{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T22:31:38Z","timestamp":1789165898090,"version":"build-2803163510"},"reference-count":100,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":16,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-37777-4","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T19:05:32Z","timestamp":1770059132000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals"],"prefix":"10.1038","volume":"16","author":[{"given":"Jalal","family":"Krayem","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lily","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lai Kuan","family":"Tham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiuan-Ni","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"37777_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0140-6736(98)90086-1","volume":"352","author":"CP Warlow","year":"1998","unstructured":"Warlow, C. P. Epidemiology of stroke. The Lancet 352, 1\u20134. https:\/\/doi.org\/10.1016\/S0140-6736(98)90086-1 (1998).","journal-title":"The Lancet"},{"issue":"9","key":"37777_CR2","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1177\/2165079916665400","volume":"64","author":"SA Randolph","year":"2016","unstructured":"Randolph, S. A. Ischemic stroke. Workplace Health Saf. 64(9), 444. https:\/\/doi.org\/10.1177\/2165079916665400 (2016).","journal-title":"Workplace Health Saf."},{"issue":"8","key":"37777_CR3","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1161\/cir.0000000000001123","volume":"147","author":"CW Tsao","year":"2023","unstructured":"Tsao, C. W. et al. Heart disease and stroke statistics-2023 update: A report from the american heart association. Circulation 147(8), 93\u2013621. https:\/\/doi.org\/10.1161\/cir.0000000000001123 (2023).","journal-title":"Circulation"},{"issue":"9","key":"37777_CR4","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.mpmed.2020.06.002","volume":"48","author":"SJ Murphy","year":"2020","unstructured":"Murphy, S. J. & Werring, D. J. Stroke: causes and clinical features. Medicine 48(9), 561\u2013566. https:\/\/doi.org\/10.1016\/j.mpmed.2020.06.002 (2020).","journal-title":"Medicine"},{"key":"37777_CR5","unstructured":"(WHO), W.H.O. Stroke, Cerebrovascular Accident. (accessed: Sep. 9, 2025). https:\/\/www.emro.who.int\/health-topics\/stroke-cerebrovascular-accident\/introduction.html."},{"key":"37777_CR6","doi-asserted-by":"publisher","unstructured":"He, Y., Nie, X., He, T., Qi, X., Chen, Z., Duan, W., Wei, Y., Liu, X., & Liu, Y. Impact of early rehabilitation on outcomes in patients with acute ischemic stroke after endovascular treatment. Front. Neurol. 13, 877773. https:\/\/doi.org\/10.3389\/fneur.2022.877773 (2022).","DOI":"10.3389\/fneur.2022.877773"},{"issue":"1","key":"37777_CR7","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s10916-024-02098-4","volume":"48","author":"K Ouanes","year":"2024","unstructured":"Ouanes, K. & Farhah, N. Effectiveness of artificial intelligence (ai) in clinical decision support systems and care delivery. J. Med. Syst. 48(1), 74. https:\/\/doi.org\/10.1007\/s10916-024-02098-4 (2024).","journal-title":"J. Med. Syst."},{"issue":"1","key":"37777_CR8","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1186\/s12911-021-01488-9","volume":"21","author":"S Secinaro","year":"2021","unstructured":"Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V. & Biancone, P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med. Inform. Decis. Mak. 21(1), 125. https:\/\/doi.org\/10.1186\/s12911-021-01488-9 (2021).","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"3","key":"37777_CR9","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1148\/radiol.2503080257","volume":"250","author":"RI Aviv","year":"2009","unstructured":"Aviv, R. I. et al. Hemorrhagic transformation of ischemic stroke: Prediction with ct perfusion. Radiology 250(3), 867\u2013877. https:\/\/doi.org\/10.1148\/radiol.2503080257 (2009).","journal-title":"Radiology"},{"issue":"1","key":"37777_CR10","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3174\/ajnr.A6883","volume":"42","author":"JE Soun","year":"2021","unstructured":"Soun, J. E. et al. Artificial intelligence and acute stroke imaging. Am. J. Neuroradiol. 42(1), 2. https:\/\/doi.org\/10.3174\/ajnr.A6883 (2021).","journal-title":"Am. J. Neuroradiol."},{"issue":"7","key":"37777_CR11","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1177\/0284185119887593","volume":"61","author":"CH Suh","year":"2019","unstructured":"Suh, C. H. et al. Mri for prediction of hemorrhagic transformation in acute ischemic stroke: a systematic review and meta-analysis. Acta Radiol. 61(7), 964\u2013972. https:\/\/doi.org\/10.1177\/0284185119887593 (2019).","journal-title":"Acta Radiol."