ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2025-25-3-475-486ntv-471Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEГлубокое обучение для адаптивной аутентификации на основе электрокардиограммы в системе телемедицины с поддержкой интернета вещейA deep learning approach for adaptive electrocardiogram-based authentication in an internet of things enabled telehealth systemhttps://orcid.org/0009-0000-1748-0029АзабМ.А.Э.AzabM.A.E.

Азаб Мохамед Абдалла Эльсейд — аспирант

Санкт-Петербург, 197101

Mohamed Abdalla Elsayed Azab — PhD Student

Saint Petersburg, 197101

mohamed.a.azab@itmo.ruУниверситет ИТМОРоссияITMO UniversityRussian Federation202503072025253475486Copyright © Азаб М., 20252025Азаб М.Azab M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/471

Телемедицинские услуги стали неотъемлемой частью медицинских приложений, надежные механизмы аутентификации имеют решающее значение для защиты конфиденциальных данных пациентов и предоставляемых сервисов. Традиционные методы аутентификации, такие как пароли и токены, подвержены кражам и нарушениям безопасности. Эта уязвимость подчеркивает необходимость альтернативных методов, которые обеспечивают более высокий уровень безопасности и удобство использования. Биометрическая аутентификация, использующая уникальные физические и поведенческие характеристики, стала перспективной альтернативой. Среди различных биометрических методов сигналы электрокардиограммы (ЭКГ) выделяются своей уникальностью, стабильностью и неинвазивным характером. В данном исследовании представлен инновационный метод аутентификации на основе глубокого обучения, использующий ЭКГ-сигналы для повышения уровня безопасности в телемедицинских системах, работающих на базе интернета вещей (IoT). Предложенная модель использует гибридную архитектуру: сначала применяется сиамская нейронная сеть (SNN) для динамической верификации, затем сверточная нейронная сеть (CNN) для извлечения признаков с использованием оптимизированного метода последовательной агрегации сердечных циклов для надежной аутентификации на основе ЭКГ. Система функционирует безопасно и адаптивно, выполняя аутентификацию в реальном времени без вмешательства человека. В рамках исследования была проведена обработка данных ЭКГ из набора данных ECG-ID, включающего 310 ЭКГ-сигналов от 90 различных участников. Этот набор данных предоставил обширную выборку для обучения и оценки. Модель достигла высокой точности аутентификации (98,5–99,5 %) и показателя ложного допуска на уровне 0,1 % при минимальной вычислительной нагрузке, что подтверждает ее применимость для задач в реальном времени. Настоящее исследование интегрирует аутентификацию на основе ЭКГ в телемедицинские системы, создавая надежную основу для защиты данных пациентов. Инновационное использование ЭКГ-сигналов способствует созданию безопасной, адаптивной и персонализированной системы удаленного мониторинга здоровья.

As telehealth services have become integral to healthcare applications; robust authentication mechanisms are critical for safeguarding sensitive patient data and services. Conventional authentication techniques including passwords and tokens are susceptible to theft and security breaches. This vulnerability highlights the need for alternative methods that offer improved security measures and ease of use. Biometric authentication, which leverages unique physical and behavioral traits, has emerged as a promising alternative. Among various biometric modalities, electrocardiogram (ECG) signals stand out because of their uniqueness, stability, and noninvasive nature. This study introduces an innovative deeplearning-based authentication system that utilizes ECG signals to enhance security in Internet of Things (IoT)-powered telehealth environments. The proposed model employs hybrid architecture, starting with a Siamese Neural Network (SNN) for dynamic verification, followed by a Convolutional Neural Network (CNN) for feature extraction, utilizing an optimized Sequential Beat Aggregation approach for robust ECG-based authentication. The system operates securely and adaptively, and performs real-time authentication without requiring human intervention. The research approach involved the acquisition and processing of electrocardiogram data from the ECG-ID dataset which encompassed 310 ECG individuals obtained from 90 individual subjects. This dataset provided a comprehensive set of samples for training and evaluation. The model achieved high authentication accuracy (98.5 %–99.5 %) and a false acceptance rate of 0.1 % with minimal computational overhead, validating its feasibility for real-time applications. This study integrates ECGbased authentication into telehealth systems, creating a secure foundation for safeguarding patient data. The innovative use of ECG signals advances secure and adaptable for a personalized remote health monitoring system development.

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The authors declare that there are no conflicts of interest present.