{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:55:09Z","timestamp":1774122909163,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11517-024-03116-w","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T02:01:18Z","timestamp":1714960878000},"page":"2853-2865","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Classification of exercise fatigue levels by multi-class SVM from ECG and HRV"],"prefix":"10.1007","volume":"62","author":[{"given":"Yuru","family":"Chen","sequence":"first","affiliation":[]},{"given":"Huanmin","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Xinhua","family":"Su","sequence":"additional","affiliation":[]},{"given":"Xinxin","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"key":"3116_CR1","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/978-3-031-13822-5_61","volume-title":"International Conference on Intelligent Robotics and Applications","author":"J Liu","year":"2022","unstructured":"Liu J, Zeng J, Wang Z, Liu H (2022) Modeling and Recognition of Moveme-nt-Inducing Fatigue State based on ECG Signal. In: International Conference on Intelligent Robotics and Applications. Springer International Publishing, Cham, pp 677\u2013685. https:\/\/doi.org\/10.1007\/978-3-031-13822-5_61"},{"key":"3116_CR2","doi-asserted-by":"publisher","first-page":"104663","DOI":"10.1016\/j.bspc.2023.104663","volume":"83","author":"W Feng","year":"2023","unstructured":"Feng W, Zeng K, Zeng X, Chen J, Peng H, Hu B, Liu G (2023) Predicting physical fatigue in athletes in rope skipping training using ECG signals. Biomed Signal Process Ctrl 83:104663. https:\/\/doi.org\/10.1016\/j.bspc.2023.104663","journal-title":"Biomed Signal Process Ctrl"},{"key":"3116_CR3","doi-asserted-by":"publisher","first-page":"965974","DOI":"10.3389\/fphys.2022.965974","volume":"13","author":"H Zhu","year":"2022","unstructured":"Zhu H, Ji Y, Wang B, Kang Y (2022) Exercise fatigue diagnosis method based on sh-ort-time Fourier transform and convolutional neural network. Front Physiol 13:965974. https:\/\/doi.org\/10.3389\/fphys.2022.965974","journal-title":"Front Physiol"},{"issue":"2","key":"3116_CR4","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1177\/1751143719853744","volume":"21","author":"BW Johnston","year":"2020","unstructured":"Johnston BW, Barrett-Jolley R, Krige A, Welters ID (2020) Heart rate variability: Mea-surement and emerging use in critical care medicine. J Intensive Care Soc 21(2):148\u2013157. https:\/\/doi.org\/10.1177\/1751143719853744","journal-title":"J Intensive Care Soc"},{"key":"3116_CR5","doi-asserted-by":"publisher","unstructured":"Kim HG, Cheon EJ, Bai DS, Lee YH, Koo BH (2018) Stress and heart rate variabilit-y: a meta-analysis and review of the literature. Psychiatr Invest (15, 3):235. https:\/\/doi.org\/10.30773\/pi.2017.08.17","DOI":"10.30773\/pi.2017.08.17"},{"key":"3116_CR6","doi-asserted-by":"publisher","first-page":"105667","DOI":"10.1016\/j.cmpb.2020.105667","volume":"196","author":"S Chen","year":"2020","unstructured":"Chen S, Xu K, Zheng X, Li J, Fan B, Yao X, Li Z (2020) Linear and nonlinear ana-lyses of normal and fatigue heart rate variability signals for miners in high-altitude and c-old areas. Comput Methods Prog Biomed 196:105667. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105667","journal-title":"Comput Methods Prog Biomed"},{"issue":"12","key":"3116_CR7","doi-asserted-by":"publisher","first-page":"16494","DOI":"10.3390\/s131216494","volume":"13","author":"G Li","year":"2013","unstructured":"Li G, Chung WY (2013) Detection of driver drowsiness using wavelet analysis of hea-rt rate variability and a support vector machine classifier. Sensors 13(12):16494\u201316511. https:\/\/doi.org\/10.3390\/s131216494","journal-title":"Sensors"},{"key":"3116_CR8","unstructured":"Makivi\u0107 B, Niki\u0107 Djordjevi\u0107 M, Willis MS (2013) Heart Rate Variability (HRV) as a tool for diagnostic and monitoring performance in sport and physical activities. J Exercise Physiol Online 16(3)"},{"issue":"1-2","key":"3116_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.autneu.2012.10.007","volume":"173","author":"T Leti","year":"2013","unstructured":"Leti T, Bricout VA (2013) Interest of analyses of heart rate variability in the preventio-n of fatigue states in senior runners. Auton Neurosci 173(1-2):14\u201321. https:\/\/doi.org\/10.1016\/j.autneu.2012.10.007","journal-title":"Auton Neurosci"},{"issue":"2","key":"3116_CR10","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1519\/JSC.0000000000003549","volume":"36","author":"RG Hebisz","year":"2022","unstructured":"Hebisz RG, Hebisz P, Zato\u0144 MW (2022) Heart rate variability after sprint interval tr-aining in cyclists and implications for assessing physical fatigue. J Strength Cond Res 36(2):558. https:\/\/doi.org\/10.1519\/JSC.0000000000003549","journal-title":"J Strength Cond Res"},{"issue":"9","key":"3116_CR11","doi-asserted-by":"publisher","first-page":"3199","DOI":"10.3390\/s22093199","volume":"22","author":"Z Ni","year":"2022","unstructured":"Ni Z, Sun F, Li Y (2022) Heart Rate Variability-Based Subjective Physical Fatigue Assessment. Sensors 22(9):3199. https:\/\/doi.org\/10.3390\/s22093199","journal-title":"Sensors"},{"issue":"1","key":"3116_CR12","doi-asserted-by":"publisher","first-page":"8515","DOI":"10.1038\/s41598-023-35717-0","volume":"13","author":"L Brockmann","year":"2023","unstructured":"Brockmann L, Hunt KJ (2023) Heart rate variability changes with respect to time and exercise intensity during heart-rate-controlled steady-state treadmill running. Sci Rep 13(1):8515. https:\/\/doi.org\/10.1038\/s41598-023-35717-0","journal-title":"Sci Rep"},{"key":"3116_CR13","doi-asserted-by":"publisher","first-page":"582","DOI":"10.3389\/fphys.2019.00582","volume":"10","author":"C Schneider","year":"2019","unstructured":"Schneider C, Wiewelhove T, Raeder C, Flatt AA, Hoos O, Hottenrott L, Ferrauti A (2019) Heart rate variability monitoring during strength and high-intensity interval training overload microcycles. Front Physiol 10:582. https:\/\/doi.org\/10.3389\/fphys.2019.00582","journal-title":"Front Physiol"},{"key":"3116_CR14","doi-asserted-by":"publisher","first-page":"106696","DOI":"10.1016\/j.cmpb.2022.106696","volume":"217","author":"M Moya-Ramon","year":"2022","unstructured":"Moya-Ramon M, Mateo-March M, Pe\u00f1a-Gonz\u00e1lez I, Zabala M, Javaloyes A (2022) V-alidity and reliability of different smartphones applications to measure HRV during short and ultra-short measurements in elite athletes. Comput Methods Prog Biomed 217:106696. https:\/\/doi.org\/10.1016\/j.cmpb.2022.106696","journal-title":"Comput Methods Prog Biomed"},{"issue":"3","key":"3116_CR15","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1109\/TAFFC.2020.3014842","volume":"13","author":"P Sarkar","year":"2020","unstructured":"Sarkar P, Etemad A (2020) Self-supervised ECG representation learning for emotion r-ecognition. IEEE Trans Affect Comput 13(3):1541\u20131554. https:\/\/doi.org\/10.1109\/TAFFC.2020.3014842","journal-title":"IEEE Trans Affect Comput"},{"key":"3116_CR16","doi-asserted-by":"publisher","first-page":"103580","DOI":"10.1016\/j.bspc.2022.103580","volume":"75","author":"S Nita","year":"2022","unstructured":"Nita S, Bitam S, Heidet