Remote patient monitoring system combining hardware and artificial intelligence based software

Kıvırcık K., ÇİMEN S., Bulduk N., Er O., Sagbas M.

Biomedical Physics and Engineering Express, cilt.11, sa.6, 2025 (ESCI, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Sayı: 6
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1088/2057-1976/ae0f1f
  • Dergi Adı: Biomedical Physics and Engineering Express
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Compendex, EMBASE, INSPEC, MEDLINE
  • Anahtar Kelimeler: AI based systems, internet of things, remote patient monitoring, wearable technologies
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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’s 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.