AI-Driven Functional Safety Framework for Real-Time Biomedical Signal Processing Systems
Keywords:
Adaptive risk management, Artificial intelligence, Biomedical signal processing, ECG monitoring, Fault detection, Functional safety, Healthcare IoT, Machine learning, Real-time systems, Safety-critical systemsAbstract
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.
References
A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, Jun. 2000.
Z. Obermeyer and E. J. Emanuel, “Predicting the future—Big data, machine learning, and clinical Medicine,” The New England Journal of Medicine, vol. 375, no. 13, 2016.
D. Shen, G. Wu, and H. Suk, “Deep learning in medical image analysis,” Annual Review of Biomedical Engineering, vol. 19, pp. 221–248, Jun. 2017.
S. G. Odaibo, “Risk management of AI/ML software as a medical device (SaMD): On ISO 14971 and related standards and guidances,” 2021.
L. Chadwick, E. F. Fallon, W. J. van der Putten, and F. Kirrane, “Functional safety of health information technology,” Health Informatics Journal, vol. 18, no. 1, pp. 36–49, 2012.
S. Dean, “IEC 61508—Understanding functional safety assessment,” Measurement and Control, vol. 32, no. 7, pp. 201–204, Sept. 1999.
ISO, ISO 14971:2019 Medical Devices—Application of Risk Management to Medical Devices, Geneva, Switzerland, 2019.
J. Hunte, M. Neil, and N. E. Fenton, “A hybrid Bayesian network for medical device risk assessment and management,” Reliability Engineering & System Safety, vol. 241, Jan. 2022.
N. Johnson, Y. Gheraibia, and T. Kelly, “Independent co-assurance using the safety-security assurance framework,” arXiv, 2020.
S. K. Kim, C. Y. Yeun, P. D. Yoo, N. W. Lo, and E. Damiani, “Deep learning-based arrhythmia detection using RR-interval framed electrocardiograms,” Proceedings of Eighth International Congress on Information and Communication Technology, 2020, pp. 11–21.
A. Ayyub, C. Politis and M. A. Usman, “A comprehensive review of AI-based detection of arrhythmia using electrocardiogram (ECG),” Computers in Biology and Medicine, vol. 196, Sept. 2025.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017.
A. Vaswani et al., “Attention is all you need,” in Proceedings of NIPS, Jun. 2017.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” International Conference on Learning Representations, 2015.
