{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:16:10Z","timestamp":1779365770297,"version":"3.53.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China,China","award":["2022YFC2407000, 2022YFC2407003"],"award-info":[{"award-number":["2022YFC2407000, 2022YFC2407003"]}]},{"name":"National Key Research and Development Program of China,China","award":["2022YFC2407000, 2022YFC2407003"],"award-info":[{"award-number":["2022YFC2407000, 2022YFC2407003"]}]},{"name":"National Key Research and Development Program of China,China","award":["2022YFC2407000, 2022YFC2407003"],"award-info":[{"award-number":["2022YFC2407000, 2022YFC2407003"]}]},{"name":"National Key Research and Development Program of China,China","award":["2022YFC2407000, 2022YFC2407003"],"award-info":[{"award-number":["2022YFC2407000, 2022YFC2407003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00371-025-04258-0","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:32:56Z","timestamp":1777573976000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transformer-Riemannian approach for MCG signal classification"],"prefix":"10.1007","volume":"42","author":[{"given":"Jinyang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YiJing","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanhao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengxing","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"issue":"15","key":"4258_CR1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.121.025224","volume":"11","author":"A Wacker-Gussmann","year":"2022","unstructured":"Wacker-Gussmann, A., Strasburger, J.F., Wakai, R.T.: Contribution of fetal magnetocardiography to diagnosis, risk assessment, and treatment of fetal arrhythmia. J. Am. Heart Assoc. 11(15), e025224 (2022)","journal-title":"J. Am. Heart Assoc."},{"key":"4258_CR2","doi-asserted-by":"publisher","first-page":"1242215","DOI":"10.3389\/fcvm.2023.1242215","volume":"10","author":"A-Y Her","year":"2023","unstructured":"Her, A.-Y., Dischl, D., Kim, Y.H., Kim, S.-W., Shin, E.-S.: Magnetocardiography for the detection of myocardial ischemia. Front. Cardiovasc. Med. 10, 1242215 (2023)","journal-title":"Front. Cardiovasc. Med."},{"issue":"4","key":"4258_CR3","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.122.027619","volume":"12","author":"D Brala","year":"2023","unstructured":"Brala, D., Thevathasan, T., Grahl, S., Barrow, S., Violano, M., Bergs, H., Golpour, A., Suwalski, P., Poller, W., Skurk, C., et al.: Application of magnetocardiography to screen for inflammatory cardiomyopathy and monitor treatment response. J. Am. Heart Assoc. 12(4), e027619 (2023)","journal-title":"J. Am. Heart Assoc."},{"key":"4258_CR4","doi-asserted-by":"publisher","first-page":"1232882","DOI":"10.3389\/fcvm.2023.1232882","volume":"10","author":"D Brisinda","year":"2023","unstructured":"Brisinda, D., Fenici, P., Fenici, R.: Clinical magnetocardiography: the unshielded bet\u2014past, present, and future. Front. Cardiovasc. Med. 10, 1232882 (2023)","journal-title":"Front. Cardiovasc. Med."},{"key":"4258_CR5","doi-asserted-by":"publisher","first-page":"1577662","DOI":"10.3389\/fcvm.2025.1577662","volume":"12","author":"X Chen","year":"2025","unstructured":"Chen, X., Li, L., Xu, X., Xia, L., Liu, C., Shen, C., Luo, Y.: New clinical application of magnetocardiography: diagnosis of left ventricular hypertrophy. Front. Cardiovasc. Med. 12, 1577662 (2025)","journal-title":"Front. Cardiovasc. Med."