Chaotic Image Encryption with Normal Sinus Rhythm ECG Signal and Chaoticity in ECG Signal

Abstract

In this study, the use of ECG data in the field of secure communication was examined by using a chaotic encryption method that has proven its reliability in the literature. ECG data, the chaoticity of which is a controversial issue in the literature, was used directly instead of chaotic number sequences in this method, and a security analysis was made over the NPCR, UACI, and entropy values of the encrypted images. While NPCR and UACI values indicate the security of the system against plaintext attacks by revealing the dissimilarity rate at the pixel level between two images encrypted with different keys, the entropy value indicates the encryption performance by giving information about how close the encrypted image is to the random appearance. In addition, phase portraits and Lyapunov exponents were examined, and the chaotic components in ECG were shown. According to the results, it has been observed that the sequence of numbers obtained by determining the person, the phase shift samples count, and the time of ECG taken can be used as the key with this method. In addition to the periodicity of healthy ECG data, the chaotic properties it contains have been shown to be sufficient for encryption applications.

Keywords

• ECG Signal
• Chaoticity
• Image Encryption
• Chaotic Encryption

Normal Sinüs Ritmi EKG Sinyali ile Kaotik Görüntü Şifreleme ve EKG Sinyalinde Kaotiklik

Abstract

Bu çalışmada literatürde güvenilirliği kanıtlanmış kaotik bir şifreleme yöntemi kullanılarak EKG verilerinin güvenli iletişim alanında kullanımı incelenmiştir. Kaotikliği literatürde tartışmalı bir konu olan EKG verileri bu yöntemde kaotik sayı dizileri yerine doğrudan kullanılmış ve şifrelenmiş görüntülerin NPCR, UACI ve entropi değerleri üzerinden güvenlik analizi yapılmıştır. NPCR ve UACI değerleri farklı anahtarlarla şifrelenmiş iki görüntü arasındaki piksel düzeyindeki farklılık oranını ortaya koyarak sistemin düz metin saldırılarına karşı güvenliğini gösterirken, entropi değeri ise şifrelenmiş görüntünün rastgele görünüme ne kadar yakın olduğu hakkında bilgi vererek şifreleme performansını göstermektedir. Ayrıca faz portreleri ve Lyapunov üstelleri incelenerek EKG'deki kaotik bileşenler gösterilmiştir. Elde edilen sonuçlara göre kişi, faz kayması örnek sayısı ve EKG çekim zamanı belirlenerek elde edilen sayı dizisinin bu yöntemle anahtar olarak kullanılabileceği görülmüştür. Sağlıklı EKG verilerinin periyodikliğinin yanı sıra içerdiği kaotik özelliklerin de şifreleme uygulamaları için yeterli olduğu gösterilmiştir.

Keywords

• EKG sinyali
• Kaotiklik
• Görüntü Şifreleme
• Kaotik Şifreleme

References

1. Glass, L. (2009). Introduction to controversial topics in nonlinear science: Is the normal heart rate chaotic?. Chaos: An Interdisciplinary Journal of Nonlinear Science, 19(2).
2. Gupta, V., Mittal, M., & Mittal, V. (2020). Chaos theory: an emerging tool for arrhythmia detection. Sensing and Imaging, 21(1), 10.
3. Gorshkov, O., & Ombao, H. (2021). Multi-chaotic analysis of inter-beat (RR) intervals in cardiac signals for discrimination between normal and pathological classes. Entropy, 23(1), 112.
4. Gupta, V., Mittal, M., & Mittal, V. (2019). R-peak detection using chaos analysis in standard and real time ECG databases. Irbm, 40(6), 341-354.
5. Gupta, V., & Mittal, M. (2019). QRS complex detection using STFT, chaos analysis, and PCA in standard and real-time ECG databases. Journal of The Institution of Engineers (India): Series B, 100(5), 489-497.
6. Casaleggio, A., & Braiotta, S. (1997). Estimation of Lyapunov exponents of ECG time series—the influence of parameters. Chaos, Solitons & Fractals, 8(10), 1591-1599.
7. Liu, Y., Tong, X., & Ma, J. (2016). Image encryption algorithm based on hyper-chaotic system and dynamic S-box. Multimedia Tools and Applications, 75, 7739-7759.
8. Akgül, A., Yıldız, M. Z., Boyraz, Ö. F., Güleryüz, E., Kaçar, S., & Gürevin, B. (2020). Doğrusal olmayan yeni bir sistem ile damar görüntülerinin mikrobilgisayar tabanlı olarak şifrelenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(3), 1369-1386.

