{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T23:15:39Z","timestamp":1776381339601,"version":"3.51.2"},"reference-count":61,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002343","name":"l\u2019Institut de recherche Robert-Sauv\u00e9 en sant\u00e9 et en s\u00e9curit\u00e9 du travail (IRSST)","doi-asserted-by":"publisher","award":["2020-0006"],"award-info":[{"award-number":["2020-0006"]}],"id":[{"id":"10.13039\/501100002343","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping.<\/jats:p>","DOI":"10.3390\/s24134317","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T06:04:18Z","timestamp":1719986658000},"page":"4317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1267-2952","authenticated-orcid":false,"given":"Fabrice","family":"Vaussenat","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, \u00c9cole de Technologie Sup\u00e9rieure, Universit\u00e9 du Qu\u00e9bec, Montr\u00e9al, QC H3C 1K3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2883-0754","authenticated-orcid":false,"given":"Abhiroop","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, \u00c9cole de Technologie Sup\u00e9rieure, Universit\u00e9 du Qu\u00e9bec, Montr\u00e9al, QC H3C 1K3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9367-2919","authenticated-orcid":false,"given":"Philippe","family":"Boudreau","sequence":"additional","affiliation":[{"name":"Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montr\u00e9al, QC H4H 1R3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3896-2710","authenticated-orcid":false,"given":"Diane B.","family":"Boivin","sequence":"additional","affiliation":[{"name":"Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montr\u00e9al, QC H4H 1R3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9484-7218","authenticated-orcid":false,"given":"Ghyslain","family":"Gagnon","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, \u00c9cole de Technologie Sup\u00e9rieure, Universit\u00e9 du Qu\u00e9bec, Montr\u00e9al, QC H3C 1K3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0092-5241","authenticated-orcid":false,"given":"Sylvain G.","family":"Cloutier","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, \u00c9cole de Technologie Sup\u00e9rieure, Universit\u00e9 du Qu\u00e9bec, Montr\u00e9al, QC H3C 1K3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"ref_1","first-page":"126","article-title":"Overview of sleep & sleep disorders","volume":"131","author":"Chokroverty","year":"2010","journal-title":"Indian J. Med. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Coutts, L.V., Plans, D., Brown, A.W., and Collomosse, J. (2020). Deep learning with wearable based heart rate variability for prediction of mental and general health. J. Biomed. Inform., 112.","DOI":"10.1016\/j.jbi.2020.103610"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1152\/physrev.00032.2011","article-title":"Control of Sleep and Wakefulness","volume":"92","author":"Brown","year":"2012","journal-title":"Physiol. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/nrn2762","article-title":"The memory function of sleep","volume":"11","author":"Diekelmann","year":"2010","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_5","first-page":"85","article-title":"Overview of Circadian Rhythms","volume":"25","author":"Vitaterna","year":"2001","journal-title":"Alcohol Res. Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/j.ncl.2012.08.011","article-title":"Circadian Rhythm Sleep Disorders","volume":"30","author":"Zhu","year":"2012","journal-title":"Neurol. Clin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.5664\/jcsm.8482","article-title":"Prevalence of narcolepsy and other sleep disorders and frequency of diagnostic tests from 2013 to 2016 in insured patients actively seeking care","volume":"16","author":"Acquavella","year":"2020","journal-title":"J. Clin. Sleep Med."},{"key":"ref_8","unstructured":"Institute of Medicine, Board on Health Sciences Policy, Committee on Sleep Medicine and Research (2006). Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem, National Academies Press."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1159\/000236900","article-title":"Sleep Disorders in the Older Adult\u2014A Mini-Review","volume":"56","author":"Neikrug","year":"2009","journal-title":"Gerontology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/B978-0-444-64032-1.00025-4","article-title":"Chapter 25\u2014Polysomnography","volume":"Volume 160","author":"Levin","year":"2019","journal-title":"Handbook of Clinical Neurology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.bpa.2014.08.006","article-title":"Photoplethysmography","volume":"28","author":"Alian","year":"2014","journal-title":"Best Pract. Res. Clin. Anaesthesiol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Blaszczyk, B., Wieczorek, T., Michalek-Zrabkowska, M., Wieckiewicz, M., Mazur, G., and Martynowicz, H. (2023). Frontiers | Polysomnography findings in sleep-related eating disorder: A systematic review and case report. Front. Psychiatry, 14.","DOI":"10.3389\/fpsyt.2023.1139670"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, S., Faust, O., Seoni, S., Chakraborty, S., Barua, P.D., Loh, H.W., Elphick, H., Molinari, F., and Acharya, U.R. (2022). A review of automated sleep disorder detection. Comput. Biol. Med., 150.","DOI":"10.1016\/j.compbiomed.2022.106100"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3390\/clockssleep4010009","article-title":"Measuring Sleep Health Disparities with Polysomnography: A Systematic Review of Preliminary Findings","volume":"4","author":"Sosso","year":"2022","journal-title":"Clocks Sleep"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ghourabi, A., Ghazouani, H., and Barhoumi, W. (2020, January 3\u20135). Driver Drowsiness Detection Based on Joint Monitoring of Yawning, Blinking and Nodding. Proceedings of the 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP51029.2020.9266160"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.smrv.2011.02.005","article-title":"Heart rate variability, sleep and sleep disorders","volume":"16","author":"Stein","year":"2012","journal-title":"Sleep Med. