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Express"],"published-print":{"date-parts":[[2025,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This study details the development of a remote patient monitoring system with a primary focus on a novel, customized Deep Neural Network (DNN) for arrhythmia detection. The system integrates hardware for real-time data collection from biomedical sensors, where IoT-based sensor data is collected and encrypted in a central database for subsequent analysis. The novelty of the work lies in the proposed AI-based software component rather than the hardware assembly, which utilizes accessible components. The developed system is designed to function as a decision support system for healthcare personnel, providing necessary information and alerts through mobile and desktop interfaces. Data obtained from the patient is classified using the proposed deep learning method, and a detailed summary is presented. The customized DNN-based model demonstrated a test accuracy of 99.94%, with a recall of 99.92% and a precision of 99.57%, results which indicate a strong potential for clinical application due to very low false positive and false negative rates. Based on this high accuracy, the model\u2019s outputs have been integrated into user-friendly interfaces to assist healthcare personnel. It is therefore suggested that the patient monitoring system, featuring this high-performance classification model, has the potential to contribute to the early and more reliable detection of significant diseases such as heart abnormalities and arrhythmia.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae0f1f","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T22:47:36Z","timestamp":1759531656000},"page":"065011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Remote patient monitoring system combining hardware and artificial intelligence based software"],"prefix":"10.1088","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0490-0997","authenticated-orcid":true,"given":"Kaan","family":"K\u0131v\u0131rc\u0131k","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3790-7362","authenticated-orcid":false,"given":"Sibel","family":"\u00c7imen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3147-2018","authenticated-orcid":false,"given":"Nilay","family":"Bulduk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4732-9490","authenticated-orcid":true,"given":"Orhan","family":"Er","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5776-3947","authenticated-orcid":true,"given":"Mehmet","family":"Sagbas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"bpexae0f1fbib1","doi-asserted-by":"publisher","first-page":"9898","DOI":"10.1007\/s10916-012-9898-z","type":"journal-article","article-title":"Smart health monitoring systems: an overview of design and modeling","volume":"37","author":"Baig","year":"2013","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-05-13","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-10-03","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-10-14","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}