AI-driven hybrid neural network for electrocardiogram-based authentication and predictive health monitoring

Swati Lakshmi Boppana (Prasad V. Potluri Siddhartha Institute of Technology)
Velicheti Anantha Lakshmi (Pragati Engineering College)
Padala SriKavitha (Aditya University)
Venkata Ashok Kalaga (Lakireddy Bali Reddy College of Engineering)
Venkateswara Rao Naramala (R.V.R. and J.C. College of Engineering)
Suneetha Thalluru (R.V.R. and J.C. College of Engineering)
Gunturi S. Raghavendra (Koneru Lakshmaiah Education Foundation)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 15, No 4: August 2026

The increasing adoption of digital healthcare systems demands secure and reliable patient authentication mechanisms. Traditional methods such as passwords and PINs are vulnerable to security breaches, motivating the use of biometric-based solutions. Among various biometrics, the electrocardiogram (ECG) signal is distinctive, stable, and non-invasive, making it suitable for secure authentication. This paper proposes CardioGuard, an artificial intelligence (AI)–based authentication framework that employs a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to extract discriminative features from ECG signals and classify users as genuine or impostors. In addition to access control, the system analyzes ECG patterns to support early detection of potential cardiovascular abnormalities. Experimental results demonstrate that CardioGuard achieves improved authentication accuracy and enhanced predictive health insights compared to conventional approaches, highlighting its effectiveness for secure and intelligent healthcare monitoring.