{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:09:19Z","timestamp":1780099759434,"version":"3.54.0"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Pujiang Program","award":["21PJ1404000"],"award-info":[{"award-number":["21PJ1404000"]}]},{"name":"Shanghai Pujiang Program","award":["62103252"],"award-info":[{"award-number":["62103252"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21PJ1404000"],"award-info":[{"award-number":["21PJ1404000"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103252"],"award-info":[{"award-number":["62103252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To prevent and diagnose hypertension early, there has been a growing demand to identify its states that align with patients. This pilot study aims to research how a non-invasive method using photoplethysmographic (PPG) signals works together with deep learning algorithms. A portable PPG acquisition device (Max30101 photonic sensor) was utilized to (1) capture PPG signals and (2) wirelessly transmit data sets. In contrast to traditional feature engineering machine learning classification schemes, this study preprocessed raw data and applied a deep learning algorithm (LSTM-Attention) directly to extract deeper correlations between these raw datasets. The Long Short-Term Memory (LSTM) model underlying a gate mechanism and memory unit enables it to handle long sequence data more effectively, avoiding gradient disappearance and possessing the ability to solve long-term dependencies. To enhance the correlation between distant sampling points, an attention mechanism was introduced to capture more data change features than a separate LSTM model. A protocol with 15 healthy volunteers and 15 hypertension patients was implemented to obtain these datasets. The processed result demonstrates that the proposed model could present satisfactory performance (accuracy: 0.991; precision: 0.989; recall: 0.993; F1-score: 0.991). The model we proposed also demonstrated superior performance compared to related studies. The outcome indicates the proposed method could effectively diagnose and identify hypertension; thus, a paradigm to cost-effectively screen hypertension could rapidly be established using wearable smart devices.<\/jats:p>","DOI":"10.3390\/s23063359","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T02:35:26Z","timestamp":1679538926000},"page":"3359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Photoplethysmography Driven Hypertension Identification: A Pilot Study"],"prefix":"10.3390","volume":"23","author":[{"given":"Liangwen","family":"Yan","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingsen","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9637-4730","authenticated-orcid":false,"given":"Sijung","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronic, Electrical and Systems Engineering, Loughborough University, Ashby Road, Loughborough, Leicestershire LE11 3TU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1161\/HYPERTENSIONAHA.120.15026","article-title":"2020 International Society of Hypertension Global Hypertension Practice Guidelines","volume":"75","author":"Unger","year":"2020","journal-title":"Hypertension"},{"key":"ref_2","unstructured":"(2021). Annual report on cardiovascular health and diseases in China 2020. J. Cardiovasc. Pulm. Dis., 40, 1005\u20131009."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"765767","DOI":"10.3389\/fphys.2021.765767","article-title":"Editorial: Diabetes, Hypertension and Cardiovascular Diseases","volume":"12","author":"Choi","year":"2021","journal-title":"Front. Physiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1161\/01.CIR.0000154900.76284.F6","article-title":"Recommendations for Blood Pressure Measurement in Humans and Experimental Animals: Part 1: Blood pressure measurement in humans\u2014A statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Co","volume":"111","author":"Pickering","year":"2005","journal-title":"Circulation"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1113972","DOI":"10.3389\/fphys.2023.1113972","article-title":"Aortic systolic and pulse pressure invasively and non-invasively obtained: Comparative analysis of recording techniques, arterial sites of measurement, waveform analysis algorithms and calibration methods","volume":"14","author":"Bia","year":"2023","journal-title":"Front. Physiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1097\/MBP.0000000000000023","article-title":"Effect of mechanical behavior of the brachial artery on blood pressure measurement during cuff inflation and cuff deflation","volume":"19","author":"Dart","year":"2014","journal-title":"Blood Press. Monit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1038\/s41440-019-0268-9","article-title":"Stage 1 hypertension defined by the 2017 ACC\/AHA Hypertension Guidelines and Risk of Cardiovascular Events: A Cohort Study from Northern China","volume":"42","author":"Ji","year":"2019","journal-title":"Hypertens. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Srinivasan, K., Mahendran, N., Vincent, D.R., Chang, C.-Y., and Syed-Abdul, S. (2020). Realizing an Integrated Multistage Support Vector Machine Model for Augmented Recognition of Unipolar Depression. Electronics, 9.","DOI":"10.3390\/electronics9040647"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49509","DOI":"10.1109\/ACCESS.2020.2977887","article-title":"Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults","volume":"8","author":"Mahendran","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2146369","DOI":"10.1155\/2021\/2146369","article-title":"Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning","volume":"2021","author":"Salankar","year":"2021","journal-title":"J. Health Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1080\/13548506.2017.1400670","article-title":"Emotional reactivity and blood pressure elevations: Anxiety as a mediator","volume":"23","author":"Ifeagwazi","year":"2017","journal-title":"Psychol. Health Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10916-008-9186-0","article-title":"Non-constrained Blood Pressure Monitoring Using ECG and PPG for Personal Healthcare","volume":"33","author":"Yoon","year":"2008","journal-title":"J. Med. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1088\/0967-3334\/23\/1\/308","article-title":"The difference in pulse transit time to the toe and finger measured by photoplethysmography","volume":"23","author":"Nitzan","year":"2001","journal-title":"Physiol. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.artmed.2011.05.001","article-title":"Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques","volume":"53","year":"2011","journal-title":"Artif. Intell. Med."},{"key":"ref_15","first-page":"2742781","article-title":"Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms","volume":"2020","author":"Nour","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"96249","DOI":"10.1109\/ACCESS.2022.3203187","article-title":"Sensing Frequency Drifts: A Lookup Table Approach","volume":"10","author":"Avon","year":"2022","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20735","DOI":"10.1109\/ACCESS.2020.2968967","article-title":"Noninvasive Classification of Blood Pressure Based on Photoplethysmography Signals Using Bidirectional Long Short-Term Memory and Time-Frequency Analysis","volume":"8","author":"Tjahjadi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liang, Y., Chen, Z., Ward, R., and Elgendi, M. (2018). Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors, 8.","DOI":"10.3390\/bios8040101"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9938584","DOI":"10.1155\/2021\/9938584","article-title":"Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning","volume":"2021","author":"Wu","year":"2021","journal-title":"Int. J. Hypertens."},{"key":"ref_20","unstructured":"Kong, H., West, S., and Introduction, A. (, January June). WORLD MEDICAL ASSOCIATION Ethical Principles for Medical Research Involving Human Subjects. Proceedings of the 18th WMA General Assembly, Helsinki, Finland."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104291","DOI":"10.1016\/j.bspc.2022.104291","article-title":"Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms","volume":"80","author":"Kyriacou","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_22","first-page":"2229","article-title":"Design and comparison of butterworth and chebyshev type-1 low pass filter using Matlab","volume":"4","author":"Malica","year":"2011","journal-title":"Res. Cell Int. J. Eng. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"342462","DOI":"10.1093\/ecam\/neq054","article-title":"Developing the Effective Method of Spectral Harmonic Energy Ratio to Analyze the Arterial Pulse Spectrum","volume":"2011","author":"Huang","year":"2011","journal-title":"Evid.-Based Complement. Altern. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/TBCAS.2013.2279103","article-title":"Shape-Preserving Preprocessing for Human Pulse Signals Based on Adaptive Parameter Determination","volume":"8","author":"Wang","year":"2013","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.ijforecast.2020.06.008","article-title":"Recurrent Neural Networks for Time Series Forecasting: Current status and future directions","volume":"37","author":"Hewamalage","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.neucom.2023.01.037","article-title":"Robust recurrent neural networks for time series forecasting","volume":"526","author":"Zhang","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to Forget: Continual Prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_28","unstructured":"Chorowski, J., Bahdanau, D., Cho, K., and Bengio, Y. (2014). End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1109\/TASLP.2019.2937190","article-title":"Neural Machine Translation with Sentence-Level Topic Context","volume":"27","author":"Chen","year":"2019","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"595","DOI":"10.18280\/ts.390221","article-title":"Image Target Recognition Based on Multiregional Features under Hybrid Attention Mechanism","volume":"39","author":"Zhao","year":"2022","journal-title":"Trait. Signal"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huang, J., Wu, W., Li, J., and Wang, S. (2023). Text Summarization Method Based on Gated Attention Graph Neural Network. Sensors, 23.","DOI":"10.3390\/s23031654"},{"key":"ref_32","first-page":"591","article-title":"Introduction to convolutional neural network using Keras; an understanding from a statistician","volume":"26","author":"Lee","year":"2019","journal-title":"Commun. Stat. Appl. Methods"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Krichen, M., Mihoub, A., Alzahrani, M.Y., Adoni, W.Y.H., and Nahhal, T. (2022, January 9\u201311). Are Formal Methods Applicable to Machine Learning and Artificial Intelligence?. Proceedings of the 2022 2nd International Conference of Smart Systems and Emerging Technologies, SMARTTECH 2022, Riyadh, Saudi Arabia.","DOI":"10.1109\/SMARTTECH54121.2022.00025"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Moscato, S., Giudice, S.L., Massaro, G., and Chiari, L. (2022). Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis. Sensors, 22.","DOI":"10.3390\/s22155831"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fleischhauer, V., Feldheiser, A., and Zaunseder, S. (2022). Beat-to-Beat Blood Pressure Estimation by Photoplethysmography and Its Interpretation. Sensors, 22.","DOI":"10.3390\/s22187037"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zambrana-Vinaroz, D., Vicente-Samper, J.M., Manrique-Cordoba, J., and Sabater-Navarro, J.M. (2022). Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals. Sensors, 22.","DOI":"10.3390\/s22239372"},{"key":"ref_37","first-page":"1","article-title":"Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques","volume":"29","author":"Bozkurt","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liang, Y., Chen, Z., Ward, R., and Elgendi, M. (2018). Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. Diagnostics, 8.","DOI":"10.3390\/diagnostics8030065"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1159\/000522660","article-title":"Challenges Presented by Cuffless Measurement of Blood Pressure if Adopted for Diagnosis and Treatment of Hypertension","volume":"10","author":"Avolio","year":"2022","journal-title":"Pulse"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3359\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:00:51Z","timestamp":1760122851000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,22]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063359"],"URL":"https:\/\/doi.org\/10.3390\/s23063359","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,22]]}}}