{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:59:17Z","timestamp":1787673557419,"version":"build-2736575974"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Ningxia National Science Foundation of China","award":["2024AAC03153"],"award-info":[{"award-number":["2024AAC03153"]}]},{"name":"the Youth Nurturing Program of North Minzu University","award":["2023QNPY27"],"award-info":[{"award-number":["2023QNPY27"]}]},{"name":"the Graduate Student Innovation Project of NUM","award":["CYX25198"],"award-info":[{"award-number":["CYX25198"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-45862-x","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:26:49Z","timestamp":1774646809000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An improved ICEEMDAN\u2013depth hybrid network model integrating multimodal data for the screening of diabetic peripheral neuropathy"],"prefix":"10.1038","volume":"16","author":[{"given":"Mingxia","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shidong","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaojie","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojing","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"issue":"12","key":"45862_CR1","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/S2213-8587(19)30081-6","volume":"7","author":"D Selvarajah","year":"2019","unstructured":"Selvarajah, D. et al. Diabetic peripheral neuropathy: Advances in diagnosis and strategies for screening and early intervention. Lancet Diabetes Endocrinol. 7(12), 938\u2013948 (2019).","journal-title":"Lancet Diabetes Endocrinol."},{"issue":"21","key":"45862_CR2","doi-asserted-by":"publisher","first-page":"6826","DOI":"10.3390\/s24216826","volume":"24","author":"RZU Rehman","year":"2024","unstructured":"Rehman, R. Z. U. et al. Assessment of physiological signals from photoplethysmography sensors compared to an electrocardiogram sensor: A validation study in daily life. Sensors 24(21), 6826 (2024).","journal-title":"Sensors"},{"key":"45862_CR3","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.bspc.2015.11.012","volume":"25","author":"K K\u00e6rgaard","year":"2016","unstructured":"K\u00e6rgaard, K., Jensen, S. H. & Puthusserypady, S. A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising. Biomed. Signal Process. Control 25, 178\u2013187 (2016).","journal-title":"Biomed. Signal Process. Control"},{"key":"45862_CR4","first-page":"178","volume":"25","author":"S Li","year":"2024","unstructured":"Li, S., Li, J., Mao, J. D., Hongm, W. & Sun, A. A denoising method for ECG signals based on CEEMDAN-TSO and stacked sparse autoencoders. Comput. Biol. Med. 25, 178\u2013187 (2024).","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"45862_CR5","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1016\/j.bbe.2019.12.007","volume":"40","author":"M Altuve","year":"2020","unstructured":"Altuve, M., Su\u00e1rez, L. & Ardila, J. Fundamental heart sounds analysis using improved complete ensemble EMD with adaptive noise. Biocybern. Biomed. Eng. 40(1), 426\u2013439 (2020).","journal-title":"Biocybern. Biomed. Eng."},{"issue":"2","key":"45862_CR6","doi-asserted-by":"publisher","first-page":"937","DOI":"10.3390\/s23020937","volume":"23","author":"C Camara","year":"2023","unstructured":"Camara, C., Peris-Lopez, P., Safkhani, M. & Bagheri, N. ECG identification based on the Gramian angular field and tested with individuals in resting and activity states. Sensors 23(2), 937 (2023).","journal-title":"Sensors"},{"key":"45862_CR7","doi-asserted-by":"publisher","first-page":"102854","DOI":"10.1016\/j.bspc.2021.102854","volume":"69","author":"A Shankar","year":"2021","unstructured":"Shankar, A., Khaing, H. K. & Dandapat, S. Analysis of epileptic seizures based on EEG using recurrence plot images and deep learning. Biomed. Signal Process. Control 69, 102854 (2021).","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"45862_CR8","doi-asserted-by":"publisher","first-page":"617","DOI":"10.3390\/rs15030617","volume":"15","author":"X Yang","year":"2023","unstructured":"Yang, X., Yang, X., Zhang, C. & Wang, J. SAR image classification using markov random fields with deep learning. Remote Sens. 15(3), 617 (2023).","journal-title":"Remote Sens."},{"key":"45862_CR9","doi-asserted-by":"publisher","first-page":"107329","DOI":"10.1016\/j.compbiolchem.2020.107329","volume":"88","author":"M Rahman","year":"2020","unstructured":"Rahman, M., Islam, D., Mukti, R. J. & Saha, I. A deep learning approach based on convolutional LSTM for detecting diabetes. Comput. Biol. Chem. 88, 107329 (2020).","journal-title":"Comput. Biol. Chem."