Vol.44, No.2, 2023, pp.1109-1123, doi:10.32604/csse.2023.025999
Secured ECG Signal Transmission Using Optimized EGC with Chaotic Neural Network in WBSN
  • Ishani Mishra1,*, Sanjay Jain2, Vivek Maik3
1 Department of ECE, New Horizon College of Engineering, Bengaluru, 560103, India
2 CMR Institute of Technology, Bengaluru, 560037, India
3 SRM University, Chennai, 603203, India
* Corresponding Author: Ishani Mishra. Email:
Received 12 December 2021; Accepted 24 January 2022; Issue published 15 June 2022
In wireless body sensor network (WBSN), the set of electrocardiogram (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. While transmitting these collected data some adversaries may capture and misuse it due to the compromise of security. So, the major aim of this work is to enhance secure transmission of ECG signal in WBSN. To attain this goal, we present Pity Beetle Swarm Optimization Algorithm (PBOA) based Elliptic Galois Cryptography (EGC) with Chaotic Neural Network. To optimize the key generation process in Elliptic Curve Cryptography (ECC) over Galois field or EGC, private key is chosen optimally using PBOA algorithm. Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data. Results of this work show that the proposed cryptography algorithm attains better encryption time, decryption time, throughput and SNR than the conventional cryptography algorithms.
Wireless body sensor network; ECG; pity beetle swarm optimization algorithm; elliptic galois cryptography and chaotic neural network
Cite This Article
I. Mishra, S. Jain and V. Maik, "Secured ecg signal transmission using optimized egc with chaotic neural network in wbsn," Computer Systems Science and Engineering, vol. 44, no.2, pp. 1109–1123, 2023.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.