},{"key":"4258_CR6","doi-asserted-by":"crossref","unstructured":"Heidecker, B.: Rediscovery of magnetocardiography for diagnostic screening and monitoring of treatment response in cardiology (2023)","DOI":"10.1093\/eurheartj\/ehad213"},{"issue":"4","key":"4258_CR7","doi-asserted-by":"publisher","first-page":"407","DOI":"10.26599\/1671-5411.2024.04.006","volume":"21","author":"C Jian-Guo","year":"2024","unstructured":"Jian-Guo, C., Feng, T., Yu-Hao, M., Qin-Hua, J., Ya-Jun, S., Li, L., Meng-Jun, S., Xiao-Ming, X., Yun-Dai, C., et al.: Accurate diagnosis of severe coronary stenosis based on resting magnetocardiography: a prospective, single-center, cross-sectional analysis. J. Geriatr. Cardiol. JGC 21(4), 407 (2024)","journal-title":"J. Geriatr. Cardiol. JGC"},{"key":"4258_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108489","volume":"258","author":"I Chaikovsky","year":"2025","unstructured":"Chaikovsky, I., Nedayvoda, I., Primin, M.: A consistent decision support system for interpreting of magnetocardiographic data as a tool to improve the acceptance of magnetocardiography in clinical practice. Comput. Methods Programs Biomed. 258, 108489 (2025)","journal-title":"Comput. Methods Programs Biomed."},{"key":"4258_CR9","doi-asserted-by":"crossref","unstructured":"Yang, M., Sun, C., Zhao, B., Wu, B., Xiang, J., Xu, M., Wu, T., Zhang, J., Xu, W., Guo, H.: Magnetocardiography for the diagnosis of coronary artery disease: a systematic review and meta-analysis, medRxiv, pp. 2024\u201301 (2024)","DOI":"10.1101\/2024.01.31.24302044"},{"key":"4258_CR10","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.1109\/ACCESS.2021.3138976","volume":"10","author":"S Sakib","year":"2021","unstructured":"Sakib, S., Fouda, M.M., Al-Mahdawi, M., Mohsen, A., Oogane, M., Ando, Y., Fadlullah, Z.M.: Deep learning models for magnetic cardiography edge sensors implementing noise processing and diagnostics. IEEE Access 10, 2656\u20132668 (2021)","journal-title":"IEEE Access"},{"issue":"6","key":"4258_CR11","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1109\/TBME.2018.2877649","volume":"66","author":"R Tao","year":"2018","unstructured":"Tao, R., Zhang, S., Huang, X., Tao, M., Ma, J., Ma, S., Zhang, C., Zhang, T., Tang, F., Lu, J., et al.: Magnetocardiography-based ischemic heart disease detection and localization using machine learning methods. IEEE Trans. Biomed. Eng. 66(6), 1658\u20131667 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"4258_CR12","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1109\/JBHI.2021.3128169","volume":"26","author":"R Tao","year":"2021","unstructured":"Tao, R., Zhang, S., Wang, Y., Mi, X., Ma, J., Shen, C., Zheng, G.: MCG-Net: End-to-end fine-grained delineation and diagnostic classification of cardiac events from magnetocardiographs. IEEE J. Biomed. Health Inform. 26(3), 1057\u20131067 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"4258_CR13","doi-asserted-by":"publisher","first-page":"7296","DOI":"10.1038\/s41598-024-58010-0","volume":"14","author":"T Yamaguchi","year":"2024","unstructured":"Yamaguchi, T., Adachi, Y., Tanida, T., Taguchi, K., Oka, Y., Yoshida, T., Kim, W.-C., Takahashi, K., Tanaka, M.: Detection of biomagnetic signals from induced pluripotent stem cell-derived cardiomyocytes using deep learning with simulation data. Sci. Rep. 14(1), 7296 (2024)","journal-title":"Sci. Rep."},{"key":"4258_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.107602","volume":"105","author":"R Wang","year":"2025","unstructured":"Wang, R., Pang, J., Han, X., Xiang, M., Ning, X.: Automated magnetocardiography classification using a deformable convolutional block attention module. Biomed. Signal Process. Control 105, 107602 (2025)","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"4258_CR15","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s12911-024-02493-4","volume":"24","author":"K Wang","year":"2024","unstructured":"Wang, K., Zhang, K., Liu, B., Chen, W., Han, M.: Early prediction of sudden cardiac death risk with nested LSTM based on electrocardiogram sequential features. BMC Med. Inform. Decis. Mak. 24(1), 94 (2024)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"4258_CR16","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, X., Bennamoun, M., Sheng, B.: Non-rigid point cloud registration via anisotropic hybrid field harmonization. IEEE Trans. Pattern Anal. Mach. Intell. 