},{"key":"37777_CR12","doi-asserted-by":"publisher","unstructured":"Choi, Y.A., Park, S.J., Jun, J.A., Pyo, C.S., Cho, K.H., Lee, H.S., & Yu, J.H. Deep learning-based stroke disease prediction system using real-time bio signals. Sensors (Basel) 21(13). https:\/\/doi.org\/10.3390\/s21134269 (2021).","DOI":"10.3390\/s21134269"},{"key":"37777_CR13","doi-asserted-by":"publisher","unstructured":"Altan, G., Kutlu, Y., & Allahverdi, N. Deep belief networks based brain activity classification using eeg from slow cortical potentials in stroke. Int. J. Appl. Math. Electron. Comput. (Special Issue-1), 205\u2013210. https:\/\/doi.org\/10.18100\/ijamec.270307 (2016).","DOI":"10.18100\/ijamec.270307"},{"key":"37777_CR14","doi-asserted-by":"publisher","unstructured":"Kamaruddin, N. H., Murugappan, M., & Mohammad Iqbal, O. Early prediction of cardiovascular diseases using ecg signal: Review. In 2012 IEEE Student Conference on Research and Development (SCOReD), pp. 48\u201353. https:\/\/doi.org\/10.1109\/SCOReD.2012.6518609.","DOI":"10.1109\/SCOReD.2012.6518609"},{"issue":"19","key":"37777_CR15","doi-asserted-by":"publisher","first-page":"6791","DOI":"10.3390\/app10196791","volume":"10","author":"J Yu","year":"2020","unstructured":"Yu, J. et al. Ai-based stroke disease prediction system using real-time electromyography signals. Appl. Sci. 10(19), 6791. https:\/\/doi.org\/10.3390\/app10196791 (2020).","journal-title":"Appl. Sci."},{"key":"37777_CR16","doi-asserted-by":"publisher","first-page":"43623","DOI":"10.1109\/ACCESS.2022.3169284","volume":"10","author":"J Yu","year":"2022","unstructured":"Yu, J., Park, S., Kwon, S. H., Cho, K. H. & Lee, H. Ai-based stroke disease prediction system using ecg and ppg bio-signals. IEEE Access 10, 43623\u201343638. https:\/\/doi.org\/10.1109\/ACCESS.2022.3169284 (2022).","journal-title":"IEEE Access"},{"key":"37777_CR17","doi-asserted-by":"publisher","unstructured":"Das, S., Tariq, A., Santos, T., Kantareddy, S.S., & Banerjee, I. In Colliot, O. (ed.) Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research, pp. 117\u2013 138. Springer, New York, NY ( 2023). https:\/\/doi.org\/10.1007\/978-1-0716-3195-9_4.","DOI":"10.1007\/978-1-0716-3195-9_4"},{"key":"37777_CR18","doi-asserted-by":"publisher","first-page":"301","DOI":"10.32604\/jai.2024.054314","volume":"6","author":"F Mortezapour Shiri","year":"2024","unstructured":"Mortezapour Shiri, F., Perumal, T. & Mohamed, R. A comprehensive overview and comparative analysis on deep learning model. J. Artif. Intell. 6, 301\u2013360. https:\/\/doi.org\/10.32604\/jai.2024.054314 (2024).","journal-title":"J. Artif. Intell."},{"key":"37777_CR19","doi-asserted-by":"publisher","first-page":"114126","DOI":"10.1016\/j.dss.2023.114126","volume":"178","author":"S Bouazizi","year":"2024","unstructured":"Bouazizi, S. & Ltifi, H. Enhancing accuracy and interpretability in eeg-based medical decision making using an explainable ensemble learning framework application for stroke prediction. Decis. Support Syst. 178, 114126. https:\/\/doi.org\/10.1016\/j.dss.2023.114126 (2024).","journal-title":"Decis. Support Syst."},{"key":"37777_CR20","doi-asserted-by":"publisher","first-page":"9859","DOI":"10.3390\/s22249859","volume":"22","author":"S Islam","year":"2022","unstructured":"Islam, S., Hussain, I., Rahman, M., Park, S. J. & Hossain, M. Explainable artificial intelligence model for stroke prediction using eeg signal. Sensors 22, 9859. https:\/\/doi.org\/10.3390\/s22249859 (2022).","journal-title":"Sensors"},{"key":"37777_CR21","doi-asserted-by":"publisher","first-page":"1392","DOI":"10.3390\/s24051392","volume":"24","author":"I Hussain","year":"2024","unstructured":"Hussain, I. & Jany, R. Interpreting stroke-impaired electromyography patterns through explainable artificial intelligence. Sensors 24, 1392. https:\/\/doi.org\/10.3390\/s24051392 (2024).","journal-title":"Sensors"},{"key":"37777_CR22","unstructured":"FEDESORIANO: Stroke Prediction Dataset (2021). https:\/\/www.kaggle.com\/datasets\/fedesoriano\/stroke-prediction-dataset."},{"key":"37777_CR23","doi-asserted-by":"publisher","unstructured":"Bhanusri, K., Reddy, K.N., Gnaneswarachary, P., Nair, P.C., & Sampath, N. Heart stroke prediction using machine learning and explainable ai insights. In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), pp. 1\u20137. https:\/\/doi.org\/10.1109\/ICITEICS61368.2024.10625543.","DOI":"10.1109\/ICITEICS61368.2024.10625543"},{"issue":"13","key":"37777_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s22134670","volume":"22","author":"E Dritsas","year":"2022","unstructured":"Dritsas, E. & Trigka, M. Stroke risk prediction with machine learning techniques. Sensors (Basel) 22(13), 1. https:\/\/doi.org\/10.3390\/s22134670 (2022).","journal-title":"Sensors (Basel)"},{"key":"37777_CR25","doi-asserted-by":"publisher","unstructured":"Explainable and interpretable model for the early detection of brain stroke using optimized boosting algorithms. Diagnostics 14(22). https:\/\/doi.org\/10.3390\/diagnostics14222514 (2024).","DOI":"10.3390\/diagnostics14222514"},{"key":"37777_CR26","doi-asserted-by":"publisher","unstructured":"Islam, R., Debnath, S., & Palash, T.I. Predictive analysis for risk of stroke using machine learning techniques. In 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), pp. 