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

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.