{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:07:03Z","timestamp":1789103223333,"version":"build-2803163510"},"reference-count":60,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009096","name":"Firat University","doi-asserted-by":"crossref","award":["TEKF.24.50"],"award-info":[{"award-number":["TEKF.24.50"]}],"id":[{"id":"10.13039\/100009096","id-type":"DOI","asserted-by":"crossref"}]},{"award":["TEKF.24.50"],"award-info":[{"award-number":["TEKF.24.50"]}],"id":[{"id":"https:\/\/ror.org\/05teb7b63","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Early detection of dementia is essential for timely intervention, yet existing diagnostic approaches remain costly, invasive, or dependent on specialized expertise. Electroencephalography (EEG) offers a non-invasive and accessible alternative; however, purely data-driven deep learning models may overlook clinically established neurophysiological biomarkers, particularly in the challenging detection of mild cognitive impairment (MCI). Methods: We propose the Clinical EEG Feature-Augmented Network (CEFA-Net), a knowledge-guided deep learning framework that systematically integrates automatic representation learning from raw multichannel EEG with clinically validated neurophysiological biomarkers. The architecture combines three complementary convolutional pathways capturing multi-scale temporal dynamics with domain-informed feature representations, enabling both data-driven discovery and clinically grounded interpretation. Task-specific optimization strategies\u2014including focal loss, class-aware augmentation, and validation-guided ensemble weighting\u2014were employed to enhance robustness under class imbalance. The model was evaluated on the large-scale the Chung-Ang University Hospital EEG (CAUEEG) dataset (1379 recordings from 1155 patients) across binary abnormality detection and three-class dementia staging tasks. Results: CEFA-Net achieved 81.02% accuracy (macro F1: 81.15%) for dementia staging and 87.15% accuracy (macro F1: 87.41%) for abnormality detection, outperforming baseline methods by 6.75\u20139.10 percentage points (p &lt; 0.001). Notably, the proposed framework substantially improved MCI detection (F1-score: 78%), representing a 14-point gain over traditional machine learning approaches. Ablation analyses confirmed that clinical biomarker integration and multi-model fusion provide complementary diagnostic value. In an additional patient-disjoint evaluation using the no-overlap partitions, CEFA-Net achieved 85.40% accuracy for abnormality detection and 73.80% accuracy for dementia staging, demonstrating generalization to subjects completely excluded from the training data. Conclusions: These findings demonstrate that knowledge-guided integration of clinical biomarkers with deep representation learning can significantly enhance EEG-based dementia detection. CEFA-Net offers a clinically aligned and computationally efficient solution, supporting its potential for real-world screening and early diagnostic workflows.<\/jats:p>","DOI":"10.3390\/diagnostics16182912","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:26:11Z","timestamp":1788956771000},"page":"2912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge-Guided Deep Learning with Clinical EEG Biomarkers for Automated Dementia Detection and Staging"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5788-5629","authenticated-orcid":false,"given":"Nebras","family":"Sobahi","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7929-7542","authenticated-orcid":false,"given":"Salih Taha Alperen","family":"\u00d6z\u00e7elik","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering, Bing\u00f6l University, Bingol 12100, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1614-2639","authenticated-orcid":false,"given":"Abdulkadir","family":"\u015eeng\u00fcr","sequence":"additional","affiliation":[{"name":"Department of Electrical-Electronics Engineering, Faculty of Technology, Firat University, Elazig 23100, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0491-8348","authenticated-orcid":false,"given":"Hanifi","family":"G\u00fcldemir","sequence":"additional","affiliation":[{"name":"Department of Electrical-Electronics Engineering, Faculty of Technology, Firat University, Elazig 23100, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1038\/s41598-025-31613-x","article-title":"Clinical Validation of New Alzheimer Disease Diagnosis Tools Based on Plasma P-Tau217","volume":"16","author":"Balaguer","year":"2025","journal-title":"Sci. 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