},{"key":"45862_CR10","doi-asserted-by":"publisher","first-page":"105968","DOI":"10.1016\/j.cmpb.2021.105968","volume":"202","author":"MT Garc\u00eda-Ord\u00e1s","year":"2021","unstructured":"Garc\u00eda-Ord\u00e1s, M. T., Benavides, C., Ben\u00edtez-Andrades, J. A., Alaiz-Moret\u00f3n, H. & Garc\u00eda-Rodr\u00edguez, I. Diabetes detection using deep learning techniques with oversampling and feature augmentation. Comput. Methods Programs Biomed. 202, 105968 (2021).","journal-title":"Comput. Methods Programs Biomed."},{"key":"45862_CR11","doi-asserted-by":"publisher","first-page":"106902","DOI":"10.1016\/j.bspc.2024.106902","volume":"99","author":"SA Tanim","year":"2025","unstructured":"Tanim, S. A. et al. Explainable deep learning for diabetes diagnosis with DeepNetX2. Biomed. Signal Process. Control 99, 106902 (2025).","journal-title":"Biomed. Signal Process. Control"},{"key":"45862_CR12","doi-asserted-by":"publisher","first-page":"3073","DOI":"10.1007\/s40747-021-00398-7","volume":"8","author":"H Gupta","year":"2022","unstructured":"Gupta, H., Varshney, H., Sharma, T. K., Pachauri, N. & Verma, O. P. Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction. Complex Intell. Syst. 8, 3073\u20133087 (2022).","journal-title":"Complex Intell. Syst."},{"issue":"5","key":"45862_CR13","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1111\/jdi.13738","volume":"13","author":"AJ Jiang","year":"2022","unstructured":"Jiang, A. J. et al. Heart rate-corrected QT interval: A novel diagnostic biomarker for diabetic peripheral neuropathy. J. Diabet. Investig. 13(5), 850\u2013885 (2022).","journal-title":"J. Diabet. Investig."},{"issue":"7","key":"45862_CR14","doi-asserted-by":"publisher","first-page":"754","DOI":"10.3390\/e22070754","volume":"22","author":"HC Wei","year":"2020","unstructured":"Wei, H. C. et al. Prognosis of diabetic peripheral neuropathy via decomposed digital volume pulse from the fingertip. Entropy 22(7), 754 (2020).","journal-title":"Entropy"},{"issue":"5","key":"45862_CR15","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1088\/0967-3334\/34\/5\/513","volume":"34","author":"A Keikhosravi","year":"2013","unstructured":"Keikhosravi, A., Aghajani, H. & Zahedi, E. Discrimination of bilateral finger photoplethysmogram responses to reactive hyperemia in diabetic and healthy subjects using a differential vascular model framework. Physiol. Meas. 34(5), 513\u2013525 (2013).","journal-title":"Physiol. Meas."},{"key":"45862_CR16","doi-asserted-by":"crossref","unstructured":"Chowdhury, A., Chowdhury, M. H., Das, D., et al. FPGA implementation of PPG-based cardiovascular diseases and diabetes classification algorithm. Arabian J. Sci. Eng., 1\u201313 (2024).","DOI":"10.1007\/s13369-024-09202-3"},{"key":"45862_CR17","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1140\/epjp\/s13360-025-07023-3","volume":"140","author":"DSR Kumar","year":"2025","unstructured":"Kumar, D. S. R., Maram, B. & Kshirsagar, P. R. Deep belief parallel forward harmonic network for cardiovascular disease detection using ECG images. Eur. Phys. J. Plus 140, 1113 (2025).","journal-title":"Eur. Phys. J. Plus"},{"key":"45862_CR18","doi-asserted-by":"crossref","unstructured":"\u015eent\u00fcrk, \u00dc., Y\u00fcceda\u011f, I., Polat, K. Repetitive neural network (RNN) based blood pressure estimation using PPG and ECG signals. In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 1\u20134. (2018).","DOI":"10.1109\/ISMSIT.2018.8567071"},{"key":"45862_CR19","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2023.1246746","author":"Y Ansari","year":"2023","unstructured":"Ansari, Y., Omar, M., Qaraqe, K. & Serpedin, E. Deep learning for ECG arrhythmia detection and classification: An overview of progress for period 2017-2023. Front. Physiol. https:\/\/doi.org\/10.3389\/fphys.2023.1246746 (2023).","journal-title":"Front. Physiol."},{"key":"45862_CR20","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s41782-020-00101-9","volume":"4","author":"S Santaji","year":"2020","unstructured":"Santaji, S. & Desai, V. Analysis of EEG signal to classify sleep stages using machine learning. Sleep Vigil. 4, 145\u2013152 (2020).","journal-title":"Sleep Vigil."