{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:16:49Z","timestamp":1771611409970,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Xiamen, China","award":["3502Z202374054"],"award-info":[{"award-number":["3502Z202374054"]}]},{"name":"Natural Science Foundation of Xiamen, China","award":["3502Z202573102"],"award-info":[{"award-number":["3502Z202573102"]}]},{"name":"Natural Science Foundation of Xiamen, China","award":["CKZ24016"],"award-info":[{"award-number":["CKZ24016"]}]},{"name":"Natural Science Foundation of Xiamen, China","award":["CYKYPT02"],"award-info":[{"award-number":["CYKYPT02"]}]},{"name":"Provincial and Ministerial-Level Scientific Research Cultivation Project of Chengyi College, Jimei University","award":["3502Z202374054"],"award-info":[{"award-number":["3502Z202374054"]}]},{"name":"Provincial and Ministerial-Level Scientific Research Cultivation Project of Chengyi College, Jimei University","award":["3502Z202573102"],"award-info":[{"award-number":["3502Z202573102"]}]},{"name":"Provincial and Ministerial-Level Scientific Research Cultivation Project of Chengyi College, Jimei University","award":["CKZ24016"],"award-info":[{"award-number":["CKZ24016"]}]},{"name":"Provincial and Ministerial-Level Scientific Research Cultivation Project of Chengyi College, Jimei University","award":["CYKYPT02"],"award-info":[{"award-number":["CYKYPT02"]}]},{"name":"Big Data Technology Institute of Chengyi College, Jimei University","award":["3502Z202374054"],"award-info":[{"award-number":["3502Z202374054"]}]},{"name":"Big Data Technology Institute of Chengyi College, Jimei University","award":["3502Z202573102"],"award-info":[{"award-number":["3502Z202573102"]}]},{"name":"Big Data Technology Institute of Chengyi College, Jimei University","award":["CKZ24016"],"award-info":[{"award-number":["CKZ24016"]}]},{"name":"Big Data Technology Institute of Chengyi College, Jimei University","award":["CYKYPT02"],"award-info":[{"award-number":["CYKYPT02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) signals play a vital role in the diagnosis and analysis of epileptic seizures. However, traditional machine learning techniques often rely on handcrafted features, limiting their robustness and generalizability across diverse EEG acquisition settings, seizure types, and patients. To address these limitations, we propose RDPNet, a multi-scale residual dilated pyramid network with entropy-guided feature fusion for automated epileptic EEG classification. RDPNet combines residual convolution modules to extract local features and a dilated convolutional pyramid to capture long-range temporal dependencies. A dual-pathway fusion strategy integrates pooled and entropy-based features from both shallow and deep branches, enabling robust representation of spatial saliency and statistical complexity. We evaluate RDPNet on two benchmark datasets: the University of Bonn and TUSZ. On the Bonn dataset, RDPNet achieves 99.56\u2013100% accuracy in binary classification, 99.29\u201399.79% in ternary tasks, and 95.10% in five-class classification. On the clinically realistic TUSZ dataset, it reaches a weighted F1-score of 95.72% across seven seizure types. Compared with several baselines, RDPNet consistently outperforms existing approaches, demonstrating superior robustness, generalizability, and clinical potential for epileptic EEG analysis.<\/jats:p>","DOI":"10.3390\/e27080830","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T15:09:53Z","timestamp":1754492993000},"page":"830","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7407-8874","authenticated-orcid":false,"given":"Tongle","family":"Xie","sequence":"first","affiliation":[{"name":"Big Data Analytics Laboratory, Chengyi College, Jimei University, Xiamen 361021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6814-1526","authenticated-orcid":false,"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Big Data Analytics Laboratory, Chengyi College, Jimei University, Xiamen 361021, China"}]},{"given":"Yanyouyou","family":"Liu","sequence":"additional","affiliation":[{"name":"Big Data Analytics Laboratory, Chengyi College, Jimei University, Xiamen 361021, China"}]},{"given":"Shixiao","family":"Xiao","sequence":"additional","affiliation":[{"name":"Big Data Analytics Laboratory, Chengyi College, Jimei University, Xiamen 361021, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,5]]},"reference":[{"key":"ref_1","unstructured":"GBD Epilepsy Collaborators (2025). 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