1\u201318 (2025)","DOI":"10.1109\/TPAMI.2025.3572584"},{"issue":"11","key":"4258_CR17","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.3390\/bioengineering11111109","volume":"11","author":"Y Jia","year":"2024","unstructured":"Jia, Y., Pei, H., Liang, J., Zhou, Y., Yang, Y., Cui, Y., Xiang, M.: Preprocessing and denoising techniques for electrocardiography and magnetocardiography: a review. Bioengineering 11(11), 1109 (2024)","journal-title":"Bioengineering"},{"key":"4258_CR18","doi-asserted-by":"crossref","unstructured":"Rosu, G., Rau, M.C., Baltag, O.: Comparison of signal processing methods applied on a magnetocardiographic signal. In: 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE). IEEE, pp. 248\u2013253 (2017)","DOI":"10.1109\/ATEE.2017.7905161"},{"issue":"3","key":"4258_CR19","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1586\/14737159.5.3.291","volume":"5","author":"R Fenici","year":"2005","unstructured":"Fenici, R., Brisinda, D., Meloni, A.M.: Clinical application of magnetocardiography. Expert Rev. Mol. Diagn. 5(3), 291\u2013313 (2005)","journal-title":"Expert Rev. Mol. Diagn."},{"issue":"3","key":"4258_CR20","doi-asserted-by":"publisher","first-page":"227","DOI":"10.3233\/CH-200905","volume":"78","author":"X Huang","year":"2021","unstructured":"Huang, X., Chen, P., Tang, F., Hua, N.: Detection of coronary artery disease in patients with chest pain: a machine learning model based on magnetocardiography parameters. Clin. Hemorheol. Microcirc. 78(3), 227\u2013236 (2021)","journal-title":"Clin. Hemorheol. Microcirc."},{"key":"4258_CR21","doi-asserted-by":"crossref","unstructured":"Embrechts, M., Szymanski, B., Sternickel, K. Naenna, T., Bragaspathi R.: Use of machine learning for classification of magnetocardiograms, In: SMC\u201903 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483), vol.\u00a02, pp. 1400\u20131405. IEEE (2003)","DOI":"10.1109\/ICSMC.2003.1244608"},{"issue":"7","key":"4258_CR22","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/j.compbiomed.2008.04.009","volume":"38","author":"T Tantimongcolwat","year":"2008","unstructured":"Tantimongcolwat, T., Naenna, T., Isarankura-Na-Ayudhya, C., Embrechts, M.J., Prachayasittikul, V.: Identification of ischemic heart disease via machine learning analysis on magnetocardiograms. Comput. Biol. Med. 38(7), 817\u2013825 (2008)","journal-title":"Comput. Biol. Med."},{"key":"4258_CR23","unstructured":"Xu, H., He, W., Xuan, Y., Xia, L., Jiao, C., Luo, J., Zhang, S.: Reconstructing the electrical activity of the heart by precise registration between magnetocardiography and computed tomography using apical calibration algorithm, Available at SSRN 5060135"},{"key":"4258_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.115548","volume":"240","author":"H Wu","year":"2025","unstructured":"Wu, H., Cui, P., Tian, P., Zhang, H., Zhao, X.: Magnetic field interference suppression method based on phase-compensation vector resonance control in near-zero magnetic field environment. Measurement 240, 115548 (2025)","journal-title":"Measurement"},{"key":"4258_CR25","unstructured":"Neufeld, P., Riedrich-Moeller, J., Fuchs, T., Wickenbrock, A., Budker, D.: Design of an optically pumped magnetometer based on hot atomic vapor targeted at medical diagnostics. In: MikroSystemTechnik Kongress,: Kongress. VDE 2023, pp. 117\u2013120 (2023)"},{"issue":"2","key":"4258_CR26","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1088\/0266-5611\/19\/2\/307","volume":"19","author":"T Nara","year":"2003","unstructured":"Nara, T., Ando, S.: A projective method for an inverse source problem of the Poisson equation. Inverse Prob. 19(2), 355 (2003)","journal-title":"Inverse Prob."