},{"key":"45862_CR21","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s11036-020-01672-7","volume":"26","author":"J Wang","year":"2021","unstructured":"Wang, J. et al. A review of deep learning on medical image analysis. Mobile Netw. Appl. 26, 351\u2013380 (2021).","journal-title":"Mobile Netw. Appl."},{"key":"45862_CR22","doi-asserted-by":"publisher","first-page":"15815","DOI":"10.1007\/s00521-021-06202-4","volume":"33","author":"M Nour","year":"2021","unstructured":"Nour, M., \u00d6zt\u00fcrk, \u015e & Polat, K. A novel classification framework using multiple bandwidth method with optimized CNN for brain-computer interfaces with EEG-fNIRS signals. Neural Comput. Applic 33, 15815\u201315829 (2021).","journal-title":"Neural Comput. Applic"},{"issue":"3","key":"45862_CR23","doi-asserted-by":"publisher","first-page":"282","DOI":"10.55730\/1300-0632.4127","volume":"33","author":"P Mahajan","year":"2025","unstructured":"Mahajan, P. & Kaul, A. Enhanced cuffless blood pressure estimation using ECG and PPG signals: A hybrid approach with Windkessel, ARIMA, and LSTM. Turk. J. Electr. Eng. Comput. Sci. 33(3), 282\u2013305 (2025).","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"issue":"3","key":"45862_CR24","doi-asserted-by":"publisher","first-page":"358","DOI":"10.55730\/1300-0632.4075","volume":"32","author":"E Eren","year":"2024","unstructured":"Eren, E., Yildirim, O. F. & \u00d6zdemir, S. Unveiling anomalies: A survey on XAI-based anomaly detection for IoT. Turk. J. Electr. Eng. Comput. Sci. 32(3), 358\u2013381 (2024).","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"45862_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S1793536909000047","volume":"1","author":"ZH Wu","year":"2009","unstructured":"Wu, Z. H. & Huang, N. E. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Data Sci. Adapt. Anal. 1, 1\u201341 (2009).","journal-title":"Adv. Data Sci. Adapt. Anal."},{"issue":"1971","key":"45862_CR26","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang, N. E. et al. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Royal Soc. London Series A: Math., Phys. Eng. Sci. 454(1971), 903\u2013995. https:\/\/doi.org\/10.1098\/rspa.1998.0193 (1998).","journal-title":"Proc. Royal Soc. London Series A: Math., Phys. Eng. Sci."},{"key":"45862_CR27","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.bspc.2014.06.009","volume":"14","author":"MA Colominas","year":"2014","unstructured":"Colominas, M. A., Schlotthauer, G. & Torres, M. E. Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomed. Signal Process. Control. 14, 19\u201329 (2014).","journal-title":"Biomed. Signal Process. Control."},{"issue":"3","key":"45862_CR28","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1142\/S1793536910000549","volume":"2","author":"G Wang","year":"2010","unstructured":"Wang, G., Chen, X. Y., Qiao, F. L., Wu, Z. H. & Huang, N. E. On intrinsic mode function. Adv. Adapt. Data Anal. 2(3), 277\u2013293 (2010).","journal-title":"Adv. Adapt. Data Anal."},{"issue":"3","key":"45862_CR29","doi-asserted-by":"publisher","first-page":"455","DOI":"10.3390\/sym14030455","volume":"14","author":"D-D Ram\u00edrez-Ochoa","year":"2022","unstructured":"Ram\u00edrez-Ochoa, D.-D., P\u00e9rez-Dom\u00ednguez, L. A., Mart\u00ednez-G\u00f3mez, E.-A. & Luviano-Cruz, D. PSO, a swarm intelligence-based evolutionary algorithm as a decision-making strategy: A review. Symmetry 14(3), 455 (2022).","journal-title":"Symmetry"},{"issue":"1","key":"45862_CR30","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3390\/bios13010023","volume":"13","author":"IM Chiu","year":"2022","unstructured":"Chiu, I. M. et al. Utilization of personalized machine-learning to screen for dysglycemia from ambulatory ECG, toward noninvasive blood glucose monitoring. Biosensors (Basel) 13(1), 23 (2022).","journal-title":"Biosensors (Basel)"},{"key":"45862_CR31","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkorei, J., Jones, L., Gomez, A. N., Kaiser, L., Polosukhin, I. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, 6000\u20136010. (2017)."},{"key":"45862_CR32","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, T., Song, X., He R, He, F. Coronary artery disease detection based on photoplethysmography via gramian angular field transformation and deep learning. J. Intell. Med., 1\u201310. (2025).","DOI":"10.1002\/jim4.70014"},{"issue":"8","key":"45862_CR33","doi-asserted-by":"publisher","first-page":"3130","DOI":"10.1109\/JBHI.2021.3061114","volume":"25","author":"C Ouyang","year":"2021","unstructured":"Ouyang, C. et al. Inter-patient classification with encoded peripheral pulse series and multi-task fusion CNN: Application in type 2 diabetes. IEEE J. Biomed. Health Inform. 