{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:30:22Z","timestamp":1775068222669,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP12S-0227-190164"],"award-info":[{"award-number":["NPRP12S-0227-190164"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Research Collaboration Co-Fund (IRCC)","award":["IRCC-2021-001"],"award-info":[{"award-number":["IRCC-2021-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.<\/jats:p>","DOI":"10.3390\/s22030919","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T21:07:11Z","timestamp":1643144831000},"page":"919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4599-2192","authenticated-orcid":false,"given":"Sakib","family":"Mahmud","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}]},{"given":"Nabil","family":"Ibtehaz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7068-9112","authenticated-orcid":false,"given":"Amith","family":"Khandakar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}]},{"given":"Anas M.","family":"Tahir","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6938-6496","authenticated-orcid":false,"given":"Tawsifur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}]},{"given":"Khandaker Reajul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}]},{"given":"Md Shafayet","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9419-6478","authenticated-orcid":false,"given":"M. Sohel","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of CSE, BUET, ECE Building, West Palashi, Dhaka 1205, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0489-0090","authenticated-orcid":false,"given":"Farayi","family":"Musharavati","sequence":"additional","affiliation":[{"name":"Department Mechanical and Industrial Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8663-886X","authenticated-orcid":false,"given":"Mohamed Arselene","family":"Ayari","sequence":"additional","affiliation":[{"name":"Department of Civil and Architectural Engineering, Qatar University, Doha P.O. Box 2713, Qatar"},{"name":"Technology Innovation and Engineering Education (TIEE), Qatar University, Doha P.O. Box 2713, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4929-3209","authenticated-orcid":false,"given":"Mohammad Tariqul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-8206","authenticated-orcid":false,"given":"Muhammad E. H.","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2021, September 29). The Top 10 Causes of Death. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/the-top-10-causes-of-death."},{"key":"ref_2","unstructured":"(2021, August 18). Heart Disease and Stroke. Cdc.gov, Available online: https:\/\/www.cdc.gov\/chronicdisease\/resources\/publications\/factsheets\/heart-disease-stroke.html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.trsl.2013.05.001","article-title":"Chronic obstructive pulmonary disease and cardiovascular disease","volume":"162","author":"Bhatt","year":"2013","journal-title":"Transl. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S52","DOI":"10.1513\/AnnalsATS.201306-157MG","article-title":"Heart-lung interaction via infection","volume":"11","author":"Morris","year":"2014","journal-title":"Ann. Am. Thorac. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2160","DOI":"10.1097\/MD.0000000000002160","article-title":"High Blood Pressure and All-Cause and Cardiovascular Disease Mortalities in Community-Dwelling Older Adults","volume":"94","author":"Wu","year":"2015","journal-title":"Medicine"},{"key":"ref_6","unstructured":"Centers for Disease Control and Prevention (CDC) (2021). Vital Signs: Awareness and Treatment of Uncontrolled Hypertension among Adults\u2014The United States, 2003\u20132010. MMWR Morb. Mortal. Wkly. Rep., 103, 583\u2013586. Available online: https:\/\/pubmed.ncbi.nlm.nih.gov\/22951452\/."},{"key":"ref_7","unstructured":"World Health Organization (2021, May 22). A Global Brief on Hypertension: Silent Killer, Global Public Health Crisis: World Health Day 2013. Available online: https:\/\/apps.who.int\/iris\/handle\/10665\/79059."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.mpaic.2020.11.007","article-title":"Measuring arterial blood pressure","volume":"22","author":"Goodman","year":"2020","journal-title":"Anaesth. Intensiv. Care Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"231","DOI":"10.3389\/fmed.2017.00231","article-title":"Techniques for Non-Invasive Monitoring of Arterial Blood Pressure","volume":"4","author":"Meidert","year":"2018","journal-title":"Front. