Advances in ECG Digitization and Analysis Using AI, Image Processing, and Synthetic Data Generation

Electrocardiogram (ECG) digitization and intelligent analysis are crucial for updating cardiac diagnostics and enabling scalable, AI-driven healthcare solutions. This paper reviews recent advancements in ECG digitization, synthetic data creation, signal security, AI modelling, hardware optimization, and IoT-based real-time monitoring. Deep learning frameworks have shown high accuracy in converting paper-based ECGs into digital signals. They overcome challenges like image distortion, overlapping leads, and noise. To tackle the lack of annotated ECG image data, synthetic toolkits like ECG-Image-Kit and Gen-ECG have emerged. These produce large, clinically realistic datasets that significantly improve model training and generalization. In predictive modelling, AI frameworks, especially Multilayer Perceptron trained on PQRST signal segments, have been effective in estimating biological ECG age, which aids personalized risk assessment. Additionally, innovative watermarking strategies using Variational Autoencoders allow for secure, tamper resistant signal embedding without risking clinical integrity. At the system level, hardware acceleration through FPGA based FIR filters and IoT-integrated AI frameworks now supports real-time ECG monitoring in wearable and remote healthcare settings. Together, these interdisciplinary advancements represent a major shift in ECG data handling, diagnostics, and delivery, connecting legacy records with next-generation, connected, and intelligent cardiac care solutions. Key Words: Root Mean Squared Error (RMSE), Pearson correlation coefficients (PCC), PTB-XL digital ECG dataset, Multilayer Perceptron (MLP), Regions of Interest (ROI), Convolutional Neural Networks (CNNs), Mean Squared Error (MSE), Variational Autoencoder (VAE), Finite Impulse Response (FIR), FPGA (Field Programmable Gate Array).