AI-Driven Functional Safety Framework for Real-Time Biomedical Signal Processing Systems

Md. Abdul Kadir, Mossadaque Azmain Awon — Wed, 15 Jul 2026 00:00:00 +0000

This work investigates a software functional safety framework for biomedical signal processing using advanced deep learning techniques to achieve reliable, accurate, and low-latency operation in real-time healthcare monitoring systems. Functional safety is a critical requirement in biomedical sensor applications, where incorrect signal interpretation or delayed responses may compromise patient safety. The proposed framework integrates safety-aware signal processing with deep learning-based classification to enhance both performance and dependability. Experiments were conducted using TensorFlow 2.15 on an Intel i9 processor with an NVIDIA RTX 4090 GPU under Ubuntu 22.04. Biomedical signals were sampled at 500 Hz and evaluated using Accuracy, Latency, and F1-score metrics. The proposed method was compared against conventional Digital Signal Processing (DSP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) approaches. Experimental results demonstrate that the proposed framework achieves the highest classification accuracy, outperforming DSP (88.4%), CNN (93.2%), and LSTM (94.1%). Furthermore, the proposed approach exhibits the lowest processing latency of 6 ms, compared with 28 ms, 18 ms, and 12 ms for DSP, CNN, and LSTM methods, respectively. These improvements indicate that the framework effectively balances computational efficiency and predictive performance while satisfying real-time operational requirements. The incorporation of functional safety principles enhances system robustness by reducing the likelihood of hazardous failures and improving confidence in decision-making processes. The findings highlight the suitability of the proposed framework for deployment in safety-critical biomedical applications, including patient monitoring, wearable healthcare devices, and intelligent diagnostic systems. Overall, the study demonstrates that combining software functional safety mechanisms with deep learning can significantly improve the reliability, responsiveness, and accuracy of biomedical signal processing systems.

International Journal of Neural Systems and Applications

AI-Driven Functional Safety Framework for Real-Time Biomedical Signal Processing Systems