{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T12:59:25Z","timestamp":1769777965418,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":43,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557189","type":"print"},{"value":"9789819557196","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5719-6_11","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:37Z","timestamp":1769718877000},"page":"162-178","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MedDPA: Multi-scale Decomposition and\u00a0Prototype-Based Channel Aggregation for\u00a0Medical Time Series Classification"],"prefix":"10.1007","author":[{"given":"Xiaotian","family":"Gu","sequence":"first","affiliation":[]},{"given":"Pengfei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yiqiao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaoling","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Tianwen","family":"Qian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"issue":"7","key":"11_CR1","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1038\/s41591-023-02396-3","volume":"29","author":"SS Al-Zaiti","year":"2023","unstructured":"Al-Zaiti, S.S., et al.: Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat. Med. 29(7), 1804\u20131813 (2023)","journal-title":"Nat. Med."},{"issue":"1","key":"11_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijpsycho.2012.07.002","volume":"86","author":"E Ba\u015far","year":"2012","unstructured":"Ba\u015far, E.: A review of alpha activity in integrative brain function: fundamental physiology, sensory coding, cognition and pathology. Int. J. Psychophysiol. 86(1), 1\u201324 (2012)","journal-title":"Int. J. Psychophysiol."},{"issue":"3","key":"11_CR3","doi-asserted-by":"publisher","first-page":"291","DOI":"10.31887\/DCNS.2013.15.3\/ebasar","volume":"15","author":"E Ba\u015far","year":"2013","unstructured":"Ba\u015far, E.: Brain oscillations in neuropsychiatric disease. Dialogues Clin. Neurosci. 15(3), 291\u2013300 (2013)","journal-title":"Dialogues Clin. Neurosci."},{"issue":"8","key":"11_CR4","doi-asserted-by":"publisher","first-page":"2895","DOI":"10.1109\/JBHI.2021.3057891","volume":"25","author":"J Cao","year":"2021","unstructured":"Cao, J., et al.: Unsupervised eye blink artifact detection from EEG with Gaussian mixture model. IEEE J. Biomed. Health Inform. 25(8), 2895\u20132905 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"14","key":"11_CR5","doi-asserted-by":"publisher","first-page":"6434","DOI":"10.3390\/s23146434","volume":"23","author":"A Chaddad","year":"2023","unstructured":"Chaddad, A., Wu, Y., Kateb, R., Bouridane, A.: Electroencephalography signal processing: a comprehensive review and analysis of methods and techniques. Sensors 23(14), 6434 (2023)","journal-title":"Sensors"},{"key":"11_CR6","unstructured":"Chen, S.A., Li, C.L., Arik, S.O., Yoder, N.C., Pfister, T.: Tsmixer: an all-MLP architecture for time series forecasting. Trans. Mach. Learn. Res. (2023)"},{"issue":"1","key":"11_CR7","doi-asserted-by":"publisher","first-page":"016025","DOI":"10.1088\/1741-2552\/ab405f","volume":"17","author":"G Dai","year":"2020","unstructured":"Dai, G., Zhou, J., Huang, J., Wang, N.: Hs-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification. J. Neural Eng. 17(1), 016025 (2020)","journal-title":"J. Neural Eng."},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Demir, A., Koike-Akino, T., Wang, Y., Haruna, M., Erdogmus, D.: EEG-GNN: graph neural networks for classification of electroencephalogram (EEG) signals. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1061\u20131067. IEEE (2021)","DOI":"10.1109\/EMBC46164.2021.9630194"},{"issue":"11","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1088\/0967-3334\/27\/11\/004","volume":"27","author":"J Escudero","year":"2006","unstructured":"Escudero, J., Ab\u00e1solo, D., Hornero, R., Espino, P., L\u00f3pez, M.: Analysis of electroencephalograms in Alzheimer\u2019s disease patients with multiscale entropy. Physiol. Meas. 27(11), 1091 (2006)","journal-title":"Physiol. Meas."},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","volume":"161","author":"O Faust","year":"2018","unstructured":"Faust, O., Hagiwara, Y., Hong, T.J., Lih, O.S., Acharya, U.R.: Deep learning for healthcare applications based on physiological signals: a review. Comput. Methods Programs Biomed. 