{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:23:35Z","timestamp":1771334615938,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,18]],"date-time":"2021-09-18T00:00:00Z","timestamp":1631923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital\/Marquette Foundation (MGH\/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson\u2019s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system\u2014the systolic blood pressure (SBP) and diastolic blood pressures (DBP)\u2014the R evaluations are 0.894 \u00b1 0.004 and 0.881 \u00b1 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 \u00b1 0.353 mmHg and 3.210 \u00b1 0.104 mmHg. Furthermore, for the PAP system\u2014the systolic and diastolic pressures\u2014the R evaluations are 0.864 \u00b1 0.003 mmHg and 0.817 \u00b1 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 \u00b1 0.136 mmHg and 2.964 \u00b1 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 \u00b1 0.001, 2.220 \u00b1 0.039 mmHg, and 1.329 \u00b1 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 \u00b1 0.003 and 2.404 \u00b1 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.<\/jats:p>","DOI":"10.3390\/s21186264","type":"journal-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T22:35:20Z","timestamp":1632263720000},"page":"6264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder"],"prefix":"10.3390","volume":"21","author":[{"given":"Muammar","family":"Sadrawi","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan"}]},{"given":"Yin-Tsong","family":"Lin","sequence":"additional","affiliation":[{"name":"AI R&D Department, New Era AI Robotic Inc., Taipei 105, Taiwan"}]},{"given":"Chien-Hung","family":"Lin","sequence":"additional","affiliation":[{"name":"AI R&D Department, New Era AI Robotic Inc., Taipei 105, Taiwan"}]},{"given":"Bhekumuzi","family":"Mathunjwa","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan"}]},{"given":"Ho-Tsung","family":"Hsin","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan"},{"name":"Cardiovascular Intensive Care Unit, Far-Eastern Memorial Hospital, New Taipei City 220, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6849-8453","authenticated-orcid":false,"given":"Shou-Zen","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8515-7933","authenticated-orcid":false,"given":"Maysam F.","family":"Abbod","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK"}]},{"given":"Jiann-Shing","family":"Shieh","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1056\/NEJMoa1207363","article-title":"A trial of intracranial-pressure monitoring in traumatic brain injury","volume":"367","author":"Chesnut","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2398","DOI":"10.1109\/JBHI.2019.2961403","article-title":"A spectral approach to model-based noninvasive intracranial pressure estimation","volume":"24","author":"Jaishankar","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1109\/TBME.2019.2940929","article-title":"Pseudo-Bayesian model-based noninvasive intracranial pressure estimation and tracking","volume":"67","author":"Imaduddin","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1001\/jama.286.6.700","article-title":"Complications of femoral and subclavian venous catheterization in critically ill patients: A randomized controlled trial","volume":"286","author":"Merrer","year":"2001","journal-title":"JAMA"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sadrawi, M., Shieh, J.S., Fan, S.Z., Lin, C.H., Haraikawa, K., Chien, J.C., and Abbod, M.F. (2016, January 4\u20137). Intermittent blood pressure prediction via multiscale entropy and ensemble artificial neural networks. Proceedings of the 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IECBES.2016.7843473"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.bspc.2019.02.028","article-title":"Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network","volume":"51","author":"Tanveer","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, H., Ji, Z., and Li, M. (2019). Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography. Sensors, 19.","DOI":"10.3390\/s19245543"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Eom, H., Lee, D., Han, S., Hariyani, Y.S., Lim, Y., Sohn, I., Park, K., and Park, C. (2020). End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism. Sensors, 20.","DOI":"10.3390\/s20082338"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sideris, C., Kalantarian, H., Nemati, E., and Sarrafzadeh, M. (2016, January 18\u201320). Building continuous arterial blood pressure prediction models using recurrent networks. Proceedings of the 2016 IEEE International Conference on Smart Computing (SMARTCOMP), St. Louis, MO, USA.","DOI":"10.1109\/SMARTCOMP.2016.7501681"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sadrawi, M., Lin, Y.T., Lin, C.H., Mathunjwa, B., Fan, S.Z., Abbod, M.F., and Shieh, J.S. (2020). Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography. Sensors, 20.","DOI":"10.3390\/s20143829"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sadrawi, M., Shieh, J.S., Haraikawa, K., Chien, J.C., Lin, C.H., and Abbod, M.F. (2016, January 4\u20137). Ensemble empirical mode decomposition applied for PPG motion artifact. Proceedings of the 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IECBES.2016.7843455"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Salehizadeh, S., Dao, D., Bolkhovsky, J., Cho, C., Mendelson, Y., and Chon, K.H. (2016). A novel time-varying spectral filtering algorithm for reconstruction of motion artifact corrupted heart rate signals during intense physical activities using a wearable photoplethysmogram sensor. Sensors, 16.","DOI":"10.3390\/s16010010"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1016\/j.jacc.2007.07.022","article-title":"Noninvasive central venous pressure measurement by controlled compression sonography at the forearm","volume":"50","author":"Thalhammer","year":"2007","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1007\/s10554-020-01889-3","article-title":"Non-invasive assessment of central venous pressure in heart failure: A systematic prospective comparison of echocardiography and Swan-Ganz catheter","volume":"36","author":"Szymczyk","year":"2020","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_15","first-page":"1","article-title":"Non-invasive pulmonary artery pressure estimation by electrical impedance tomography in a controlled hypoxemia study in healthy subjects","volume":"10","author":"Braun","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sadrawi, M., Lin, C.H., Lin, Y.T., Hsieh, Y., Kuo, C.C., Chien, J.C., Haraikawa, K., Abbod, M.F., and Shieh, J.S. (2017). Arrhythmia evaluation in wearable ECG devices. Sensors, 17.","DOI":"10.3390\/s17112445"},{"key":"ref_17","first-page":"e4067","article-title":"HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia","volume":"5","author":"Liu","year":"2017","journal-title":"PeerJ"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"536863","DOI":"10.1155\/2015\/536863","article-title":"Computational depth of anesthesia via multiple vital signs based on artificial neural networks","volume":"2015","author":"Sadrawi","year":"2015","journal-title":"BioMed Res. Int."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1109\/TBME.2015.2422378","article-title":"A novel algorithm for the automatic detection of sleep apnea from single-lead ECG","volume":"62","author":"Varon","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"S48","DOI":"10.1038\/sj.npp.1300146","article-title":"Dynamics of heart rate and sleep stages in normals and patients with sleep apnea","volume":"28","author":"Penzel","year":"2003","journal-title":"Neuropsychopharmacology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/TBME.2003.817636","article-title":"Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea","volume":"50","author":"Penzel","year":"2003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1136\/hrt.25.4.460","article-title":"Effect of hypertension on the P wave of the electrocardiogram","volume":"25","author":"Ross","year":"1963","journal-title":"Br. Heart J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1161\/01.CIR.34.5.818","article-title":"Electrocardiographic changes reflecting left atrial abnormality in hypertension","volume":"34","author":"Tarazi","year":"1966","journal-title":"Circulation"},{"key":"ref_24","first-page":"1","article-title":"Hypertensive crisis-induced electrocardiographic changes: A case series","volume":"3","author":"Vrijlandt","year":"2009","journal-title":"J. Med. Case Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1161\/01.HYP.20.1.32","article-title":"Hypertension and ST segment depression during ambulatory electrocardiographic recording. Results from the prospective population study\u2019men born in 1914\u2019from Malm\u00f6, Sweden","volume":"20","author":"Hedblad","year":"1992","journal-title":"Hypertension"},{"key":"ref_26","first-page":"1","article-title":"Causal Pathways from Blood Pressure to Larger QRS Amplitudes: A Mendelian Randomization Study","volume":"8","author":"Hendriks","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kovacs, G., Avian, A., Foris, V., Tscherner, M., Kqiku, X., Douschan, P., Bachmaier, G., Olschewski, A., Matucci-Cerinic, M., and Olschewski, H. (2016). Use of ECG and other simple non-invasive tools to assess pulmonary hypertension. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0168706"},{"key":"ref_28","first-page":"4606053","article-title":"ECG markers of hemodynamic improvement in patients with pulmonary hypertension","volume":"2018","author":"Tyrka","year":"2018","journal-title":"BioMed Res. Int."