Enhancing the Reliability of Closed-Loop Medical Systems with Real-Time Biosignal Modeling

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 medic...

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Vydané v:	Journal of hardware and systems security Ročník 8; číslo 1; s. 12 - 24
Hlavní autori:	Mahmud, Shakil, Zareen, Farhath, Olney, Brooks, Karam, Robert
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.03.2024 Springer Nature B.V
Predmet:	Algorithms Artificial intelligence Biosensors Bladder Circuits and Systems Closed loop systems Closed loops Component reliability Computer Hardware Control systems Control theory Controllers Data acquisition Electronic systems Energy consumption Engineering Error correction & detection Glucose Information Systems Applications (incl.Internet) Machine learning Medical equipment Neural networks Nonlinear control Nonlinear systems Pacemakers Pancreas Patients Physiology Prediction models Predictive control Quality of life Real time Sensors System reliability Systems and Data Security Target acquisition Transplants & implants Urogenital system Wearable computers Biosignal Closed-loop system Neural network Implantable medical device Nonlinear autoregressive
ISSN:	2509-3428, 2509-3436
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Abstract	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.
AbstractList	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 0.022mm2 and a total power consumption of 0.31 mW, which is suitable for WIMDs. 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.
Author	Mahmud, Shakil Zareen, Farhath Karam, Robert Olney, Brooks
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SubjectTerms	Algorithms Artificial intelligence Biosensors Bladder Circuits and Systems Closed loop systems Closed loops Component reliability Computer Hardware Control systems Control theory Controllers Data acquisition Electronic systems Energy consumption Engineering Error correction & detection Glucose Information Systems Applications (incl.Internet) Machine learning Medical equipment Neural networks Nonlinear control Nonlinear systems Pacemakers Pancreas Patients Physiology Prediction models Predictive control Quality of life Real time Sensors System reliability Systems and Data Security Target acquisition Transplants & implants Urogenital system Wearable computers
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Title	Enhancing the Reliability of Closed-Loop Medical Systems with Real-Time Biosignal Modeling
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