HEART: A High-Efficiency Adaptive Real-Time Telemonitoring Framework for Secure Electrocardiogram Signal Transmission Using Chaotic Encryption

Computer Science > Cryptography and Security [Submitted on 8 May 2026] HEART: A High-Efficiency Adaptive Real-Time Telemonitoring Framework for Secure Electrocardiogram Signal Transmission Using Chaotic Encryption Beyazıt Bestami Yuksel The realtime analysis and secure transmission of electrocardiogram ECG signals are critical for accurate diagnosis and safeguarding patient privacy in telemedicine applications This study presents a novel realtime ECG monitoring system that employs a learnable key generator LKG derived from each patients own ECG signal characteristics to dynamically produce unique encryption keys These keys determine the parameters r and x0 of a logistic map used for chaotic encryption The system securely encrypts realtime ECG data immediately after acquisition ensuring confidential transmission and storage in the cloud For remote clinical access the encrypted data is downloaded and decrypted on the doctors side using the matching key generated at the source or securely stored in the cloud This approach eliminates the need for traditional key exchange and substantially raises the cost of exhaustive key search in practice through persegment biometric key refresh and combined permutation and XOR diffusion supported by minentropy evaluation Compared to statickey methods the learnable biometric key design offers greater unpredictability and individualization A comprehensive set of security assessments including Shannon entropy 7678 bits correlation and autocorrelation disruption histogram statistics NIST SP 80022 frequency testing plaintextkey sensitivity avalanche effect FFTbased spectral flatness and robustness to noise and occlusion confirms the methods strength Reconstruction fidelity MSE approximately 5x106 PSNR greater than 52 dB MAE approximately 0002 demonstrates nearlossless decryption and preserved diagnostic features Encryption latency remains low preserving realtime performance. Comments: 15 pages, 4 figure, 3 table Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2605.08456 [cs.CR]   (or arXiv:2605.08456v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.08456 Focus to learn more Journal reference: ELECTRICA 2026 Related DOI: https://doi.org/10.5152/electrica.2026.25232 Focus to learn more Submission history From: Beyazit Bestami Yuksel [view email] [v1] Fri, 8 May 2026 20:25:53 UTC (1,384 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)