9. Abundiz-Pérez, F., Cruz-Hernández, C., Murillo-Escobar, M. A., López-Gutiérrez, R. M., & Arellano-Delgado, A. (2016). A fingerprint image encryption scheme based on hyperchaotic Rössler map. Mathematical Problems in Engineering, 2016.
10. Murillo-Escobar, M. A., Cardoza-Avendaño, L., López-Gutiérrez, R. M., & Cruz-Hernández, C. (2017). A double chaotic layer encryption algorithm for clinical signals in telemedicine. Journal of medical systems, 41, 1-17.
11. Mathivanan, P., Ganesh, A. B., & Venkatesan, R. (2019). QR code–based ECG signal encryption/decryption algorithm. Cryptologia, 43(3), 233-253.
12. Algarni, A. D., Soliman, N. F., Abdallah, H. A., & Abd El-Samie, F. E. (2021). Encryption of ECG signals for telemedicine applications. Multimedia Tools and Applications, 80, 10679-10703.
13. Sufi, F., & Khalil, I. (2008). Enforcing secured ecg transmission for realtime telemonitoring: A joint encoding, compression, encryption mechanism. Security and Communication Networks, 1(5), 389-405.
14. Wang, H., Bai, T., Pang, Y., Wang, W., Lin, J., Li, G., ... & Jiang, X. (2018). The dynamic encryption method based on ecg characteristic value. In Communications, Signal Processing, and Systems: Proceedings of the 2016 International Conference on Communications, Signal Processing, and Systems (pp. 431-438). Springer Singapore.
15. Zheng, G., Fang, G., Shankaran, R., & Orgun, M. A. (2015). Encryption for implantable medical devices using modified one-time pads. IEEE Access, 3, 825-836.
16. Huang, P., Li, B., Guo, L., Jin, Z., & Chen, Y. (2016, December). A robust and reusable ecg-based authentication and data encryption scheme for ehealth systems. In 2016 IEEE global communications conference (GLOBECOM) (pp. 1-6). IEEE.
17. Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, 101(23), e215-e220.
18. Hossain, M. B., Rahman, M. T., Rahman, A. S., & Islam, S. (2014, May). A new approach of image encryption using 3D chaotic map to enhance security of multimedia component. In 2014 International Conference on Informatics, Electronics & Vision (ICIEV) (pp. 1-6). IEEE.
19. Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of atmospheric sciences, 20(2), 130-141.
20. Ye, G., Zhao, H., & Chai, H. (2016). Chaotic image encryption algorithm using wave-line permutation and block diffusion. Nonlinear Dynamics, 83, 2067-2077.
21. Loukhaoukha, K., Nabti, M., & Zebbiche, K. (2013, May). An efficient image encryption algorithm based on blocks permutation and Rubik's cube principle for iris images. In 2013 8th International workshop on systems, signal processing and their applications (WoSSPA) (pp. 267-272). IEEE.
22. Kumari, M., Gupta, S., & Sardana, P. (2017). A survey of image encryption algorithms. 3D Research, 8, 1-35.
23. Wolf, A., Swift, J. B., Swinney, H. L., & Vastano, J. A. (1985). Determining Lyapunov exponents from a time series. Physica D: nonlinear phenomena, 16(3), 285-317.
24. McClellan, J. H., & Parks, T. W. (2005). A personal history of the Parks-McClellan algorithm. IEEE signal processing magazine, 22(2), 82-86.
25. Stefanski, A., Dabrowski, A., & Kapitaniak, T. (2005). Evaluation of the largest Lyapunov exponent in dynamical systems with time delay. Chaos, Solitons & Fractals, 23(5), 1651-1659.