Enhancing the Reliability of Closed-Loop Medical Systems with Real-Time Biosignal Modeling - Mahmud, Shakil; Zareen, Farhath; Olney, Brooks; Karam, Robert (Journal of hardware and systems security, 2024)
Biosignal monitoring using wearable and implantable devices (WIMDs) is driving the advent of highly personalized medicine. However, such devices may suffer from the same faulty behavior as any electronic system and may furthermore be targeted by malicious actors seeking to do harm. Closed-loop medical control systems, which monitor biosignals for data acquisition, contain and interact with many other components, any of which may be maliciously targeted or suffer a naturally occurring fault. Any measure aiming to improve the security and reliability of these systems must also consider the interplay between each component. In this paper, we explore the vulnerability of closed-loop medical control systems considering both individual system components and the system as a whole and utilize a predictive model based on a nonlinear autoregressive neural network (NARNN) to detect and correct faulty behavior in real time. We present a case study using a human bladder pressure dataset from nine subjects undergoing acute urodynamics testing. Signals are corrupted to simulate faulty sensor readings or malicious attacks and then processed using a custom bladder event detection algorithm designed for use in a closed-loop neuromodulation system. Using the proposed technique, 100% of faulty measurements were detected and corrected, so the control algorithm induced no additional false positives. We present the circuit-level implementation of the NARNN suitable for on-chip machine learning (ML)/artificial intelligence (AI) applications. We synthesized and generated the layout of the NARNN architecture in SAED 32 nm technology. The implementation requires an area of 0.022 m m 2 and a total power consumption of 0.31 mW , which is suitable for WIMDs.