{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:07:37Z","timestamp":1792789657220,"version":"4.5.0"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,9]],"date-time":"2020-02-09T00:00:00Z","timestamp":1581206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PDD Grant of Ristekdikti","award":["NKB-1843\/UN2.R3.1\/HKP.05.00\/2019"],"award-info":[{"award-number":["NKB-1843\/UN2.R3.1\/HKP.05.00\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classi\ufb01cation accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the K-nearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPG\u2013BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual pre-processing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.<\/jats:p>","DOI":"10.3390\/info11020093","type":"journal-article","created":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T11:48:51Z","timestamp":1581335331000},"page":"93","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study"],"prefix":"10.3390","volume":"11","author":[{"given":"Hendrana","family":"Tjahjadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalamullah","family":"Ramli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Al-Zaben, A., Fora, M., and Obaidat, A. (2018, January 28\u201330). Detection of Premature Ventricular Beats from Arterial Blood Pressure Signal. Proceedings of the 2018 IEEE 4th Middle East. Conference on Biomedical Engineering (MECBME), Tunis, Tunisia.","DOI":"10.1109\/MECBME.2018.8402398"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/TIM.2018.2800539","article-title":"Arterial blood pressure estimation from local pulse wave velocity using dual-element photoplethysmograph probe","volume":"67","author":"Nabeel","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s10916-018-1138-8","article-title":"Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL) \u2013 Review of Methodologies and Devices","volume":"43","author":"Stojanova","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-016-0302-y","article-title":"Feasibility study for the non-invasive blood pressure estimation based on ppg morphology: Normotensive subject study","volume":"16","author":"Shin","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"025006","DOI":"10.1088\/1361-6579\/ab030e","article-title":"Estimating blood pressure trends and the nocturnal dip from photoplethysmograph","volume":"2","author":"Radha","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Savkar, A., Khatate, P., and Patil, C.Y. (2019, January 14\u201315). Study on Techniques Involved in Tourniqueteless Blood Pressure Measurement Using PPG. Proceedings of the in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICCONS.2018.8663139"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"025005","DOI":"10.1088\/1361-6579\/aaa454","article-title":"New Photoplethysmogram Indicators for Improving Cuffless and Continuous Blood Pressure Estimation Accuracy","volume":"39","author":"Lin","year":"2010","journal-title":"Physiol. Meas."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tamura, T., and Maeda, Y. (2017). Photoplethysmogram. Seamless Healthcare Monitoring, Springer International Publishing.","DOI":"10.1007\/978-3-319-69362-0_6"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/28\/3\/R01","article-title":"Photoplethysmography and its application in clinical physiological measurement","volume":"28","author":"Allen","year":"2007","journal-title":"Physiol. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"063501","DOI":"10.1088\/2040-8986\/aab74c","article-title":"Oximetry using multispectral imaging: Theory and application","volume":"20","author":"MacKenzie","year":"2018","journal-title":"J. Optics"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Datta, S., Banerjee, R., Choudhury, A.D., Sinha, A., and Pal, A. (2016, January 23\u201327). Blood pressure estimation from photoplethysmogram using latent parameters. Proceedings of the 2016 IEEE International Conference on Communications (ICC), Kuala, Lumpur.","DOI":"10.1109\/ICC.2016.7511599"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tjahjadi, H., and Ramli, K. (2017, January 19\u201321). Variance analysis of photoplethysmography for blood pressure measurement. Proceedings of the 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI 2017), Yogyakarta, Indonesia.","DOI":"10.1109\/EECSI.2017.8239092"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MCAS.2018.2849261","article-title":"Towards a continuous non-invasive cuffless blood pressure monitoring system using PPG: Systems and circuits review","volume":"18","author":"Wang","year":"2018","journal-title":"IEEE Circuits Syst. Mag."},{"key":"ref_14","unstructured":"Teng, X.F., and Zhang, Y.T. (2003, January 17\u201321). Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico."},{"key":"ref_15","unstructured":"Kim, J.Y., Cho, B.H., Im, S.M., Jeon, M.J., Kim, I.Y., and Kim, S.I. (September, January 31). Comparative study on artificial neural network with multiple regressions for continuous estimation of blood pressure. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yan, Y.S., and Zhang, Y.T. (September, January 31). Noninvasive Estimation of Blood Pressure Using Photoplethysmographic Signals in the Period Domain. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1617255"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"McCombie, D.B., Reisner, A.T., and Asada, H.H. (September, January 30). Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics. Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA.","DOI":"10.1109\/IEMBS.2006.260590"},{"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","doi-asserted-by":"crossref","unstructured":"Rundo, F., Ortis, A., Battiato, S., and Conoci, S. (2018). Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis. Computation, 6.","DOI":"10.3390\/computation6030046"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tjahjadi, H., and Ramli, K. (2017, January 24\u201327). Review of photoplethysmography based non-invasive continuous blood pressure methods. Proceedings of the QiR 2017\u20142017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, Bali, Indonesia.","DOI":"10.1109\/QIR.2017.8168477"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Choudhury, A.D., Banerjee, R., Sinha, A., and Kundu, S. (2014, January 26\u201330). Estimating blood pressure using Windkessel model on photoplethysmogram. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944640"},{"key":"ref_22","first-page":"1","article-title":"Differential photoplethysmogram sensor with an optical notch filter shows potential for reducing motion artifact signals","volume":"4","author":"Blomqvist","year":"2018","journal-title":"Biomed. Phys. Eng. Express Inst. Phys. Eng. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1088\/0967-3334\/35\/12\/2369","article-title":"Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis","volume":"35","author":"Couceiro","year":"2014","journal-title":"Physiol. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105005","DOI":"10.1088\/1361-6579\/aadf1e","article-title":"Adaptive template matching of photoplethysmogram pulses to detect motion artefact","volume":"39","author":"Lim","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s13534-019-00097-w","article-title":"Current progress of photoplethysmography and SPO2 for health monitoring","volume":"9","author":"Tamura","year":"2019","journal-title":"Biomed. Eng. Lett."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Hassani, A., and Foruzan, A.H. (2019). Improved PPG-based estimation of the blood pressure using latent space features. Signal. Image Video Process.","DOI":"10.1007\/s11760-019-01460-1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4587","DOI":"10.1007\/s00542-018-3892-4","article-title":"Theoretical developments and clinical experiments of measuring blood flow volume (BFV) at arteriovenous fistula (AVF) using a photoplethysmography (PPG) sensor","volume":"24","author":"Chiang","year":"2018","journal-title":"Microsyst. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sanuki, H., Fukui, R., Inajima, T. (2020, January 30). Cuff-less Calibration-free Blood Pressure Estimation under Ambulatory Environment using Pulse Wave Velocity and Photoplethysmogram Signals. Available online: https:\/\/www.scitepress.org\/Papers\/2017\/61125\/61125.pdf.","DOI":"10.5220\/0006112500420048"},{"key":"ref_30","unstructured":"Wu, Y., and Zhong, S. (2020, January 30). Noninvasive Blood Pressure Measurement Based on Photoplethysmography. Available online: https:\/\/www.researchgate.net\/profile\/Ming_Liu6\/publication\/320772542_Effects_of_Sample_Tilt_on_Vickers_Indentation_Hardness\/links\/5b873b27a6fdcc5f8b71068e\/Effects-of-Sample-Tilt-on-Vickers-Indentation-Hardness.pdf#page=125."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"025010","DOI":"10.1088\/1361-6579\/aa996d","article-title":"Cuff-less blood pressure measurement from dual-channel photoplethysmographic signals via peripheral pulse transit time with singular spectrum analysis","volume":"39","author":"Wang","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","article-title":"Deep Learning for Health Informatics","volume":"21","author":"Rav","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.20","article-title":"A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China","volume":"5","author":"Liang","year":"2018","journal-title":"Sci. Data"},{"key":"ref_35","first-page":"605","article-title":"Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background","volume":"3","author":"Imandoust","year":"2013","journal-title":"Int. J. Eng. Res. Appl."},{"key":"ref_36","first-page":"95","article-title":"Skewness and Kurtosis in Function of Selection of Network Traffic Distribution","volume":"7","year":"2010","journal-title":"Acta Polytech. Hung."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.cmpb.2019.05.004","article-title":"A new approach for arrhythmia classification using deep coded features and LSTM networks","volume":"176","author":"Yildirim","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_38","unstructured":"Brownlee, J. (2019, February 01). How to Scale Data for Long Short-Term Memory Networks in Python. Available online: https:\/\/machinelearningmastery.com\/how-to-scale-data-for-long-short-term-memory-networks-in-python."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/93\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:56:14Z","timestamp":1760172974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/93"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,9]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["info11020093"],"URL":"https:\/\/doi.org\/10.3390\/info11020093","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,9]]}}}