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103924","DOI":"10.1016\/j.compbiomed.2020.103924","article-title":"Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank","volume":"123","author":"Rajput","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fan, X., Wang, H., Zhao, Y., Li, Y., and Tsui, K.L. (2021). An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals. Sensors, 21.","DOI":"10.3390\/s21051595"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1161\/01.CIR.23.6.853","article-title":"Electrocardiographic changes in head injuries","volume":"23","author":"Hersch","year":"1961","journal-title":"Circulation"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jelectrocard.2004.09.004","article-title":"Electrocardiographic changes after head trauma","volume":"38","author":"Wittebole","year":"2005","journal-title":"J. Electrocardiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s12471-010-0049-1","article-title":"ECG changes in subarachnoid haemorrhage: A synopsis","volume":"19","author":"Chatterjee","year":"2011","journal-title":"Neth. Heart J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1097\/00008506-200407000-00003","article-title":"Traumatic subarachnoid hemorrhage and QTc prolongation","volume":"16","author":"Collier","year":"2004","journal-title":"J. Neurosurg. Anesthesiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1136\/bmj.1.5952.242","article-title":"Electrocardiographic abnormalities associated with raised intracranial pressure","volume":"1","author":"Jachuck","year":"1975","journal-title":"Br. Med. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.jelectrocard.2009.04.001","article-title":"J-wave formation in patients with acute intracranial hypertension","volume":"42","author":"Milewska","year":"2009","journal-title":"J. Electrocardiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.3389\/fneur.2020.597737","article-title":"The association of early electrocardiographic abnormalities with brain injury severity and outcome in severe traumatic brain injury","volume":"11","author":"Lenstra","year":"2021","journal-title":"Front. Neurol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_39","first-page":"96","article-title":"The Massachusetts General Hospital-Marquette Foundation hemodynamic and electrocardiographic database\u2013comprehensive collection of critical care waveforms","volume":"7","author":"Welch","year":"1991","journal-title":"J. Clin. Monit."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s10877-015-9779-3","article-title":"Trending autoregulatory indices during treatment for traumatic brain injury","volume":"30","author":"Kim","year":"2016","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1136\/bmj.324.7334.415","article-title":"Introduction. I\u2014Leads, rate, rhythm, and cardiac axis","volume":"324","author":"Meek","year":"2002","journal-title":"BMJ"},{"key":"ref_43","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/31\/1\/R01","article-title":"Arterial blood pressure measurement and pulse wave analysis\u2014their role in enhancing cardiovascular assessment","volume":"31","author":"Avolio","year":"2009","journal-title":"Physiol. Meas."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Slapni\u010dar, G., Mlakar, N., and Lu\u0161trek, M. (2019). Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network. Sensors, 19.","DOI":"10.3390\/s19153420"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.H., Shuzan, M.N.I., Chowdhury, M.E., Mahbub, Z.B., Uddin, M.M., Khandakar, A., and Reaz, M.B.I. (2020). Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques. Sensors, 20.","DOI":"10.3390\/s20113127"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.compbiomed.2018.09.013","article-title":"Arterial blood pressure feature estimation using photoplethysmography","volume":"102","author":"Zadi","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Aguirre, N., Grall-Ma\u00ebs, E., Cymberknop, L.J., and Armentano, R.L. (2021). Blood pressure morphology assessment from photoplethysmogram and demographic information using deep learning with attention mechanism. Sensors, 21.","DOI":"10.3390\/s21062167"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sadrawi, M., Sun, W.Z., Ma, M.H.M., Yeh, Y.T., Abbod, M.F., and Shieh, J.S. (2018). Ensemble genetic fuzzy neuro model applied for the emergency medical service via unbalanced data evaluation. Symmetry, 10.","DOI":"10.3390\/sym10030071"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1016\/j.jcmg.2019.06.009","article-title":"State-of-the-art deep learning in cardiovascular image analysis","volume":"12","author":"Litjens","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1109\/TMI.2017.2655486","article-title":"Detecting cardiovascular disease from mammograms with deep learning","volume":"36","author":"Wang","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Munir, K., Elahi, H., Ayub, A., Frezza, F., and Rizzi, A. (2019). Cancer diagnosis using deep learning: A bibliographic review. Cancers, 11.","DOI":"10.3390\/cancers11091235"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6264\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:01:52Z","timestamp":1760166112000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,18]]},"references-count":52,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21186264"],"URL":"https:\/\/doi.org\/10.3390\/s21186264","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,18]]}}}