1\u20134. https:\/\/doi.org\/10.1109\/IC4ME253898.2021.9768524.","DOI":"10.1109\/IC4ME253898.2021.9768524"},{"key":"37777_CR27","doi-asserted-by":"publisher","first-page":"52288","DOI":"10.1109\/ACCESS.2023.3278273","volume":"11","author":"K Mridha","year":"2023","unstructured":"Mridha, K. et al. Automated stroke prediction using machine learning: An explainable and exploratory study with a web application for early intervention. IEEE Access 11, 52288\u201352308. https:\/\/doi.org\/10.1109\/ACCESS.2023.3278273 (2023).","journal-title":"IEEE Access"},{"key":"37777_CR28","doi-asserted-by":"publisher","unstructured":"Chadaga, K., Sampathila, N., Prabhu, S., & Chadaga, R. Multiple explainable approaches to predict the risk of stroke using artificial intelligence. Information 14(8). https:\/\/doi.org\/10.3390\/info14080435 (2023).","DOI":"10.3390\/info14080435"},{"key":"37777_CR29","doi-asserted-by":"publisher","unstructured":"Ugbomeh, O., Yiye, V., Ibeke, E., Ezenkwu, C.P., Sharma, V., & Alkhayyat, A. Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (xai). In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), vol. 1, pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICEECT61758.2024.10739320.","DOI":"10.1109\/ICEECT61758.2024.10739320"},{"issue":"13","key":"37777_CR30","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1042\/cs20160043","volume":"130","author":"V Austin","year":"2016","unstructured":"Austin, V., Crack, P., Bozinovski, S., Miller, A. & Vlahos, R. Copd and stroke: are systemic inflammation and oxidative stress the missing links?. Clin. Sci. 130(13), 1039\u20131050. https:\/\/doi.org\/10.1042\/cs20160043 (2016).","journal-title":"Clin. Sci."},{"issue":"8","key":"37777_CR31","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1080\/02699200701427401","volume":"21","author":"SN Awan","year":"2007","unstructured":"Awan, S. N. & Alphonso, V. A. Effects of smoking on respiratory capacity and control. Clin. Ling. Phonet. 21(8), 623\u2013636. https:\/\/doi.org\/10.1080\/02699200701427401 (2007).","journal-title":"Clin. Ling. Phonet."},{"key":"37777_CR32","doi-asserted-by":"publisher","unstructured":"Barnett, H.M., Davis, A.P., & Khot, S.P. Chapter 10 - stroke and breathing. In Chen, R., Guyenet, P.G. (eds.) Respiratory Neurobiology: Physiology and Clinical Disorders, Part II. Handbook of Clinical Neurology, vol. 189, pp. 201\u2013 222. Elsevier, Amsterdam, Netherlands ( 2022). https:\/\/doi.org\/10.1016\/B978-0-323-91532-8.00016-1.","DOI":"10.1016\/B978-0-323-91532-8.00016-1"},{"issue":"21","key":"37777_CR33","doi-asserted-by":"publisher","first-page":"2655","DOI":"10.1161\/01.CIR.0000128587.30041.C8","volume":"109","author":"RD Brook","year":"2004","unstructured":"Brook, R. D. et al. Air pollution and cardiovascular disease. Circulation 109(21), 2655\u20132671. https:\/\/doi.org\/10.1161\/01.CIR.0000128587.30041.C8 (2004).","journal-title":"Circulation"},{"issue":"10","key":"37777_CR34","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.1161\/STROKEAHA.120.030429","volume":"51","author":"MSV Elkind","year":"2020","unstructured":"Elkind, M. S. V., Boehme, A. K., Smith, C. J., Meisel, A. & Buckwalter, M. S. Infection as a stroke risk factor and determinant of outcome after stroke. Stroke 51(10), 3156\u20133168. https:\/\/doi.org\/10.1161\/STROKEAHA.120.030429 (2020).","journal-title":"Stroke"},{"issue":"4","key":"37777_CR35","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1177\/1747493019832999","volume":"14","author":"CJ Estol","year":"2019","unstructured":"Estol, C. J. Is breathing our polluted air a risk factor for stroke?. Int. J. Stroke 14(4), 340\u2013350. https:\/\/doi.org\/10.1177\/1747493019832999 (2019).","journal-title":"Int. J. Stroke"},{"issue":"1","key":"37777_CR36","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1186\/s12883-015-0414-1","volume":"15","author":"KG Haeusler","year":"2015","unstructured":"Haeusler, K. G. et al. Impact of chronic inflammatory airway disease on stroke severity and long-term survival after ischemic stroke - a retrospective analysis. BMC Neurol. 15(1), 164. https:\/\/doi.org\/10.1186\/s12883-015-0414-1 (2015).","journal-title":"BMC Neurol."},{"issue":"8","key":"37777_CR37","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1161\/01.STR.31.8.1893","volume":"31","author":"CL Hart","year":"2000","unstructured":"Hart, C. L., Hole, D. J. & Smith, G. D. Comparison of risk factors for stroke incidence and stroke mortality in 20 years of follow-up in men and women in the renfrew\/paisley study in scotland. Stroke 31(8), 1893\u20131896. https:\/\/doi.org\/10.1161\/01.STR.31.8.1893 (2000).","journal-title":"Stroke"},{"key":"37777_CR38","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.2147\/COPD.S205050","volume":"14","author":"FI Hawari","year":"2019","unstructured":"Hawari, F. I. et al. Respiratory health and quality of life in young exclusive, habitual smokers - a comparison of waterpipe smokers, cigarette smokers and non-smokers. Int. J. Chronic Obstruct. Pulm. Dis. 14, 1813\u20131824. https:\/\/doi.org\/10.2147\/COPD.S205050 (2019).","journal-title":"Int. J. Chronic Obstruct. Pulm. Dis."