},{"key":"4258_CR27","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1016\/j.physc.2005.01.078","volume":"426","author":"M Yoshizawa","year":"2005","unstructured":"Yoshizawa, M., He, D., Nakai, K., Kobayashi, K., Nakamura, Y., Yaegashi, M., Ito, M., Yashiro, H., Daibo, M., Simizu, T., et al.: Application of squids in the Iwate create project. Phys. C: Superconduct. Appl. 426, 1572\u20131579 (2005)","journal-title":"Phys. C: Superconduct. Appl."},{"key":"4258_CR28","doi-asserted-by":"crossref","unstructured":"Chandra, K.A., Upadhyay, P.K.: Pressure physiotherapy and bio-signals. In: Immersive Virtual and Augmented Reality in Healthcare, pp. 165\u2013187. CRC Press (2023)","DOI":"10.1201\/9781003340133-9"},{"key":"4258_CR29","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, J., Li, H., Huang, X., Xia, J., Li, Z., Wu, W., Sheng, B.: Temporal goal-aware transformer assisted visual reinforcement learning for virtual table tennis agent. The Visual Computer pp. 1\u201315 (2025)","DOI":"10.1007\/s00371-025-03822-y"},{"key":"4258_CR30","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, X., Li, H., Huang, X., Xia, J.: Enhanced ping pong training assessment via VR: integrating time-spatial alignment and multi-modal fusion. The Visual Computer pp. 1\u201317 (2025)","DOI":"10.1007\/s00371-025-04058-6"},{"key":"4258_CR31","doi-asserted-by":"publisher","unstructured":"Zhu, C.: Mcg dataset, (2024). [Online]. Available: https:\/\/doi.org\/10.21227\/qkpa-bc19","DOI":"10.21227\/qkpa-bc19"},{"issue":"4","key":"4258_CR32","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/ad40b1","volume":"10","author":"S Senthilnathan","year":"2024","unstructured":"Senthilnathan, S., Devi, S.S., Sasikala, M., Satheesh, S., Selvaraj, R.J.: The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG). Biomed. Phys. Eng. Express 10(4), 045007 (2024)","journal-title":"Biomed. Phys. Eng. Express"},{"key":"4258_CR33","doi-asserted-by":"crossref","unstructured":"Jung, T., Kim, J.-K., Lee, S., Kang, D.: Cluster-guided label generation in extreme multi-label classification. Preprint at arXiv:2302.09150 (2023)","DOI":"10.18653\/v1\/2023.eacl-main.122"},{"issue":"3","key":"4258_CR34","doi-asserted-by":"publisher","first-page":"4735","DOI":"10.1093\/mnras\/stad2627","volume":"525","author":"D Tagliacozzo","year":"2023","unstructured":"Tagliacozzo, D., Marinucci, A., Ursini, F., Matt, G., Bianchi, S., Baldini, L., Barnouin, T., Cavero Rodriguez, N., De Rosa, A., Di Gesu, L., et al.: The geometry of the hot corona in mcg-05-23-16 constrained by x-ray polarimetry. Monthly Not. Royal Astronom. Soc. 525(3), 4735\u20134743 (2023)","journal-title":"Monthly Not. Royal Astronom. Soc."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04258-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04258-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04258-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:42:59Z","timestamp":1779363779000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04258-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,30]]},"references-count":34,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["4258"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04258-0","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,30]]},"assertion":[{"value":"29 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"276"}}