M, Mellouk A (2022) A new data augmentation convolution-al neural network for human emotion recognition based on ECG signals. Biomed Signal Process Ctrl 75:103580. https:\/\/doi.org\/10.1016\/j.bspc.2022.103580","journal-title":"Biomed Signal Process Ctrl"},{"key":"3116_CR17","doi-asserted-by":"publisher","first-page":"115950","DOI":"10.1016\/j.eswa.2021.115950","volume":"187","author":"N Salari","year":"2022","unstructured":"Salari N, Hosseinian-Far A, Mohammadi M, Ghasemi H, Khazaie H, Daneshkhah A, Ahmadi A (2022) Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review. Expert Syst Appl 187:115950. https:\/\/doi.org\/10.1016\/j.eswa.2021.115950","journal-title":"Expert Syst Appl"},{"key":"3116_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3151947","volume":"71","author":"M Bahrami","year":"2022","unstructured":"Bahrami M, Forouzanfar M (2022) Sleep apnea detection from single-lead ECG: A c-omprehensive analysis of machine learning and deep learning algorithms. IEEE Trans Instrum Meas 71:1\u201311. https:\/\/doi.org\/10.1109\/TIM.2022.3151947","journal-title":"IEEE Trans Instrum Meas"},{"key":"3116_CR19","doi-asserted-by":"publisher","first-page":"104796","DOI":"10.1016\/j.bspc.2023.104796","volume":"84","author":"SM Malakouti","year":"2023","unstructured":"Malakouti SM (2023) Heart disease classification based on ECG using machine learni-ng models. Biomed Signal Process Ctrl 84:104796. https:\/\/doi.org\/10.1016\/j.bspc.2023.104796","journal-title":"Biomed Signal Process Ctrl"},{"issue":"9","key":"3116_CR20","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1109\/JBHI.2021.3066347","volume":"25","author":"D Bertsimas","year":"2021","unstructured":"Bertsimas D, Mingardi L, Stellato B (2021) Machine learning for real-time heart dise-ase prediction. IEEE J Biomed Health Inform 25(9):3627\u20133637. https:\/\/doi.org\/10.1109\/JBHI.2021.3066347","journal-title":"IEEE J Biomed Health Inform"},{"issue":"11","key":"3116_CR21","doi-asserted-by":"publisher","first-page":"8755","DOI":"10.1007\/s00521-022-06889-z","volume":"34","author":"AS Eltrass","year":"2022","unstructured":"Eltrass AS, Tayel MB, Ammar AI (2022) Automated ECG multi-class classification s-ystem based on combining deep learning features with HRV and ECG measures. Neural Comput & Applic 34(11):8755\u20138775","journal-title":"Neural Comput & Applic"},{"key":"3116_CR22","doi-asserted-by":"publisher","unstructured":"Kazemnejad A, Gordany P, Sameni R (2021) An open\u2013access simultaneous electrocardiogram and phonocardiogram database. bioRxiv. https:\/\/doi.org\/10.1101\/2021.05.17.444563","DOI":"10.1101\/2021.05.17.444563"},{"key":"3116_CR23","doi-asserted-by":"publisher","unstructured":"Wang B, Zhu H (2022) The recognition method of athlete exercise intensity based on ECG and PCG. Comput Math Methods Med 2022. https:\/\/doi.org\/10.1155\/2022\/5741787","DOI":"10.1155\/2022\/5741787"},{"issue":"4","key":"3116_CR24","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.3390\/s21041061","volume":"21","author":"K Hinde","year":"2021","unstructured":"Hinde K, White G, Armstrong N (2021) Wearable devices suitable for monitoring tw-enty four hour heart rate variability in military populations. Sensors 21(4):1061. https:\/\/doi.org\/10.3390\/s21041061","journal-title":"Sensors"},{"issue":"10","key":"3116_CR25","doi-asserted-by":"publisher","first-page":"143","DOI":"10.29322\/IJSRP.9.10.2019.p9420","volume":"9","author":"S Tammina","year":"2019","unstructured":"Tammina S (2019) Transfer learning using vgg-16 with deep convolutional neural net-work for classifying images. Int J Sci Res Publ (IJSRP) 