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1016\/j.chest.2017.10.030","article-title":"Noninvasive BP Monitoring in the Critically Ill","volume":"153","author":"Lakhal","year":"2018","journal-title":"Chest"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1038\/hr.2015.78","article-title":"Noninvasive estimation of central blood pressure and analysis of pulse waves by applanation tonometry","volume":"38","author":"Salvi","year":"2015","journal-title":"Hypertens. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kachuee, M., Kiani, M., Mohammadzade, H., and Shabany, M. (2015, January 24\u201327). Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time. Proceedings of the 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, Portugal.","DOI":"10.1109\/ISCAS.2015.7168806"},{"key":"ref_13","unstructured":"Ibtehaz, N., and Rahman, M.S. (2020). PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP)Waveforms using Fully Convolutional Neural Networks. arXiv, Available online: https:\/\/arxiv.org\/abs\/2005.01669."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/TBME.2016.2580904","article-title":"Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring","volume":"64","author":"Kachuee","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xie, Q., Wang, G., Peng, Z., and Lian, Y. (2018, January 19\u201321). Machine Learning Methods for Real-Time Blood Pressure Measurement Based on Photoplethysmography. Proceedings of the 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China.","DOI":"10.1109\/ICDSP.2018.8631690"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sasso, A.M., Datta, S., Jeitler, M., Steckhan, N., Kessler, S.C., Michalsen, A., Arnrich, B., and B\u00f6ttinger, E. (2020). HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population BT\u2014Artificial Intelligence in Medicine, Springer International Publishing.","DOI":"10.1101\/2020.05.27.20107243"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.H., Shuzan, 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_18","doi-asserted-by":"crossref","unstructured":"Kurylyak, Y., Lamonaca, F., and Grimaldi, D. (2013, January 6\u20139). A Neural Network-based method for continuous blood pressure estimation from a PPG signal. Proceedings of the 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Minneapolis, MN, USA.","DOI":"10.1109\/I2MTC.2013.6555424"},{"key":"ref_19","first-page":"1","article-title":"A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram","volume":"2018","author":"Wang","year":"2018","journal-title":"J. Healthc. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Manamperi, B., and Chitraranjan, C. (2019, January 28\u201330). A robust neural network-based method to estimate arterial blood pressure using photoplethysmography. Proceedings of the 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece.","DOI":"10.1109\/BIBE.2019.00128"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hsu, Y.C., Li, Y.H., Chang, C.C., and Harfiya, L.N. (2020). Generalized deep neural network model for cuffless blood pressure estimation with photoplethysmogram signal only. Sensors, 20.","DOI":"10.3390\/s20195668"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, Y.H., Harfiya, L.N., Purwandari, K., and der Lin, Y. (2020). Real-time cuffless continuous blood pressure estimation using deep learning model. Sensors, 20.","DOI":"10.3390\/s20195606"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Harfiya, L.N., Chang, C.C., and Li, Y.H. (2021). Continuous blood pressure estimation using exclusively photoplethysmography by lstm-based signal-to-signal translation. Sensors, 21.","DOI":"10.3390\/s21092952"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Athaya, T., and Choi, S. (2021). An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach. Sensors, 21.","DOI":"10.3390\/s21051867"},{"key":"ref_26","unstructured":"(2021, October 08). \u201cU-Net: Convolutional Networks for Biomedical Image Segmentation\u201d Lmb.informatik.uni-freiburg.de, 2021. Available online: https:\/\/lmb.informatik.uni-freiburg.de\/people\/ronneber\/u-net\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.joms.2005.09.023","article-title":"Hypertension: Classification, pathophysiology, and management during outpatient sedation and local anesthesia","volume":"64","author":"Holm","year":"2006","journal-title":"J. Oral Maxillofac. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). UNet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Esser, P., and Sutter, E. (2018). A Variational U-Net for Conditional Appearance and Shape Generation Heidelberg Collaboratory for Image Processing. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 8857\u20138866.","DOI":"10.1109\/CVPR.2018.00923"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Isensee, F., Petersen, J., Klein, A., Zimmermer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., and Wirkert, S. (2018). nnU-Net: Self-Adapting Framework for Unet-Based Medical Image Segmentation. arXiv, Available online: https:\/\/arxiv.org\/abs\/1809.10486.","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"ref_33","unstructured":"Iglovikov, V., and Shvets, A. (2018). TernausNet: U-Net with VGG11 Encoder Pre-Trained on Imagenet for Image Segmentation. arXiv, Available online: https:\/\/arxiv.org\/abs\/1801.05746."},{"key":"ref_34","unstructured":"Stoller, D., Ewert, S., and Dixon, S. (2018, January 23\u201327). Wave-U-Net: A multi-scale neural network for end-to-end audio source separation. Proceedings of the 19th International Society for Music Information Retrieval Conference ISMIR 2018, Paris, France."},{"key":"ref_35","first-page":"2016","article-title":"3D U-net: Learning dense volumetric segmentation from sparse annotation","volume":"9901","author":"Abdulkadir","year":"2016","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hao, X., Su, X., Wang, Z., and Zhang, H. (2019, January 15\u201319). Batushiren Unetgan: A robust speech enhancement approach in the time domain for extremely low signal-to-noise ratio condition. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech 2019, Graz, Austria.","DOI":"10.21437\/Interspeech.2019-1567"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kim, J.H., and Chang, J.H. (2020, January 25\u201329). Attention Wave-U-Net for acoustic echo cancellation. Proceedings of the Annual Conference International Speech Communication Association. INTERSPEECH, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-3200"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, X., Li, M., Lin, X., Wu, J., Xi, Y., and Jin, X. (2020, January 12). Shallow triple Unet for shadow detection. Proceedings of the Twelfth International Conference on Digital Image Processing, Osaka, Japan.","DOI":"10.1117\/12.2572916"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103719","DOI":"10.1016\/j.compbiomed.2020.103719","article-title":"A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals","volume":"120","author":"Esmaelpoor","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"101919","DOI":"10.1016\/j.artmed.2020.101919","article-title":"Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques","volume":"108","author":"Miao","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102972","DOI":"10.1016\/j.bspc.2021.102972","article-title":"Deep generative model with domain adversarial training for predicting arterial blood pressure waveform from photoplethysmogram signal","volume":"70","author":"Qin","year":"2021","journal-title":"Biomed. Signal Processing Control."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"96775","DOI":"10.1109\/ACCESS.2021.3095380","article-title":"A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model","volume":"9","author":"Shuzan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_43","unstructured":"Dheeru, D., and Casey, G. (2021, October 02). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_44","unstructured":"(2021, October 08). Archive.physionet.org. Available online: https:\/\/archive.physionet.org\/mimic2\/."},{"key":"ref_45","unstructured":"(2021, October 09). Physionet.org. Available online: https:\/\/physionet.org\/content\/mimic3wdb\/1.0\/."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Carlson, C., Turpin, V., Suliman, A., Ade, C., Warren, S., and Thompson, D. (2020). Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters. Sensors, 21.","DOI":"10.3390\/s21010156"},{"key":"ref_47","unstructured":"(2021, October 10). \u201cNI-9220\u201d, Ni.com. Available online: https:\/\/www.ni.com\/en-lb\/support\/model.ni-9220.html."},{"key":"ref_48","unstructured":"(2021, October 11). Finapres.com. Finapres Medical Systems|Products\u2014Finometer PRO, 2021. Available online: https:\/\/www.finapres.com\/Products\/Finometer-PRO."},{"key":"ref_49","unstructured":"(2021, October 05). Moving minimum\u2014MATLAB Movmin. Available online: https:\/\/www.mathworks.com\/help\/matlab\/ref\/movmin.html."},{"key":"ref_50","unstructured":"(2021, October 12). Polynomial Curve Fitting\u2014MATLAB Polyfit. Available online: https:\/\/www.mathworks.com\/help\/matlab\/ref\/polyfit.html."},{"key":"ref_51","unstructured":"(2021, October 12). Polynomial Evaluation\u2014MATLAB Polyval. Available online: https:\/\/www.mathworks.com\/help\/matlab\/ref\/polyval.html."