Advancing Biomedical Signal Security: Real-Time ECG Monitoring with Chaotic Encryption

• URL: http://arxiv.org/abs/2411.01310v1
• Date: Sat, 02 Nov 2024 17:08:14 GMT
• Title: Advancing Biomedical Signal Security: Real-Time ECG Monitoring with Chaotic Encryption
• Authors: Beyazit Bestami Yuksel, Ayse Yilmazer Metin,
• Abstract summary: We develop a real time ECG monitoring system that integrates chaotic encryption to protect the integrity and confidentiality of ECG signals. Results demonstrate that chaotic encryption is effective in enhancing data security. System's real time disease detection model, based on deep learning, operates seamlessly with encrypted data, providing accurate diagnosis without compromising security.
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• Abstract: The real time analysis and secure transmission of electrocardiogram (ECG) signals are critical for ensuring both effective medical diagnosis and patient data privacy. In this study, we developed a real time ECG monitoring system that integrates chaotic encryption to protect the integrity and confidentiality of ECG signals during acquisition, transmission, and storage. By leveraging the logistic map as the chaotic function for encryption, our system offers a highly secure framework that dynamically encrypts ECG signals without adding significant latency. To validate the system's reliability, we applied a series of security tests. The results demonstrate that chaotic encryption is effective in enhancing data security, as evidenced by high entropy values and strong key sensitivity, ensuring protection against common cryptographic attacks. Additionally, the system's real time disease detection model, based on deep learning, operates seamlessly with encrypted data, providing accurate diagnosis without compromising security. Our findings indicate that chaotic encryption, paired with real time analysis, is a powerful method for protecting sensitive medical data, making this approach particularly relevant for telemedicine and remote patient monitoring applications. The success of this system highlights its potential for broader application to other biomedical signals, providing a secure infrastructure for the future of digital health.

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