9(10):143\u2013150. https:\/\/doi.org\/10.29322\/IJSRP.9.10.2019.p9420","journal-title":"Int J Sci Res Publ (IJSRP)"},{"key":"3116_CR26","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.psychsport.2019.05.001","volume":"44","author":"LS Fortes","year":"2019","unstructured":"Fortes LS, Lima-Junior D, Nascimento-J\u00fanior JR, Costa EC, Matta MO, Ferreira ME (2019) Effect of exposure time to smartphone apps on passing decision-making in male s-occer athletes. Psychol Sport Exerc 44:35\u201341. https:\/\/doi.org\/10.1016\/j.psychsport.2019.05.001","journal-title":"Psychol Sport Exerc"},{"issue":"6","key":"3116_CR27","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1519\/JSC.0000000000003337","volume":"34","author":"A Javaloyes","year":"2020","unstructured":"Javaloyes A, Sarabia JM, Lamberts RP, Plews D, Moya-Ramon M (2020) Training pr-escription guided by heart rate variability vs. block periodization in well-trained cyclists. T-he. J Strength Cond Res 34(6):1511\u20131518. https:\/\/doi.org\/10.1519\/JSC.0000000000003337","journal-title":"J Strength Cond Res"},{"key":"3116_CR28","doi-asserted-by":"publisher","unstructured":"Makowski D, Pham T, Lau ZJ, Brammer JC, Lespinasse F, Pham H, Chen SA (2021) NeuroKit2: A Python toolbox for neurophysiological signal processing. Behav Res Methods:1\u20138. https:\/\/doi.org\/10.31234\/osf.io\/eyd62","DOI":"10.31234\/osf.io\/eyd62"},{"issue":"2","key":"3116_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classificatio-n evaluations. Int J Data Mining Knowledge Manag Process 5(2):1. https:\/\/doi.org\/10.5121\/ijdkp.2015.5201","journal-title":"Int J Data Mining Knowledge Manag Process"},{"issue":"1","key":"3116_CR30","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/s12911-022-01808-7","volume":"22","author":"D Li","year":"2022","unstructured":"Li D, Chen C (2022) Research on exercise fatigue estimation method of Pilates reha-bilitation based on ECG and sEMG feature fusion. BMC Med Inform Decis Mak 22(1):67. https:\/\/doi.org\/10.1186\/s12911-022-01808-7","journal-title":"BMC Med Inform Decis Mak"},{"issue":"9","key":"3116_CR31","doi-asserted-by":"publisher","first-page":"3003","DOI":"10.3390\/s21093003","volume":"21","author":"T Pan","year":"2021","unstructured":"Pan T, Wang H, Si H, Li Y, Shang L (2021) Identification of pilots\u2019fatigue status ba-sed on electrocardiogram signals. Sensors 21(9):3003. https:\/\/doi.org\/10.3390\/s21093003","journal-title":"Sensors"},{"key":"3116_CR32","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.ijmedinf.2018.08.010","volume":"119","author":"S Huang","year":"2018","unstructured":"Huang S, Li J, Zhang P, Zhang W (2018) Detection of mental fatigue state with we-arable ECG devices. Int J Med Inform 119:39\u201346. https:\/\/doi.org\/10.1016\/j.ijmedinf.2018.08.010","journal-title":"Int J Med Inform"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03116-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03116-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03116-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T05:48:31Z","timestamp":1723873711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03116-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,6]]},"references-count":32,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3116"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03116-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,6]]},"assertion":[{"value":"3 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Sports Science Experiment Ethics Committee of Beijing Sport University (No.2023314H).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}