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101682","DOI":"10.1016\/j.bspc.2019.101682","article-title":"Blind, Cuff-less, Calibration-Free and Continuous Blood Pressure Estimation using Optimized Inductive Group Method of Data Handling","volume":"57","author":"Mohebbian","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chakraborty, A., Sadhukhan, D., and Mitra, M. (2019). An Automated Algorithm to Extract Time Plane Features from the PPG Signal and its Derivatives for Personal Health Monitoring Application. IETE J. Res., 1\u201313.","DOI":"10.1080\/03772063.2019.1604178"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Elgendi, M., Liang, Y., and Ward, R. (2018). Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms. Diseases, 6.","DOI":"10.3390\/diseases6010020"},{"key":"ref_55","unstructured":"(2021, October 27). Differences and Approximate Derivatives\u2014MATLAB Diff. Mathworks.com, 2021. Available online: https:\/\/www.mathworks.com\/help\/matlab\/ref\/diff.html."},{"key":"ref_56","unstructured":"(2021, October 27). Take Derivatives of a Signal\u2014MATLAB & Simulink. Mathworks.com, 2021. Available online: https:\/\/www.mathworks.com\/help\/signal\/ug\/take-derivatives-of-a-signal.html."},{"key":"ref_57","unstructured":"(2021, October 29). Design Digital Filters, 2021. Mathworks\u2014MATLAB Designfilt. Mathworks.com, 2021. Available online: https:\/\/www.mathworks.com\/help\/signal\/ref\/designfilt.html."},{"key":"ref_58","unstructured":"(2021, November 05). Average Filter Delay (Group Delay)\u2014MATLAB Grpdelay. Mathworks.com, 2021. Available online: https:\/\/www.mathworks.com\/help\/signal\/ref\/grpdelay.html#f7-916897_sep_shared-n."},{"key":"ref_59","unstructured":"(2022, January 18). Mean Absolute Error (MAE)\u2014Sample Calculation. Medium, 2021. Available online: https:\/\/medium.com\/@ewuramaminka\/mean-absolute-error-mae-sample-calculation-6eed6743838a."},{"key":"ref_60","first-page":"S43","article-title":"The British hypertension society protocol for the evaluation of blood pressure measuring devices","volume":"11","author":"Petrie","year":"1993","journal-title":"J Hyper. Tens."},{"key":"ref_61","unstructured":"(2022, January 18). \u201cANSI\/AAMI SP10:2002\/(R)2008 and A1:2003\/(R)2008 and A2:2006\/(R)2008\u2014Manual, Electronic, or Automated Sphygmomanometers\u201d, Webstore.ansi.org, 2022. [Online]. Available online: https:\/\/webstore.ansi.org\/standards\/aami\/ansiaamisp1020022008a12003a2."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"141","DOI":"10.11613\/BM.2015.015","article-title":"Understanding Bland Altman analysis","volume":"25","author":"Giavarina","year":"2015","journal-title":"Biochem. Med."},{"key":"ref_63","unstructured":"(2021, October 28). Simple Linear Regression and Pearson Correlation\u2014, Statsdirect.com, 2021. Available online: https:\/\/www.statsdirect.com\/help\/regression_and_correlation\/simple_linear.htm."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Sagirova, Z., Kuznetsova, N., Gogiberidze, N., Gognieva, D., Suvorov, A., Chomakhidze, P., Omboni, S., Saner, H., and Kopylov, P. (2021). Cuffless Blood Pressure Measurement Using a Smartphone-Case Based ECG Monitor with Photoplethysmography in Hypertensive Patients. Sensors, 21.","DOI":"10.3390\/s21103525"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"106191","DOI":"10.1016\/j.cmpb.2021.106191","article-title":"A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms","volume":"207","author":"Baker","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"102772","DOI":"10.1016\/j.bspc.2021.102772","article-title":"A multi-type features fusion neural network for blood pressure prediction based on photoplethysmography","volume":"68","author":"Rong","year":"2021","journal-title":"Biomed. Signal Processing Control."},{"key":"ref_67","first-page":"272","article-title":"Photoplethysmography (PPG) Scheming System Based on Finite Impulse Response (FIR) Filter Design in Biomedical Applications","volume":"10","author":"Tun","year":"2021","journal-title":"Int. J. Electr. Electron. Eng. Telecommun."},{"key":"ref_68","unstructured":"Mahmud, S. (2022, January 18). \u201cPPG-ECG-to-BP-Prediction-ABP-Estimation\u201d, GitHub, 2021. [Online]. Available online: https:\/\/github.com\/Sakib1263\/PPG-ECG-to-BP-Prediction-ABP-Estimation."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/919\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:07:16Z","timestamp":1760134036000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,25]]},"references-count":68,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030919"],"URL":"https:\/\/doi.org\/10.3390\/s22030919","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,25]]}}}