},{"issue":"4","key":"37777_CR39","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1016\/j.ncl.2005.08.001","volume":"23","author":"C Iber","year":"2005","unstructured":"Iber, C. Sleep-related breathing disorders. Neurol. Clin. 23(4), 1045\u20131057. https:\/\/doi.org\/10.1016\/j.ncl.2005.08.001 (2005).","journal-title":"Neurol. Clin."},{"key":"37777_CR40","doi-asserted-by":"publisher","first-page":"177","DOI":"10.6061\/clinics\/2018\/e177","volume":"73","author":"YR Kim","year":"2018","unstructured":"Kim, Y. R. et al. Stroke risk among patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis. Clinics 73, 177. https:\/\/doi.org\/10.6061\/clinics\/2018\/e177 (2018).","journal-title":"Clinics"},{"issue":"6","key":"37777_CR41","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1161\/01.STR.32.6.1271","volume":"32","author":"V Mohsenin","year":"2001","unstructured":"Mohsenin, V. Sleep-related breathing disorders and risk of stroke. Stroke 32(6), 1271\u20131278. https:\/\/doi.org\/10.1161\/01.STR.32.6.1271 (2001).","journal-title":"Stroke"},{"issue":"1","key":"37777_CR42","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1056\/NEJM199901073400103","volume":"340","author":"DH O\u2019Leary","year":"1999","unstructured":"O\u2019Leary, D. H. et al. Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults. N. Engl. J. Med. 340(1), 14\u201322. https:\/\/doi.org\/10.1056\/NEJM199901073400103 (1999).","journal-title":"N. Engl. J. Med."},{"issue":"4","key":"37777_CR43","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.jaci.2003.08.031","volume":"112","author":"A Qureshi","year":"2003","unstructured":"Qureshi, A., Ballard, R. D. & Nelson, H. S. Obstructive sleep apnea. J. Allergy Clin. Immunol. 112(4), 643\u2013651. https:\/\/doi.org\/10.1016\/j.jaci.2003.08.031 (2003).","journal-title":"J. Allergy Clin. Immunol."},{"issue":"6676","key":"37777_CR44","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1136\/bmj.298.6676.789","volume":"298","author":"R Shinton","year":"1989","unstructured":"Shinton, R. & Beevers, G. Meta-analysis of relation between cigarette smoking and stroke. BMJ 298(6676), 789. https:\/\/doi.org\/10.1136\/bmj.298.6676.789 (1989).","journal-title":"BMJ"},{"issue":"6","key":"37777_CR45","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.1378\/chest.127.6.1952","volume":"127","author":"DD Sin","year":"2005","unstructured":"Sin, D. D., Wu, L. & Man, S. F. P. The relationship between reduced lung function and cardiovascular mortality: A population-based study and a systematic review of the literature. Chest 127(6), 1952\u20131959. https:\/\/doi.org\/10.1378\/chest.127.6.1952 (2005).","journal-title":"Chest"},{"issue":"1","key":"37777_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/brainsci12010106","volume":"12","author":"A Szyli\u0144ska","year":"2022","unstructured":"Szyli\u0144ska, A., Kotfis, K., Bott-Olejnik, M., Wa\u0144kowicz, P. & Rotter, I. Post-stroke outcomes of patients with chronic obstructive pulmonary disease. Brain Sci. 12(1), 1. https:\/\/doi.org\/10.3390\/brainsci12010106 (2022).","journal-title":"Brain Sci."},{"issue":"6","key":"37777_CR47","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.1378\/chest.130.6.1631","volume":"130","author":"E Tamagawa","year":"2006","unstructured":"Tamagawa, E. & Eeden, S. F. Impaired lung function and risk for stroke: Role of the systemic inflammation response?. Chest 130(6), 1631\u20131633. https:\/\/doi.org\/10.1378\/chest.130.6.1631 (2006).","journal-title":"Chest"},{"issue":"11","key":"37777_CR48","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1161\/01.STR.26.11.2004","volume":"26","author":"SG Wannamethee","year":"1995","unstructured":"Wannamethee, S. G., Shaper, A. G. & Ebrahim, S. Respiratory function and risk of stroke. Stroke 26(11), 2004\u20132010. https:\/\/doi.org\/10.1161\/01.STR.26.11.2004 (1995).","journal-title":"Stroke"},{"issue":"8","key":"37777_CR49","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1161\/01.STR.22.8.983","volume":"22","author":"PA Wolf","year":"1991","unstructured":"Wolf, P. A., Abbott, R. D. & Kannel, W. B. Atrial fibrillation as an independent risk factor for stroke: the framingham study. Stroke 22(8), 983\u2013988. https:\/\/doi.org\/10.1161\/01.STR.22.8.983 (1991).","journal-title":"Stroke"},{"issue":"7","key":"37777_CR50","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1001\/jama.1988.03720070025028","volume":"259","author":"PA Wolf","year":"1988","unstructured":"Wolf, P. A., D\u2019Agostino, R. B., Kannel, W. B., Bonita, R. & Belanger, A. J. Cigarette smoking as a risk factor for stroke: The framingham study. JAMA 259(7), 1025\u20131029. https:\/\/doi.org\/10.1001\/jama.1988.03720070025028 (1988).","journal-title":"JAMA"},{"issue":"9","key":"37777_CR51","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1145\/3561048","volume":"55","author":"R Dwivedi","year":"2023","unstructured":"Dwivedi, R. et al. Explainable ai (xai): Core ideas, techniques, and solutions. ACM Comput. Surv. 55(9), 194. https:\/\/doi.org\/10.1145\/3561048 (2023).","journal-title":"ACM Comput. Surv."