161, 1\u201313 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"23","key":"11_CR11","doi-asserted-by":"publisher","first-page":"e215","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), e215\u2013e220 (2000)","journal-title":"Circulation"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Han, L., Chen, X.Y., Ye, H.J., Zhan, D.C.: Softs: efficient multivariate time series forecasting with series-core fusion. In: The Thirty-eighth Annual Conference on Neural Information Processing Systems (2024)","DOI":"10.52202\/079017-2046"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Lai, C.Q., Ibrahim, H., Abdullah, M.Z., Abdullah, J.M., Suandi, S.A., Azman, A.: Artifacts and noise removal for electroencephalogram (EEG): a literature review. In: 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 326\u2013332. IEEE (2018)","DOI":"10.1109\/ISCAIE.2018.8405493"},{"issue":"1","key":"11_CR14","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1088\/0967-3334\/29\/1\/002","volume":"29","author":"Q Li","year":"2007","unstructured":"Li, Q., Mark, R.G., Clifford, G.D.: Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol. Meas. 29(1), 15 (2007)","journal-title":"Physiol. Meas."},{"issue":"10","key":"11_CR15","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.3390\/math12101504","volume":"12","author":"X Liu","year":"2024","unstructured":"Liu, X., Wang, W.: Deep time series forecasting models: a comprehensive survey. Mathematics 12(10), 1504 (2024)","journal-title":"Mathematics"},{"key":"11_CR16","doi-asserted-by":"publisher","first-page":"107187","DOI":"10.1016\/j.knosys.2021.107187","volume":"227","author":"X Liu","year":"2021","unstructured":"Liu, X., Wang, H., Li, Z., Qin, L.: Deep learning in ECG diagnosis: a review. Knowl.-Based Syst. 227, 107187 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"11_CR17","unstructured":"Liu, Y., et al.: itransformer: inverted transformers are effective for time series forecasting. In: The Twelfth International Conference on Learning Representations (2024)"},{"issue":"8","key":"11_CR18","doi-asserted-by":"publisher","first-page":"2168","DOI":"10.1109\/TBME.2011.2113395","volume":"58","author":"T Mar","year":"2011","unstructured":"Mar, T., Zaunseder, S., Mart\u00ednez, J.P., Llamedo, M., Poll, R.: Optimization of ECG classification by means of feature selection. IEEE Trans. Biomed. Eng. 58(8), 2168\u20132177 (2011)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"11_CR19","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3390\/data8060095","volume":"8","author":"A Miltiadous","year":"2023","unstructured":"Miltiadous, A., et al.: A dataset of scalp EEG recordings of Alzheimer\u2019s disease, frontotemporal dementia and healthy subjects from routine EEG. Data 8(6), 95 (2023)","journal-title":"Data"},{"issue":"8","key":"11_CR20","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.3390\/diagnostics11081437","volume":"11","author":"A Miltiadous","year":"2021","unstructured":"Miltiadous, A., et al.: Alzheimer\u2019s disease and frontotemporal dementia: a robust classification method of EEG signals and a comparison of validation methods. Diagnostics 11(8), 1437 (2021)","journal-title":"Diagnostics"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Murad, M.M.N., Aktukmak, M., Yilmaz, Y.: Wpmixer: efficient multi-resolution mixing for long-term time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a039, pp. 19581\u201319588 (2025)","DOI":"10.1609\/aaai.v39i18.34156"},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.bspc.2016.07.007","volume":"31","author":"S Padhy","year":"2017","unstructured":"Padhy, S., Dandapat, S.: Third-order tensor based analysis of multilead ECG for classification of myocardial infarction. Biomed. Signal Process. Control 31, 71\u201378 (2017)","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"11_CR23","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.tins.2007.02.001","volume":"30","author":"S Palva","year":"2007","unstructured":"Palva, S., Palva, J.M.: New vistas for $$\\alpha $$-frequency band oscillations. Trends Neurosci. 30(4), 150\u2013158 (2007)","journal-title":"Trends Neurosci."},{"key":"11_CR24","unstructured":"Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., Ng, A.Y.: Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836 (2017)"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"103275","DOI":"10.1016\/j.bspc.2021.103275","volume":"71","author":"A Rasti-Meymandi","year":"2022","unstructured":"Rasti-Meymandi, A., Ghaffari, A.: A deep learning-based framework for ECG signal denoising based on stacked cardiac cycle tensor. Biomed. Signal Process. Control 71, 103275 (2022)","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"11_CR26","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/0013-4694(93)90023-O","volume":"87","author":"B Rockstroh","year":"1993","unstructured":"Rockstroh, B., M\u00fcller, M., Wagner, M., Cohen, R., Elbert, T.: probing the nature of the CNV. Electroencephalogr. Clin. Neurophysiol. 