pressure estimation from appropriate and inappropriate PPG signals using A whole-based method[J]. Biomedical Signal Processing and Control, 2019, 47: 196-206.
[24] Khalid SG, Liu H, Zia T, et al. Cuffless blood pressure estimation using single channel photoplethysmography: a two-step method[J]. IEEE Access, 2020, 8: 58146-58154.
[25] Zhang Y, Feng Z. A SVM method for continuous blood pressure estimation from a PPG signal[C]// Proceedings of the 9th International Conference on Machine Learning and Computing. Raleigh, North Carolina, USA: EDM 2016, 2017: 128–132.
[26] Liu M, Po LM, Fu H. Cuffless blood pressure estimation based on photoplethysmography signal and its second derivative[J]. International Journal of Computer Theory and Engineering, 2017, 9(3): 202-206.
[27] Su P, Ding X, Zhang Y, et al. Long-term blood pressure prediction with deep recurrent neural networks[C]// 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). ?Las Vegas, NV, USA: IEEE, 2018: 323-328.
[28] Lin W-H, Wang H, Samuel OW, et al. New photoplethysmogram indicators for improving cuffless and continuous blood pressure estimation accuracy[J]. Physiological Measurement, 2018, 39(2): 025005.
[29] Slapni?ar G, Lu?trek M, Marinko M. Continuous blood pressure estimation from PPG signal[J]. Informatica, 2018, 42(1): 33-42.
[30] Ertu?rul ?F, Sezgin N. A noninvasive time-frequency-based approach to estimate cuffless arterial blood pressure[J]. Turkish Journal of Electrical Engineering and Computer Sciences, 2018, 26(5): 2260-2274.
[31] Chowdhury MH, Shuzan MN, Chowdhury MEH, et al. Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques[J]. Sensors, 2020, 20(11): 3127.
[32] Saeed M, Villarroel M, Reisner AT, et al. Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database[J]. Critical Care Medicine, 2011, 39(5): 952-960.
[33] Liu D, G?rges M, Jenkins SA. University of Queensland vital signs dataset: development of an accessible repository of anesthesia patient monitoring data for research[J]. Anesthesia and Analgesia, 2012, 114(3): 584-589.
[34] White WB, Berson AS, Robbins C, et al. National standard for measurement of resting and ambulatory blood pressures with automated sphygmomanometers[J]. Hypertension, 1993, 21(4): 504-509.
[35] Shimazaki S, Kawanaka H, Ishikawa H, et al. Cuffless blood pressure estimation from only the waveform of photoplethysmography using CNN[C]// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin, Germany: IEEE, 2019: 5042-5045.
[36] Slapni?ar G, Mlakar N, Lu?trek M. Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network[J]. Sensors, 2019, 19(15): 3420.
[37] Yan C, Li Z, Zhao W, et al. Novel deep convolutional neural network for cuff-less blood pressure measurement using ECG and PPG signals[C]// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin, Germany: IEEE, 2019: 1917-1920.
[38] Baek S, Jang J, Yoon S. End-to-end blood pressure prediction via fully convolutional networks[J]. IEEE Access, 2019, 7: 185458-185468.
[39] Shimazaki S, Bhuiyan S, Kawanaka H, et al. Features Extraction for Cuffless Blood Pressure Estimation by Autoencoder from Photoplethysmography[C]// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). ?Honolulu, HI, USA: IEEE Press, 2018: 2857-2860.
[40] Landry C, Peterson SD, Arami A. Nonlinear Dynamic Modeling of Blood Pressure Waveform: Towards an Accurate Cuffless Monitoring System[J]. IEEE Sensors Journal, 2020, 20(10): 5368-5378.
[41] Tanveer MS, Hasan MK. Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network[J]. Biomedical Signal Processing and Control, 2019, 51: 382-392.
|