},{"key":"37777_CR52","doi-asserted-by":"publisher","unstructured":"Das, A., & Rad, P. Opportunities and challenges in explainable artificial intelligence (xai): A survey (2020) https:\/\/doi.org\/10.48550\/arXiv.2006.11371.","DOI":"10.48550\/arXiv.2006.11371"},{"issue":"12","key":"37777_CR53","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1007\/s10462-025-11399-0","volume":"58","author":"N Mehdiyev","year":"2025","unstructured":"Mehdiyev, N., Majlatow, M. & Fettke, P. Interpretable and explainable machine learning methods for predictive process monitoring: a systematic literature review. Artif. Intell. Rev. 58(12), 378. https:\/\/doi.org\/10.1007\/s10462-025-11399-0 (2025).","journal-title":"Artif. Intell. Rev."},{"key":"37777_CR54","doi-asserted-by":"publisher","unstructured":"Lundberg, S., & Lee, S.-I. A Unified Approach to Interpreting Model Predictions (2017). https:\/\/doi.org\/10.48550\/arXiv.1705.07874.","DOI":"10.48550\/arXiv.1705.07874"},{"key":"37777_CR55","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778","author":"MT Ribeiro","year":"2016","unstructured":"Ribeiro, M. T., Singh, S. & Guestrin, C. Why Should I Trust You? Explaining the Predictions of Any Classifier. Assoc. Comput. Mach. https:\/\/doi.org\/10.1145\/2939672.2939778 (2016).","journal-title":"Assoc. Comput. Mach."},{"key":"37777_CR56","doi-asserted-by":"publisher","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. Anchors: High-precision model-agnostic explanations. In Proceedings of the AAAI Conference on Artificial Intelligence 32(1) (2018) https:\/\/doi.org\/10.1609\/aaai.v32i1.11491.","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"37777_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12599-024-00922-2","volume":"1","author":"S Kruschel","year":"2025","unstructured":"Kruschel, S. et al. Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models. Bus. Inf. Syst. Eng. 1, 1. https:\/\/doi.org\/10.1007\/s12599-024-00922-2 (2025).","journal-title":"Bus. Inf. Syst. Eng."},{"key":"37777_CR58","doi-asserted-by":"publisher","unstructured":"Kadir, M.A., Mosavi, A., & Sonntag, D. Evaluation metrics for xai: A review, taxonomy, and practical applications. In 2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES), pp. 000111\u2013000124. https:\/\/doi.org\/10.1109\/INES59282.2023.10297629.","DOI":"10.1109\/INES59282.2023.10297629"},{"key":"37777_CR59","doi-asserted-by":"publisher","first-page":"58","DOI":"10.21608\/ijci.2023.236156.1132","volume":"10","author":"A El-gezawy","year":"2023","unstructured":"El-gezawy, A., Abd elkader, H. & Ali, A. A new xai evaluation metric for classification. IJCI. Int. J. Comput. Inf. 10, 58\u201362. https:\/\/doi.org\/10.21608\/ijci.2023.236156.1132 (2023).","journal-title":"IJCI. Int. J. Comput. Inf."},{"issue":"23","key":"37777_CR60","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1161\/01.cir.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A. L. et al. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215\u201320. https:\/\/doi.org\/10.1161\/01.cir.101.23.e215 (2000).","journal-title":"Circulation"},{"issue":"1","key":"37777_CR61","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1161\/strokeaha.109.565556","volume":"41","author":"V Novak","year":"2010","unstructured":"Novak, V. et al. Cerebral flow velocities during daily activities depend on blood pressure in patients with chronic ischemic infarctions. Stroke 41(1), 61\u20136. https:\/\/doi.org\/10.1161\/strokeaha.109.565556 (2010).","journal-title":"Stroke"},{"key":"37777_CR62","doi-asserted-by":"publisher","unstructured":"Rizvi, S.M., Siyam, M.A.U., Jibon, F.A., Shorna, S.A., & Akter, R. Voice-based parkinson disease detection using svm & xgboost. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), pp. 1\u2013 6. https:\/\/doi.org\/10.1109\/QPAIN66474.2025.11172195 (2025).","DOI":"10.1109\/QPAIN66474.2025.11172195"},{"issue":"3","key":"37777_CR63","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1109\/jbhi.2015.2458965","volume":"20","author":"AR Fekr","year":"2016","unstructured":"Fekr, A. R., Janidarmian, M., Radecka, K. & Zilic, Z. Respiration disorders classification with informative features for m-health applications. IEEE J. Biomed. Health Inform. 20(3), 733\u2013747. https:\/\/doi.org\/10.1109\/jbhi.2015.2458965 (2016).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"37777_CR64","doi-asserted-by":"publisher","first-page":"106068","DOI":"10.1016\/j.compbiomed.2022.106068","volume":"149","author":"B Nicholls","year":"2022","unstructured":"Nicholls, B. et al. An emg-based eating behaviour monitoring system with haptic feedback to promote mindful eating. Comput. Biol. Med. 149, 106068. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106068 (2022).","journal-title":"Comput. Biol. Med."