{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:33:09Z","timestamp":1760236389371,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"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>Non-invasive measurement of physiological parameters and indicators, specifically among the elderly, is of utmost importance for personal health monitoring. In this study, we focused on photoplethysmography (PPG), and developed a regression model that calculates variables from the second (SDPPG) and third (TDPPG) derivatives of the PPG pulse that can observe the inflection point of the pulse wave measured by a wearable PPG device. The PPG pulse at the earlobe was measured for 3 min in 84 elderly Korean women (age: 71.19 \u00b1 6.97 years old). Based on the PPG-based cardiovascular function, we derived additional variables from TDPPG, in addition to the aging variable to predict the age. The Aging Index (AI) from SDPPG and Sum of TDPPG variables were calculated in the second and third differential forms of PPG. The variables that significantly correlated with age were c\/a, Tac, AI of SDPPG, sum of TDPPG, and correlation coefficient \u2018r\u2019 of the model. In multiple linear regression analysis, the r value of the model was 0.308, and that using deep learning on the model was 0.839. Moreover, the possibility of improving the accuracy of the model using supervised deep learning techniques, rather than the addition of datasets, was confirmed.<\/jats:p>","DOI":"10.3390\/s21237782","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7782","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Age-Related Changes in the Characteristics of the Elderly Females Using the Signal Features of an Earlobe Photoplethysmogram"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2661-349X","authenticated-orcid":false,"given":"Jeong-Woo","family":"Seo","sequence":"first","affiliation":[{"name":"Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34504, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5287-7942","authenticated-orcid":false,"given":"Jungmi","family":"Choi","sequence":"additional","affiliation":[{"name":"Human Anti-Aging Standards Research Institute, Uiryeong, Gyungnam 52151, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4144-8162","authenticated-orcid":false,"given":"Kunho","family":"Lee","sequence":"additional","affiliation":[{"name":"Gwangju Alzheimer\u2019s Disease and Related Dementias (GARD) Cohort Research Center, Chosun University, Gwangju 61452, Korea"},{"name":"Department of Biomedical Science, Chosun University, Gwangju 61452, Korea"},{"name":"Dementia Research Group, Korea Brain Research Institute, Daegu 41602, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0408-5569","authenticated-orcid":false,"given":"Jaeuk U.","family":"Kim","sequence":"additional","affiliation":[{"name":"Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34504, Korea"},{"name":"Korean Convergence Medicine, University of Science and Technology, Daejeon 34054, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104111","DOI":"10.17485\/ijst\/2017\/v10i12\/104111","article-title":"Predicting arterial stiffness from physiological characteristics of photoplethysmography signals quantified through second derivative","volume":"10","author":"Mohanalakshmi","year":"2017","journal-title":"Indian J. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Moraes, J.L., Rocha, M.X., Vasconcelos, G.G., Filho, J.E., Albuquerque, V.H., and Alexandria, A.R. (2018). Advances in photopletysmography signal analysis for biomedical applications. Sensors, 18.","DOI":"10.3390\/s18061894"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/hr.2016.123","article-title":"Second derivative of the finger photoplethysmogram and cardiovascular mortality in middle-aged and elderly Japanese women","volume":"40","author":"Inoue","year":"2017","journal-title":"Hypertens. Res."},{"key":"ref_4","first-page":"205","article-title":"Acceleration plethysmography to evaluate aging effect in cardiovascular system. Using new criteria of four wave patterns","volume":"21","author":"Takada","year":"1996","journal-title":"Clin. Trial"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"198","DOI":"10.3389\/fphys.2019.00198","article-title":"Quantitative comparison of photoplethysmography waveform characteristics: Effect of measurement site","volume":"10","author":"Hartmann","year":"2019","journal-title":"Front. Physiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1088\/0967-3334\/24\/2\/306","article-title":"Age-related changes in the characteristics of the photoplethysmographic pulse shape at various body sites","volume":"24","author":"Allen","year":"2003","journal-title":"Physiol. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"40","DOI":"10.4093\/dmj.2013.37.1.40","article-title":"Factor structure of indices of the second derivative of the finger photoplethysmogram with metabolic components and other cardiovascular risk indicators","volume":"37","author":"Kawada","year":"2013","journal-title":"Diabetes Metab. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1161\/01.HYP.32.2.365","article-title":"Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform","volume":"32","author":"Takazawa","year":"1998","journal-title":"Hypertension"},{"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","first-page":"19756","DOI":"10.1038\/s41598-020-76816-6","article-title":"Prediction of vascular aging based on smartphone acquired PPG signals","volume":"10","author":"Curti","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.medengphy.2019.07.009","article-title":"Data-driven assessment of cardiovascular ageing through multisite photoplethysmography and electrocardiography","volume":"73","author":"Chiarelli","year":"2019","journal-title":"Med. Eng. Phys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104240","DOI":"10.1016\/j.mvr.2021.104240","article-title":"Discrimination of vascular aging using the arterial pulse spectrum and machine-learning analysis","volume":"139","author":"Hsiu","year":"2022","journal-title":"Microvasc. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rinkevi\u010dius, M., Kontaxis, S., Gil, E., Bail\u00f3n, R., L\u00e1zaro, J., Laguna, P., and Marozas, V. (2019, January 8\u201311). Photoplethysmogram Signal Morphology-Based Stress Assessment. Proceedings of the 2019 Computing in Cardiology (CinC), Singapore.","DOI":"10.22489\/CinC.2019.126"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14","DOI":"10.2174\/157340312801215782","article-title":"On the analysis of fingertip photoplethysmogram signals","volume":"8","author":"Elgendi","year":"2012","journal-title":"Curr. Cardiol. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"266","DOI":"10.2478\/v10048-012-0036-3","article-title":"The analysis of PPG morphology: Investigating the effects of aging on arterial compliance","volume":"12","author":"Yousef","year":"2012","journal-title":"Meas. Sci. Rev."},{"key":"ref_16","first-page":"1203","article-title":"Comparison of the neural network model and linear regression model for predicting the intermingled yarn breaking strength and elongation","volume":"105","author":"Kuvvetli","year":"2014","journal-title":"J. Text. Inst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"054001","DOI":"10.1088\/1361-6579\/aabe6a","article-title":"Assessing mental stress from the photoplethysmogram: A numerical study","volume":"15","author":"Charlton","year":"2018","journal-title":"J. Physiol. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3991\/ijoe.v16i09.13577","article-title":"Methods of extracting feature from photoplethysmogram waveform for non-invasive diagnostic applications","volume":"16","author":"Hafifah","year":"2020","journal-title":"Int. J. Online Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s10558-007-9037-5","article-title":"Analysis of the effect of ageing on rising edge characteristics of the photoplethysmogram using a modified windkessel model","volume":"7","author":"Zahedi","year":"2007","journal-title":"Cardiovasc. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"169035","DOI":"10.1155\/2013\/169035","article-title":"New photoplethysmographic signal analysis algorithm for arterial stiffness estimation","volume":"2013","author":"Pilt","year":"2013","journal-title":"Sci. World J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"633","DOI":"10.5897\/SRE2015.6322","article-title":"Photoplethysmogram second derivative review: Analysis and applications","volume":"10","author":"Yousef","year":"2015","journal-title":"Sci. Res. 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