{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T17:17:33Z","timestamp":1778087853006,"version":"3.51.4"},"reference-count":15,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Physiotherapy Res Intl"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Purpose<\/jats:title>\n                    <jats:p>Electroencephalogram (EEG) signals play a vital role in analyzing neurological activity and diagnosing various neurological disorders. With the rapid growth of the Internet of medical things (IoMT), EEG\u2010based diagnostic systems have become more interconnected and scalable. However, early and accurate diagnosis of neurological diseases remains challenging due to the intrinsic complexity, high dimensionality, and noise\u2010prone nature of EEG signals. This study aims to design an automated, robust, and intelligent diagnostic support framework for reliable early detection of neurological diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This paper proposes the design and development of an automated electroencephalogram signal processing and diagnostic support architecture employing advanced deep learning models (EEG\u2010DSS\u2010SMCNN). EEG signals were collected from Alzheimer's disease and Frontotemporal Dementia datasets. Initially, a Fast Desensitized Kalman Filter Algorithm (FDKFA) is applied for preprocessing to remove noise, artifacts, and signal distortions while ensuring data normalization. Subsequently, a Graph Fractional Fourier Transform (GFFTT) is employed to extract discriminative features from time, spatial, and channel domains. The extracted features are then fed into a Self\u2010Modulating Convolutional Neural Network (SMCNN) to perform automatic disease detection and classification. Furthermore, the network parameters were optimized using an elephant clan optimization (ECO) algorithm to enhance classification accuracy and model robustness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental evaluation demonstrates that the proposed EEG\u2010DSS\u2010SMCNN framework significantly outperforms existing state\u2010of\u2010the\u2010art methods. The proposed approach achieves accuracy improvements of 6.42%, 6.58%, and 8.27% compared to RT\u2010EEG\u2010DL, EEML\u2010DLT\u2010EEG, and EDDL\u2010ED\u2010EEG methods, respectively, confirming its superior diagnostic performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results highlight the effectiveness of integrating advanced signal preprocessing, graph\u2010based feature extraction, and deep learning within a unified diagnostic framework. The proposed EEG\u2010DSS\u2010SMCNN model efficiently handles noisy EEG signals and captures complex neurological patterns, making it suitable for early\u2010stage disease diagnosis in IoMT\u2010enabled healthcare environments. The optimized SMCNN further enhances classification reliability, suggesting strong potential for real\u2010time clinical decision support and scalable neurological disease monitoring systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pri.70204","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:51:38Z","timestamp":1774673498000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Design and Development of an Automated Electroencephalogram Signal Processing and Diagnostic Support Architecture Employing Advanced Deep Learning Models for Early Accurate and Scalable Detection of Neurological Diseases"],"prefix":"10.1002","volume":"31","author":[{"given":"Rajesh","family":"Thammuluri","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering Shri Vishnu Engineering College for Women (A)  Bhimavaram Andhra Pradesh India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajeshwari S.","family":"Mathad","sequence":"additional","affiliation":[{"name":"Bagalkot University Karnataka State Public University  Jamkhandi India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"CH. M. H.","family":"Saibaba","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engingeering Koneru Lakshmaiah Education Foundation  Guntur India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kancharla","family":"Nagababu","sequence":"additional","affiliation":[{"name":"Department of Computer Applications School of Engineering Aditya University  Surampalem Andhra Pradesh India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8541-3493","authenticated-orcid":false,"given":"Ashok","family":"Koujalagi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Shri Vishnu Engineering College for Women (A)  Bhimavaram Andhra Pradesh India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mukesh","family":"Soni","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Division of Research and Development Lovely Professional University  Phagwara India"},{"name":"Centre for Research Impact &amp; Outcome Chitkara University Institute of Engineering and Technology  Rajpura Punjab India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","DOI":"10.1109\/TSP.2024.3439211","article-title":"Graph Fractional Fourier Transform: A Unified Theory","author":"Alika\u015fifo\u011flu T.","year":"2024","journal-title":"IEEE Transactions on Signal Processing"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12035\u2010024\u201004222\u20104"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003479970-8"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00702\u2010023\u201002719\u20101"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10548\u2010025\u201001106\u20101"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/2631\u20108695\/ad9b00"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3532515"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207721.2024.2367079"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3436829"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11831\u2010023\u201010047\u20106"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2025.05.003"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122937"},{"key":"e_1_2_12_14_1","first-page":"204","volume-title":"Improving Cancer Classification Through an Deep Learning Framework Using Transfer Learning","author":"Subitha D.","year":"2025"},{"key":"e_1_2_12_15_1","first-page":"1","volume-title":"Performance Analysis of Machine Learning Algorithms for Covid\u201019 Detection Using Cough Analysis","author":"Subitha D.","year":"2024"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2023.109248"}],"container-title":["Physiotherapy Research International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pri.70204","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/pri.70204","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pri.70204","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T17:01:46Z","timestamp":1778086906000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/pri.70204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":15,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1002\/pri.70204"],"URL":"https:\/\/doi.org\/10.1002\/pri.70204","archive":["Portico"],"relation":{},"ISSN":["1358-2267","1471-2865"],"issn-type":[{"value":"1358-2267","type":"print"},{"value":"1471-2865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,27]]},"assertion":[{"value":"2025-12-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70204"}}