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e15557","DOI":"10.14814\/phy2.15557","article-title":"Temporal relationships among changes in the RR-interval and the powers of the low- and high-frequency components of heart rate variability in normal subjects","volume":"11","author":"Yokobori","year":"2023","journal-title":"Physiol. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.sleep.2022.10.018","article-title":"Associations between sleep-related heart rate variability and both sleep and symptoms of depression and anxiety: A systematic review","volume":"101","author":"Correia","year":"2023","journal-title":"Sleep Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.bbe.2017.02.003","article-title":"Classification of falling asleep states using HRV analysis","volume":"37","author":"Piotrowski","year":"2017","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1016\/j.matpr.2020.08.420","article-title":"Overcome the challenges in bio-medical instruments using IOT\u2013A review","volume":"45","author":"Karthick","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e8882378","DOI":"10.1155\/2020\/8882378","article-title":"IoT Healthcare: Design of Smart and Cost-Effective Sleep Quality Monitoring System","volume":"2020","author":"Saleem","year":"2020","journal-title":"J. Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hung, P. (2017, January 7\u20138). Estimating respiration rate using an accelerometer sensor. Proceedings of the 8th International Conference on Computational Systems-Biology and Bioinformatics, Nha Trang City, Viet Nam.","DOI":"10.1145\/3156346.3156349"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9","DOI":"10.22381\/ajmr8220211","article-title":"Artificial Intelligence-enabled Wearable Medical Devices, Clinical and Diagnostic Decision Support Systems, and Internet of Things-based Healthcare Applications in COVID-19 Prevention, Screening, and Treatment","volume":"8","author":"Barnes","year":"2021","journal-title":"Am. J. Med. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dias, D., and Paulo Silva Cunha, J. (2018). Wearable Health Devices\u2014Vital Sign Monitoring, Systems and Technologies. Sensors, 18.","DOI":"10.3390\/s18082414"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e27","DOI":"10.1183\/20734735.008417","article-title":"Wearable technology: Role in respiratory health and disease","volume":"13","author":"Aliverti","year":"2017","journal-title":"Breathe"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101897","DOI":"10.1016\/j.smrv.2024.101897","article-title":"Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice","volume":"74","author":"Yue","year":"2024","journal-title":"Sleep Med. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sekkal, R.N., Bereksi-Reguig, F., Ruiz-Fernandez, D., Dib, N., and Sekkal, S. (2022). Automatic sleep stage classification: From classical machine learning methods to deep learning. Biomed. Signal Process. Control, 77.","DOI":"10.1016\/j.bspc.2022.103751"},{"key":"ref_28","first-page":"2073","article-title":"Accurate Deep Learning-Based Sleep Staging in a Clinical Population With Suspected Obstructive Sleep Apnea","volume":"24","author":"Korkalainen","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/s41746-024-01016-9","article-title":"Evaluating reliability in wearable devices for sleep staging","volume":"7","author":"Birrer","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102461","DOI":"10.1016\/j.isci.2021.102461","article-title":"Recent advances in wearable sensors and portable electronics for sleep monitoring","volume":"24","author":"Kwon","year":"2021","journal-title":"iScience"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"98450","DOI":"10.1109\/ACCESS.2022.3206782","article-title":"Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review","volume":"10","author":"Diab","year":"2022","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.apenergy.2019.02.052","article-title":"Deep learning-based feature engineering methods for improved building energy prediction","volume":"240","author":"Fan","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.bbe.2015.11.001","article-title":"Automatic sleep scoring using statistical features in the EMD domain and ensemble methods","volume":"36","author":"Hassan","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7046","DOI":"10.1016\/j.eswa.2013.06.023","article-title":"Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels","volume":"40","author":"Khalighi","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MEMB.2009.934244","article-title":"Time\u2013frequency analysis of biosignals","volume":"28","author":"Addison","year":"2009","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_36","unstructured":"Chatlapalli, S., Nazeran, H., Melarkod, V., Krishnam, R., Estrada, E., Pamula, Y., and Cabrera, S. (2004, January 1\u20135). Accurate derivation of heart rate variability signal for detection of sleep disordered breathing in children. Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, USA."},{"key":"ref_37","unstructured":"Enderle, J.D., Blanchard, S.M., and Bronzino, J.D. (2005). 10\u2014BIOSIGNAL PROCESSING. Introduction to Biomedical Engineering, Academic Press. [2nd ed.]. Biomedical Engineering."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A Real-Time QRS Detection Algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Arzeno, N.M., Poon, C.S., and Deng, Z.D. (September, January 30). Quantitative Analysis of QRS Detection Algorithms Based on the First Derivative of the ECG. Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA.","DOI":"10.1109\/IEMBS.2006.260051"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.5665\/sleep.3230","article-title":"Circadian Variation of Heart Rate Variability Across Sleep Stages","volume":"36","author":"Boudreau","year":"2013","journal-title":"Sleep"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"665","DOI":"10.5664\/jcsm.6576","article-title":"AASM Scoring Manual Updates for 2017 (Version 2.4)","volume":"13","author":"Berry","year":"2017","journal-title":"J. Clin. Sleep Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.biopsycho.2007.04.001","article-title":"Accuracy of the LifeShirt\u00ae (Vivometrics) in the detection of cardiac rhythms","volume":"75","author":"Heilman","year":"2007","journal-title":"Biol. Psychol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1080\/00031305.1989.10475612","article-title":"Some Implementations of the Boxplot","volume":"43","author":"Frigge","year":"1989","journal-title":"Am. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Dunn, S., Constantinides, A., and Moghe, P.V. (2005). Numerical Methods in Biomedical Engineering, Elsevier.","DOI":"10.1016\/B978-012186031-8\/50005-4"},{"key":"ref_45","unstructured":"Kumar, A. (2019). Mastering Pandas: A Complete Guide to Pandas, from Installation to Advanced Data Analysis Techniques, Packt Publishing Ltd.. [2nd ed.]."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Trardi, Y., Ananou, B., and Ouladsine, M. (2022, January 13\u201315). Computationally Efficient Algorithm for Atrial Fibrillation Detection using Linear and Geometric Features of RR Time-Series Derivatives. Proceedings of the 2022 International Conference on Control, Automation and Diagnosis (ICCAD), Lisbon, Portugal.","DOI":"10.1109\/ICCAD55197.2022.9853910"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"H946","DOI":"10.1152\/ajpheart.1998.275.3.H946","article-title":"Dynamic heart rate variability: A tool for exploring sympathovagal balance continuously during sleep in men","volume":"275","author":"Otzenberger","year":"1998","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kamath, M.V., Watanabe, M., and Upton, A. (2012). Heart Rate Variability (HRV) Signal Analysis: Clinical Applications, CRC Press.","DOI":"10.1201\/b12756-2"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Murukesan, L., Murugappan, M., and Iqbal, M. (2013, January 8\u201310). Sudden cardiac death prediction using ECG signal derivative (Heart Rate Variability): A review. Proceedings of the 2013 IEEE 9th International Colloquium on Signal Processing and Its Applications, Kuala Lumpur, Malaysia.","DOI":"10.1109\/CSPA.2013.6530054"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Jeyhani, V., Mahdiani, S., Peltokangas, M., and Vehkaoja, A. (2015, January 25\u201329). Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319747"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"104807","DOI":"10.1016\/j.engappai.2022.104807","article-title":"Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0","volume":"111","author":"Portillo","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41746-020-0291-x","article-title":"Deep learning for automated sleep staging using instantaneous heart rate","volume":"3","author":"Sridhar","year":"2020","journal-title":"npj Digit. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s40101-016-0113-7","article-title":"The physiological basis and measurement of heart rate variability in humans","volume":"35","author":"Draghici","year":"2016","journal-title":"J. Physiol. Anthropol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"235","DOI":"10.30773\/pi.2017.08.17","article-title":"Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature","volume":"15","author":"Kim","year":"2018","journal-title":"Psychiatry Investig."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s10286-018-0560-9","article-title":"Autonomic regulation during sleep and wakefulness: A review with implications for defining the pathophysiology of neurological disorders","volume":"28","author":"Fink","year":"2018","journal-title":"Clin. Auton. Res. Off. J. Clin. Auton. Res. Soc."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"15405","DOI":"10.1038\/ncomms15405","article-title":"Deep sleep maintains learning efficiency of the human brain","volume":"8","author":"Fattinger","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"7860","DOI":"10.1038\/s41598-019-44201-7","article-title":"Voluntary upregulation of heart rate variability through biofeedback is improved by mental contemplative training","volume":"9","author":"Bornemann","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"17645","DOI":"10.1038\/s41598-022-22584-4","article-title":"Resting-state heart rate variability (HRV) mediates the association between perceived chronic stress and ambiguity avoidance","volume":"12","author":"Jiryis","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/TBCAS.2008.2008817","article-title":"Sleep and Wake Classification With ECG and Respiratory Effort Signals","volume":"3","author":"Karlen","year":"2009","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"104535","DOI":"10.1016\/j.jmva.2019.104535","article-title":"A bootstrap-based KPSS test for functional time series","volume":"174","author":"Chen","year":"2019","journal-title":"J. Multivar. Anal."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"120632","DOI":"10.1016\/j.eswa.2023.120632","article-title":"A novel forecasting strategy for improving the performance of deep learning models","volume":"230","author":"Livieris","year":"2023","journal-title":"Expert Syst. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4317\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:09:31Z","timestamp":1760108971000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,3]]},"references-count":61,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24134317"],"URL":"https:\/\/doi.org\/10.3390\/s24134317","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,3]]}}}