},{"issue":"189","key":"37777_CR65","doi-asserted-by":"publisher","first-page":"20220012","DOI":"10.1098\/rsif.2022.0012","volume":"19","author":"S Rahman","year":"2022","unstructured":"Rahman, S., Karmakar, C., Natgunanathan, I., Yearwood, J. & Palaniswami, M. Robustness of electrocardiogram signal quality indices. J. R. Soc. Interface 19(189), 20220012. https:\/\/doi.org\/10.1098\/rsif.2022.0012 (2022) https:\/\/royalsocietypublishing.org\/doi\/pdf\/10.1098\/rsif.2022.0012.","journal-title":"J. R. Soc. Interface"},{"issue":"3","key":"37777_CR66","doi-asserted-by":"publisher","first-page":"3383","DOI":"10.1109\/TITS.2022.3225782","volume":"24","author":"J Xu","year":"2023","unstructured":"Xu, J., Pan, S., Sun, P. Z. H. & Hyeong Park, S. Human-factors-in-driving-loop: Driver identification and verification via a deep learning approach using psychological behavioral data. IEEE Trans. Intell. Transp. Syst. 24(3), 3383\u20133394. https:\/\/doi.org\/10.1109\/TITS.2022.3225782 (2023).","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"37777_CR67","doi-asserted-by":"publisher","unstructured":"Ji, X., Li, Y., & Wen, P. 3dsleepnet: A multi-channel bio-signal based sleep stages classification method using deep learning. IEEE Trans. Neural Syst. Rehab. Eng.. https:\/\/doi.org\/10.1109\/TNSRE.2023.3309542 (2023).","DOI":"10.1109\/TNSRE.2023.3309542"},{"issue":"2","key":"37777_CR68","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TCE.2018.2844736","volume":"64","author":"D Ayata","year":"2018","unstructured":"Ayata, D., Yaslan, Y. & Kamasak, M. E. Emotion based music recommendation system using wearable physiological sensors. IEEE Trans. Consum. Electron. 64(2), 196\u2013203. https:\/\/doi.org\/10.1109\/TCE.2018.2844736 (2018).","journal-title":"IEEE Trans. Consum. Electron."},{"key":"37777_CR69","doi-asserted-by":"publisher","unstructured":"Qaisar, S.M., Nisar, H., & Subasi, A. Advances in non-invasive biomedical signal sensing and processing with machine learning. Springer, New York, NY ( 2023). https:\/\/doi.org\/10.1007\/978-3-031-23239-8.","DOI":"10.1007\/978-3-031-23239-8"},{"issue":"4","key":"37777_CR70","doi-asserted-by":"publisher","first-page":"4058","DOI":"10.1016\/j.eswa.2010.09.068","volume":"38","author":"J Rafiee","year":"2011","unstructured":"Rafiee, J., Rafiee, M. A., Yavari, F. & Schoen, M. P. Feature extraction of forearm emg signals for prosthetics. Expert Syst. Appl. 38(4), 4058\u20134067. https:\/\/doi.org\/10.1016\/j.eswa.2010.09.068 (2011).","journal-title":"Expert Syst. Appl."},{"key":"37777_CR71","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.patrec.2015.11.018","volume":"70","author":"R Ghorbani Afkhami","year":"2016","unstructured":"Ghorbani Afkhami, R., Azarnia, G. & Tinati, M. A. Cardiac arrhythmia classification using statistical and mixture modeling features of ecg signals. Pattern Recogn. Lett. 70, 45\u201351. https:\/\/doi.org\/10.1016\/j.patrec.2015.11.018 (2016).","journal-title":"Pattern Recogn. Lett."},{"issue":"12","key":"37777_CR72","doi-asserted-by":"publisher","first-page":"3787","DOI":"10.1016\/j.sigpro.2006.03.020.","volume":"86","author":"IT Rekanos","year":"2006","unstructured":"Rekanos, I. T. & Hadjileontiadis, L. J. An iterative kurtosis-based technique for the detection of nonstationary bioacoustic signals. Signal Process. 86(12), 3787\u20133795. https:\/\/doi.org\/10.1016\/j.sigpro.2006.03.020. (2006) (Special Section: Multimodal Human-Computer Interfaces).","journal-title":"Signal Process."},{"issue":"7","key":"37777_CR73","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.medengphy.2010.04.009","volume":"32","author":"KC Chua","year":"2010","unstructured":"Chua, K. C., Chandran, V., Acharya, U. R. & Lim, C. M. Application of higher order statistics\/spectra in biomedical signals-a review. Med. Eng. Phys. 32(7), 679\u2013689. https:\/\/doi.org\/10.1016\/j.medengphy.2010.04.009 (2010).","journal-title":"Med. Eng. Phys."},{"issue":"8","key":"37777_CR74","doi-asserted-by":"publisher","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","volume":"39","author":"A Phinyomark","year":"2012","unstructured":"Phinyomark, A., Phukpattaranont, P. & Limsakul, C. Feature reduction and selection for emg signal classification. Expert Syst. Appl. 39(8), 7420\u20137431. https:\/\/doi.org\/10.1016\/j.eswa.2012.01.102 (2012).","journal-title":"Expert Syst. Appl."},{"key":"37777_CR75","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.inffus.2017.05.005","volume":"40","author":"E Kanjo","year":"2018","unstructured":"Kanjo, E., Younis, E. M. G. & Sherkat, N. Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach. Inf. Fusion 40, 18\u201331. https:\/\/doi.org\/10.1016\/j.inffus.2017.05.005 (2018).","journal-title":"Inf. Fusion"},{"issue":"3","key":"37777_CR76","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1002\/cphy.c140016","volume":"5","author":"AM Dorrance","year":"2015","unstructured":"Dorrance, A. M. & Fink, G. Effects of stroke on the autonomic nervous system. Compr. Physiol. 5(3), 1241\u201363. https:\/\/doi.org\/10.1002\/cphy.c140016 (2015).","journal-title":"Compr. Physiol."