Advanced chaotic wavelet encryption for simultaneous securing multi-lead ECG and EEG physiological signals in IoMT applications

• Published in: Physica scripta Vol. 100; no. 7; pp. 75255 - 75279
• Main Authors: Adélaïde Nicole, Kengnou Telem, Tabekoueng Njitacke, Zeric, Sone, Michael Ekonde, Kengne, Jacques
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.07.2025
• Subjects: chaotic map; discrete wavelet transform; internet of medical things; multi-lead ECG/EEG encryption; security analysis; telemedicine
• DOI: 10.1088/1402-4896/ade5d3
• ISSN: 0031-8949

Secure and efficient transmission of sensitive physiological signals is crucial in telemedicine and the Internet of Medical Things (IoMT). This paper presents the Chaotic Wavelet-based Encryption Scheme (CHWES) for simultaneously securing all the 12 leads of the electrocardiogram (ECG) or all the 21 leads of the electroencephalogram (EEG). The CHWES algorithm uses both a 2D Discrete Wavelet Transform (2D-DWT) and a logistic map to make it more efficient and secure against cryptography attacks. A dual-key mechanism has been implemented, consisting of an external 32-character hexadecimal key and an internal key derived from the signal itself, ensuring high sensitivity to small variations either on the original signal or in the encryption/decryption key. Initially, the permutation is applied to each lead individually using a chaotic sequence generated by the logistic map. After permutation, the signals are represented as 2D matrices and decomposed into sub-bands using 2D-DWT, followed by chaotic permutation and substitution using logistic map-generated sequences. The processed signal is then reconstructed, subdivided into non-overlapping square sub-blocks, and subjected to chaotic substitution operations using different sequences from one sub-block to another. The final encryption stage involves a global chaotic permutation step, which serves to further strengthen security. The efficacy of the CHWES is substantiated by a comprehensive performance evaluation on 250 ECG and 250 EEG signals, which manifests in low correlation (∼10 −3 ), high entropy (7.99), NPCR (99.964%), UACI (33.5%), MSE (10713.59), PSNR (7.8315), and SSIM (0.208272). These findings substantiate the algorithm’s resilience to cyberattacks, its high efficiency, and its enhancement of security measures. The CHWES signifies a substantial advancement in the realm of secure telemedicine, ensuring the protection of patient privacy in remote monitoring and diagnosis within the context of IoMT systems.