25(8), 3130\u20133140 (2021).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"45862_CR34","unstructured":"Tan, M., Le, Q. EfficientNetV2: Smaller models and faster training. In Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, 139: 10096\u201310106 (2021)."},{"issue":"1","key":"45862_CR35","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1115\/1.3653121","volume":"86","author":"HJ Kushner","year":"1964","unstructured":"Kushner, H. J. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J. Basic Eng. 86(1), 97\u2013106 (1964).","journal-title":"J. Basic Eng."},{"issue":"9","key":"45862_CR36","doi-asserted-by":"publisher","first-page":"6486","DOI":"10.1109\/TPAMI.2024.3382294","volume":"46","author":"P Zhou","year":"2024","unstructured":"Zhou, P., Xie, X., Lin, Z. & Yan, S. Towards understanding convergence and generalization of AdamW. IEEE Trans. Pattern Anal. Mach. Intell. 46(9), 6486\u20136493 (2024).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"45862_CR37","doi-asserted-by":"crossref","unstructured":"Wang, C. L., Wang, J. H., Xie, J. H. Cosine annealing weights in knowledge distillation. In Proceedings of the 2025 6th International Conference on Computer Information and Big Data Applications. New York: Association for Computing Machinery, 222\u2013228 (2025).","DOI":"10.1145\/3746709.3746749"},{"key":"45862_CR38","first-page":"1","volume":"2024","author":"K Rohitha","year":"2024","unstructured":"Rohitha, K., Supriya, K., Kuchoor, S. K. & Bodapati, J. D. Optimizing deep convolutional neural networks with progressive unfreezing for enhanced sports activity recognition. Int. Conf. Integr. Intell. Commun. Syst. (ICIICS) 2024, 1\u20136 (2024).","journal-title":"Int. Conf. Integr. Intell. Commun. Syst. (ICIICS)"},{"issue":"A","key":"45862_CR39","doi-asserted-by":"publisher","first-page":"105602","DOI":"10.1016\/j.bspc.2023.105602","volume":"88","author":"D Rastogi","year":"2024","unstructured":"Rastogi, D., Johri, P., Tiwari, V. & Elngar, A. A. Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross validation deep learning framework. Biomed. Signal Process. Control 88(A), 105602 (2024).","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"45862_CR40","doi-asserted-by":"publisher","first-page":"331","DOI":"10.3390\/biomechanics2030026","volume":"2","author":"M Pau","year":"2022","unstructured":"Pau, M. et al. Cyclograms reveal alteration of inter-joint coordination during gait in people with multiple sclerosis minimally disabled. Biomechanics 2(3), 331\u2013341 (2022).","journal-title":"Biomechanics"},{"issue":"1","key":"45862_CR41","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3390\/diagnostics10010032","volume":"10","author":"HC Wei","year":"2020","unstructured":"Wei, H. C. et al. Percussion entropy analysis of synchronized ECG and PPG signals as a prognostic indicator for future peripheral neuropathy in type 2 diabetic subjects. Diagnostics 10(1), 32 (2020).","journal-title":"Diagnostics"},{"issue":"12","key":"45862_CR42","doi-asserted-by":"publisher","first-page":"591","DOI":"10.3390\/bioengineering12060591","volume":"12","author":"KN Dibbern","year":"2025","unstructured":"Dibbern, K. N. et al. Scoping review of machine learning techniques in marker-based clinical gait analysis. Bioengineering 12(12), 591 (2025).","journal-title":"Bioengineering"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-45862-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-45862-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-45862-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T11:12:46Z","timestamp":1774955566000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-45862-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["45862"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-45862-x","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,27]]},"assertion":[{"value":"10 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 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 that there is no potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was approved by the Biomedical Research Ethics Committee of Northern University for Nationalities (Approval number: 2024-2). Patients provided written informed consent to participate in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"10954"}}