},{"issue":"7","key":"37777_CR77","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/s00769-009-0526-7","volume":"14","author":"R Willink","year":"2009","unstructured":"Willink, R. The role of the sample standard deviation in the analysis of measurement data. Accred. Qual. Assur. 14(7), 353\u2013358. https:\/\/doi.org\/10.1007\/s00769-009-0526-7 (2009).","journal-title":"Accred. Qual. Assur."},{"issue":"1","key":"37777_CR78","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1111\/1467-9884.00122","volume":"47","author":"DN Joanes","year":"1998","unstructured":"Joanes, D. N. & Gill, C. A. Comparing measures of sample skewness and kurtosis. J. R. Stat. Soc. Ser. D (The Statistician) 47(1), 183\u2013189. https:\/\/doi.org\/10.1111\/1467-9884.00122 (1998).","journal-title":"J. R. Stat. Soc. Ser. D (The Statistician)"},{"key":"37777_CR79","doi-asserted-by":"publisher","unstructured":"Li, Q., Selvaperumal, S., Ullah, Y., & Roslee, M. A study of spectrum sensing techniques based on gnuradio and energy detection. In 2024 Multimedia University Engineering Conference (MECON), pp. 1\u2013 4. https:\/\/doi.org\/10.1109\/MECON62796.2024.10776459 (2024).","DOI":"10.1109\/MECON62796.2024.10776459"},{"key":"37777_CR80","doi-asserted-by":"publisher","unstructured":"Misra, H., Ikbal, S., Bourlard, H., & Hermansky, H. Spectral entropy based feature for robust asr. In 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, p. 193. https:\/\/doi.org\/10.1109\/ICASSP.2004.1325955.","DOI":"10.1109\/ICASSP.2004.1325955"},{"key":"37777_CR81","doi-asserted-by":"publisher","first-page":"765","DOI":"10.3389\/fphys.2017.00765","volume":"8","author":"J Pavei","year":"2017","unstructured":"Pavei, J. et al. Early seizure detection based on cardiac autonomic regulation dynamics. Front. Physiol. 8, 765. https:\/\/doi.org\/10.3389\/fphys.2017.00765 (2017).","journal-title":"Front. Physiol."},{"issue":"1","key":"37777_CR82","doi-asserted-by":"publisher","first-page":"2400304","DOI":"10.1002\/aisy.202400304","volume":"7","author":"AM Salih","year":"2025","unstructured":"Salih, A. M. et al. A perspective on explainable artificial intelligence methods: Shap and lime. Adv. Intell. Syst. 7(1), 2400304. https:\/\/doi.org\/10.1002\/aisy.202400304 (2025).","journal-title":"Adv. Intell. Syst."},{"key":"37777_CR83","doi-asserted-by":"publisher","unstructured":"Castillo-Escario, Y., Werthen-Brabants, L., Groenendaal, W., Deschrijver, D., & Jane, R. Convolutional neural networks for apnea detection from smartphone audio signals: Effect of window size. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 666\u2013 669 ( 2022). https:\/\/doi.org\/10.1109\/EMBC48229.2022.9871396.","DOI":"10.1109\/EMBC48229.2022.9871396"},{"issue":"16","key":"37777_CR84","doi-asserted-by":"publisher","first-page":"31754","DOI":"10.1109\/JSEN.2025.3581211","volume":"25","author":"S Lee","year":"2025","unstructured":"Lee, S., Lee, S., Yun, S., Ryu, S. & Jeong, I. C. Toward real-world ecg-based human activity recognition: Optimal window size and subject-independent 1-d cnn approach. IEEE Sens. J. 25(16), 31754\u201331768. https:\/\/doi.org\/10.1109\/JSEN.2025.3581211 (2025).","journal-title":"IEEE Sens. J."},{"key":"37777_CR85","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.patrec.2015.11.018","volume":"70","author":"R Ghorbani Afkhami","year":"2016","unstructured":"Ghorbani Afkhami, R., Azarnia, G. & Tinati, M. A. Cardiac arrhythmia classification using statistical and mixture modeling features of ecg signals. Pattern Recogn. Lett. 70, 45\u201351. https:\/\/doi.org\/10.1016\/j.patrec.2015.11.018 (2016).","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"37777_CR86","doi-asserted-by":"publisher","first-page":"6751932","DOI":"10.1155\/2019\/6751932","volume":"2019","author":"TN Alotaiby","year":"2019","unstructured":"Alotaiby, T. N., Alrshoud, S. R., Alshebeili, S. A. & Aljafar, L. M. Ecg-based subject identification using statistical features and random forest. J. Sensors 2019(1), 6751932. https:\/\/doi.org\/10.1155\/2019\/6751932 (2019) https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2019\/6751932.","journal-title":"J. Sensors"},{"key":"37777_CR87","first-page":"7594","volume":"28","author":"Y Kaya","year":"2017","unstructured":"Kaya, Y., Pehlivan, H. & Tenekeci, M. Effective ecg beat classification using higher order statistic features and genetic feature selection. Biomed. Res. India 28, 7594\u20137603 (2017).","journal-title":"Biomed. Res. India"},{"issue":"7","key":"37777_CR88","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1177\/0271678x19867154","volume":"40","author":"D Jiang","year":"2020","unstructured":"Jiang, D. et al. Normal variations in brain oxygen extraction fraction are partly attributed to differences in end-tidal co2. J. Cereb. Blood Flow Metab. 40(7), 1492\u20131500. https:\/\/doi.org\/10.1177\/0271678x19867154 (2020).","journal-title":"J. Cereb. Blood Flow Metab."