{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T23:09:27Z","timestamp":1768691367315,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T00:00:00Z","timestamp":1594252800000},"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>Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.<\/jats:p>","DOI":"10.3390\/s20143829","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T10:45:19Z","timestamp":1594291519000},"page":"3829","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography"],"prefix":"10.3390","volume":"20","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&amp;D Department, New Era AI Robotic Inc., Taipei 105, Taiwan"}]},{"given":"Chien-Hung","family":"Lin","sequence":"additional","affiliation":[{"name":"AI R&amp;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"}]},{"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 Computer 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":[[2020,7,9]]},"reference":[{"key":"ref_1","unstructured":"(2020, March 18). Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/hypertension."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S211","DOI":"10.1016\/S0002-8703(99)70312-1","article-title":"Elevated systolic blood pressure and risk of cardiovascular and renal disease: Overview of evidence from observational epidemiologic studies and randomized controlled trials","volume":"138","author":"He","year":"1999","journal-title":"Am. Heart J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/S0140-6736(08)60655-8","article-title":"Global burden of blood-pressure-related disease, 2001","volume":"371","author":"Lawes","year":"2008","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1093\/bmb\/ldh050","article-title":"Hypertensive retinopathy signs as risk indicators of cardiovascular morbidity and mortality","volume":"73","author":"Wong","year":"2005","journal-title":"Br. Med. Bull."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"45644","DOI":"10.1038\/srep45644","article-title":"Identification of atrial fibrillation by quantitative analyses of fingertip photoplethysmogram","volume":"7","author":"Tang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liang, Y., Chen, Z., Ward, R., and Elgendi, M. (2019). Hypertension assessment using photoplethysmography: A risk stratification approach. J. Clin. Med., 8.","DOI":"10.3390\/jcm8010012"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.3390\/s120201816","article-title":"Evaluation of electrical and optical plethysmography sensors for noninvasive monitoring of hemoglobin concentration","volume":"12","author":"Phillips","year":"2012","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Perpetuini, D., Chiarelli, A.M., Cardone, D., Rinella, S., Massimino, S., Bianco, F., Bucciarelli, V., Vinciguerra, V., Fallica, G., and Perciavalle, V. (2020). Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach. Appl. Sci., 10.","DOI":"10.3390\/app10062137"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wei, H.C., Ta, N., Hu, W.R., Xiao, M.X., Tang, X.J., Haryadi, B., Liou, J.J., and Wu, H.T. (2019). Digital Volume Pulse Measured at the Fingertip as an Indicator of Diabetic Peripheral Neuropathy in the Aged and Diabetic. Entropy, 21.","DOI":"10.3390\/e21121229"},{"key":"ref_11","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_12","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_13","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_14","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_15","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_16","doi-asserted-by":"crossref","first-page":"3420","DOI":"10.3390\/s19153420","article-title":"Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network","volume":"19","author":"Mlakar","year":"2019","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sadrawi, M., Yunus, J., Khalil, M., Sofyan, S.E., Abbod, M.F., and Shieh, J.S. (2019, January 22\u201324). Computational fluid dynamics based fuzzy control optimization of heat exchanger via genetic algorithm. Proceedings of the 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), Banda Aceh, Indonesia.","DOI":"10.1109\/CYBERNETICSCOM.2019.8875637"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1016\/j.renene.2019.07.065","article-title":"Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization","volume":"146","author":"Silitonga","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Song, C., Lee, S., Gu, B., Chang, I., Cho, G.Y., Baek, J.D., and Cha, S.W. (2020). A Study of Anode-Supported Solid Oxide Fuel Cell Modeling and Optimization Using Neural Network and Multi-Armed Bandit Algorithm. Energies, 13.","DOI":"10.3390\/en13071621"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.eswa.2014.08.007","article-title":"Bayesian hierarchical models for aerospace gas turbine engine prognostics","volume":"42","author":"Zaidan","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13","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_22","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_23","doi-asserted-by":"crossref","unstructured":"Liao, Y.H., Shih, C.H., Abbod, M.F., Shieh, J.S., and Hsiao, Y.J. (2020). Development of an E-nose system using machine learning methods to predict ventilator-associated pneumonia. Microsyst. Technol., 1\u201311.","DOI":"10.1007\/s00542-020-04782-0"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liao, Y.H., Wang, Z.C., Zhang, F.G., Abbod, M.F., Shih, C.H., and Shieh, J.S. (2019). Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit. Sensors, 19.","DOI":"10.3390\/s19081866"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","article-title":"Ensembling neural networks: Many could be better than all","volume":"137","author":"Zhou","year":"2002","journal-title":"Artif. Intell."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","article-title":"Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals","volume":"100","author":"Acharya","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"53731","DOI":"10.1109\/ACCESS.2019.2912273","article-title":"Spectrum analysis of eeg signals using cnn to model patient\u2019s consciousness level based on anesthesiologists\u2019 experience","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s10916-018-0999-1","article-title":"Design and evaluation of a real time physiological signals acquisition system implemented in multi-operating rooms for anesthesia","volume":"42","author":"Liu","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_32","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_33","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. Arxiv Prepr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_35","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"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3829\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:49:31Z","timestamp":1760176171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3829"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,9]]},"references-count":35,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20143829"],"URL":"https:\/\/doi.org\/10.3390\/s20143829","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,9]]}}}