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.

翻译：心电图（ECG）信号的实时分析与安全传输对于确保有效的医疗诊断和患者数据隐私至关重要。本研究开发了一种集成混沌加密的实时心电图监测系统，以在采集、传输和存储过程中保护心电图信号的完整性和机密性。通过利用逻辑映射作为加密的混沌函数，我们的系统提供了一个高度安全的框架，能够动态加密心电图信号而不引入显著延迟。为验证系统的可靠性，我们进行了一系列安全测试。结果表明，混沌加密能有效增强数据安全性，这体现在高熵值和强密钥敏感性上，确保了对常见密码攻击的防护。此外，系统基于深度学习的实时疾病检测模型可在加密数据上无缝运行，在不影响安全性的前提下提供准确诊断。我们的研究结果表明，混沌加密与实时分析相结合是保护敏感医疗数据的有效方法，使该方法特别适用于远程医疗和远程患者监测应用。该系统的成功突显了其更广泛应用于其他生物医学信号的潜力，为数字健康的未来提供了安全的基础设施。