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Пульс безопасности: раскрытие потенциала биометрической аутентификации ЭКГ

Authors: Mohamed A. Azab; FGAOU VO \\"Natsional'nyi issledovatel'skii universitet ITMO\\"; Viktoriia M. Korzhuk; FGAOU VO \\"Natsional'nyi issledovatel'skii universitet ITMO\\";

Пульс безопасности: раскрытие потенциала биометрической аутентификации ЭКГ

Abstract

Телемедицина и удаленный мониторинг повышают доступность медицинской помощи, но также подвергают персональные данные угрозам безопасности и конфиденциальности, в то время как традиционные методы аутентификации часто оказываются несовершенными. Электрокардиограмма (ЭКГ) зарекомендовала себя как перспективный биометрический метод благодаря присущим ей скорости, универсальности и уникальности. В этой статье рассматривается канал аутентификации по ЭКГ, включая сбор сигнала с помощью переносных устройств, методы предварительной обработки для снижения шума, методы выделения признаков (фидуциарные, нефидуцированные и глубокое обучение) и модели классификации. Анализируются общедоступные наборы данных, а также ключевые критерии эффективности. Обсуждаются сохраняющиеся проблемы, такие как внутрипредметная вариативность, вычислительные ограничения, связанные с переносными устройствами, и этические соображения, при этом в перспективе выделяются мультимодальные системы, методы сохранения конфиденциальности и стандартизированная проверка. Аутентификация по ЭКГ демонстрирует большой потенциал для того, чтобы стать безопасным и практичным компонентом систем телемедицины.

Telehealth and remote monitoring improve healthcare accessibility but also expose sensitive data to security and privacy risks, where traditional authentication methods often prove inadequate. The electrocardiogram (ECG) has emerged as a promising biometric modality due to its inherent liveness detection, universality, and individual uniqueness. This paper reviews the ECG authentication pipeline, including signal acquisition through wearables, pre-processing techniques for noise reduction, feature extraction methods (fiducial, non-fiducial, and deep learning), and classification models. Publicly available datasets are examined, alongside key performance benchmarks. Persistent challenges-such as intra-subject variability, computational constraints in wearables, and ethical concerns are discussed, with future directions highlighting multimodal systems, privacy-preserving techniques, and standardized validation. ECG authentication demonstrates strong potential to become a secure and practical component of Telehealth systems.

Keywords

patient identification, идентификация пациента, биометрия ЭКГ, персонализация медицинских работников, ECG biometrics, обработка данных, биометрическая аутентификация, biometric authentication, healthcare personalization, telehealth security, signal processing, телемедицинская безопасность

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green