},{"key":"37777_CR89","doi-asserted-by":"publisher","unstructured":"Kamaruddin, N.H., Murugappan, M., & Omar, M.I. Early prediction of cardiovascular diseases using ecg signal: Review. In 2012 IEEE Student Conference on Research and Development (SCOReD), pp. 48\u2013 53 ( 2012). https:\/\/doi.org\/10.1109\/SCOReD.2012.6518609.","DOI":"10.1109\/SCOReD.2012.6518609"},{"issue":"1","key":"37777_CR90","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1002\/widm.2","volume":"1","author":"PJ Rousseeuw","year":"2011","unstructured":"Rousseeuw, P. J. & Hubert, M. Robust statistics for outlier detection. WIREs Data Min. Knowl. Discov. 1(1), 73\u201379. https:\/\/doi.org\/10.1002\/widm.2 (2011) https:\/\/wires.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/widm.2.","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"37777_CR91","doi-asserted-by":"publisher","unstructured":"Mian Qaisar, S., Nisar, H. & Subasi, A. Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning. https:\/\/doi.org\/10.1007\/978-3-031-23239-8 (2023).","DOI":"10.1007\/978-3-031-23239-8"},{"issue":"4","key":"37777_CR92","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1097\/phm.0000000000000003","volume":"93","author":"C-H Lin","year":"2014","unstructured":"Lin, C.-H. et al. Muscle pain intensity and pressure pain threshold changes in different periods of stroke patients. Am. J. Phys. Med. Rehab. 93(4), 299\u2013309. https:\/\/doi.org\/10.1097\/phm.0000000000000003 (2014).","journal-title":"Am. J. Phys. Med. Rehab."},{"issue":"4","key":"37777_CR93","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1097\/aln.0000000000004354","volume":"137","author":"PE Vlisides","year":"2022","unstructured":"Vlisides, P. E. et al. Carbon dioxide, blood pressure, and perioperative stroke: A retrospective case-control study. Anesthesiology 137(4), 434\u2013445. https:\/\/doi.org\/10.1097\/aln.0000000000004354 (2022).","journal-title":"Anesthesiology"},{"issue":"4","key":"37777_CR94","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.dsx.2018.03.009","volume":"12","author":"A Alloubani","year":"2018","unstructured":"Alloubani, A., Saleh, A. & Abdelhafiz, I. Hypertension and diabetes mellitus as a predictive risk factors for stroke. Diabetes Metab. Syndr. 12(4), 577\u2013584. https:\/\/doi.org\/10.1016\/j.dsx.2018.03.009 (2018).","journal-title":"Diabetes Metab. Syndr."},{"issue":"8","key":"37777_CR95","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1161\/01.STR.25.8.1605","volume":"25","author":"M Alter","year":"1994","unstructured":"Alter, M., Friday, G., Lai, S. M., O\u2019Connell, J. & Sobel, E. Hypertension and risk of stroke recurrence. Stroke 25(8), 1605\u20131610. https:\/\/doi.org\/10.1161\/01.STR.25.8.1605 (1994).","journal-title":"Stroke"},{"key":"37777_CR96","doi-asserted-by":"publisher","unstructured":"Swamy, V., Frej, J., & K\u00e4ser, T. The Future of Human-centric eXplainable Artificial Intelligence (XAI) Is Not Post-hoc Explanations. https:\/\/doi.org\/10.48550\/arXiv.2307.00364 (2023).","DOI":"10.48550\/arXiv.2307.00364"},{"issue":"1","key":"37777_CR97","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s10916-023-02020-4","volume":"48","author":"M Chen","year":"2024","unstructured":"Chen, M. et al. Systematic review of machine learning applied to the secondary prevention of ischemic stroke. J. Med. Syst. 48(1), 8. https:\/\/doi.org\/10.1007\/s10916-023-02020-4 (2024).","journal-title":"J. Med. Syst."},{"key":"37777_CR98","doi-asserted-by":"publisher","unstructured":"Gharehbaghi, A. Deep Learning in Time Series Analysis, 1st edn., p. 208. CRC Press, Boca Raton (2023). https:\/\/doi.org\/10.1201\/9780429321252.","DOI":"10.1201\/9780429321252"},{"key":"37777_CR99","doi-asserted-by":"publisher","first-page":"101091","DOI":"10.1016\/j.jestch.2021.101091","volume":"34","author":"G Altan","year":"2022","unstructured":"Altan, G. Deepoct: An explainable deep learning architecture to analyze macular edema on oct images. Eng. Sci. Technol. Int. J. 34, 101091. https:\/\/doi.org\/10.1016\/j.jestch.2021.101091 (2022).","journal-title":"Eng. Sci. Technol. Int. J."},{"issue":"5","key":"37777_CR100","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/JBHI.2019.2931395","volume":"24","author":"G Altan","year":"2020","unstructured":"Altan, G., Kutlu, Y. & Allahverdi, N. Deep learning on computerized analysis of chronic obstructive pulmonary disease. IEEE J. Biomed. Health Inform. 24(5), 1344\u20131350. https:\/\/doi.org\/10.1109\/JBHI.2019.2931395 (2020).","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37777-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37777-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37777-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T11:49:46Z","timestamp":1771415386000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37777-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,2]]},"references-count":100,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["37777"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-37777-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,2]]},"assertion":[{"value":"8 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6906"}}