87(4), 235\u2013241 (1993)","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"issue":"2","key":"11_CR27","doi-asserted-by":"publisher","first-page":"225","DOI":"10.37936\/ecti-eec.2022202.246906","volume":"20","author":"TB Shams","year":"2022","unstructured":"Shams, T.B., Hossain, M.S., Mahmud, M.F., Tehjib, M.S., Hossain, Z., Pramanik, M.I.: EEG-based biometric authentication using machine learning: a comprehensive survey. ECTI Trans. Electr. Eng. Electron. Commun. 20(2), 225\u2013241 (2022)","journal-title":"ECTI Trans. Electr. Eng. Electron. Commun."},{"key":"11_CR28","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","volume":"31","author":"Y Song","year":"2022","unstructured":"Song, Y., Zheng, Q., Liu, B., Gao, X.: EEG conformer: convolutional transformer for EEG decoding and visualization. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 710\u2013719 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"4","key":"11_CR29","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1016\/j.eswa.2006.02.005","volume":"32","author":"A Subasi","year":"2007","unstructured":"Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32(4), 1084\u20131093 (2007)","journal-title":"Expert Syst. Appl."},{"key":"11_CR30","unstructured":"Van Den\u00a0Oord, A., Vinyals, O., et\u00a0al.: Neural discrete representation learning. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"1","key":"11_CR31","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/s41597-022-01409-z","volume":"9","author":"H Van Dijk","year":"2022","unstructured":"Van Dijk, H., Van Wingen, G., Denys, D., Olbrich, S., Van Ruth, R., Arns, M.: The two decades brainclinics research archive for insights in neurophysiology (TDBrain) database. Sci. Data 9(1), 333 (2022)","journal-title":"Sci. Data"},{"key":"11_CR32","unstructured":"Wang, S., et al.: Timemixer: decomposable multiscale mixing for time series forecasting. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"11_CR33","doi-asserted-by":"publisher","first-page":"1126994","DOI":"10.3389\/fpsyg.2023.1126994","volume":"14","author":"X Wang","year":"2023","unstructured":"Wang, X., Ren, Y., Luo, Z., He, W., Hong, J., Huang, Y.: Deep learning-based EEG emotion recognition: current trends and future perspectives. Front. Psychol. 14, 1126994 (2023)","journal-title":"Front. Psychol."},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, N., Li, T., Yan, Y., Zhang, X.: Medformer: a multi-granularity patching transformer for medical time-series classification. In: The Thirty-Eighth Annual Conference on Neural Information Processing Systems (2024)","DOI":"10.52202\/079017-1145"},{"key":"11_CR35","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: temporal 2D-variation modeling for general time series analysis. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"11_CR36","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1109\/TNSRE.2022.3166181","volume":"30","author":"Q Xin","year":"2022","unstructured":"Xin, Q., Hu, S., Liu, S., Zhao, L., Zhang, Y.D.: An attention-based wavelet convolution neural network for epilepsy EEG classification. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 957\u2013966 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"11_CR37","first-page":"76656","volume":"36","author":"K Yi","year":"2023","unstructured":"Yi, K., et al.: Frequency-domain MLPS are more effective learners in time series forecasting. Adv. Neural. Inf. Process. Syst. 36, 76656\u201376679 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 11121\u201311128 (2023)","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"11_CR39","unstructured":"Zhang, T., et al.: Less is more: fast multivariate time series forecasting with light sampling-oriented MLP structures. arXiv preprint arXiv:2207.01186 (2022)"},{"key":"11_CR40","unstructured":"Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"11_CR41","doi-asserted-by":"crossref","unstructured":"Zhong, B., Wang, P., Wang, X.: Ts-HCL: hierarchical layer-wise contrastive learning for unsupervised domain adaptation on time-series. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, pp. 31\u201345. Springer (2024)","DOI":"10.1007\/978-981-97-7238-4_3"},{"key":"11_CR42","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"11_CR43","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: Fedformer: frequency enhanced decomposed transformer for long-term series forecasting. In: International Conference on Machine Learning, pp. 27268\u201327286. PMLR (2022)"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5719-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:42Z","timestamp":1769718882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5719-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557189","9789819557196"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5719-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb2025.sau.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}