Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Journal of Hardware and Systems Security

04.01.2024

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

verfasst von: Shakil Mahmud, Farhath Zareen, Brooks Olney, Robert Karam

Erschienen in: Journal of Hardware and Systems Security

Einloggen, um Zugang zu erhalten

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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 \(0.022~mm^2\) and a total power consumption of 0.31 mW, which is suitable for WIMDs.
Literatur
1.
Khan W, Muntimadugu E, Jaffe M, Domb AJ (2014) Implantable medical devices. In: Focal controlled drug delivery, pp. 33–59. Springer
2.
3.
Merchant FM, Levy WC, Kramer DB (2020) Time to shock the system: moving beyond the current paradigm for primary prevention implantable cardioverter-defibrillator use. J Am Heart Assoc 9(5):e015139
4.
Middour TG, Chen JH, El-Chami MF (2021) Leadless pacemakers: a review of current data and future directions. Prog Cardiovasc Dis 66:61–69CrossRef
5.
Burleson W, Clark SS, Ransford B, Fu K (2012) Design challenges for secure implantable medical devices. In: DAC Design Automation Conference 2012, pp. 12–17. IEEE
6.
Camara C, Peris-Lopez P, Tapiador JE (2015) Security and privacy issues in implantable medical devices: a comprehensive survey. J Biomed Inform 55:272–289CrossRef
7.
Halperin D, Heydt-Benjamin TS, Ransford B, Clark SS, Defend B, Morgan W, Fu K, Kohno T, Maisel WH (2008) Pacemakers and implantable cardiac defibrillators: software radio attacks and zero-power defenses. In: 2008 IEEE Symposium on Security and Privacy (sp 2008), pp. 129–142. IEEE
8.
Hei X, Du X, Wu J, Hu F (2010) Defending resource depletion attacks on implantable medical devices. In: 2010 IEEE global telecommunications conference GLOBECOM 2010, pp. 1–5. IEEE
9.
Marin E, Singelée D, Garcia FD, Chothia T, Willems R, Preneel B (2016) On the (in) security of the latest generation implantable cardiac defibrillators and how to secure them. In: Proceedings of the 32nd annual conference on computer security applications, pp. 226–236
10.
Marin E, Singelée D, Yang B, Volski V, Vandenbosch GA, Nuttin B, Preneel B (2018) Securing wireless neurostimulators. In: Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy, pp. 287–298
11.
Pycroft L, Aziz TZ (2018) Security of implantable medical devices with wireless connections: the dangers of cyber-attacks. Expert Rev Med Devices 15(6):403–406CrossRef
12.
Raturi R, Sargsyan H (2018) A nonlinear autoregressive scheme for time series prediction via artificial neural networks. J Comput Commun 6(9):14–23CrossRef
13.
Le VM, Pham BT, Le TT, Ly HB, Le LM (2020) Daily rainfall prediction using nonlinear autoregressive neural network. Micro-Electronics and Telecommunication Engineering pp. 213–221
14.
Benrhmach G, Namir K, Namir A, Bouyaghroumni J (2020) Nonlinear autoregressive neural network and extended Kalman filters for prediction of financial time series. J Appl Math 2020
15.
Ruiz LGB, Cuéllar MP, Calvo-Flores MD, Jiménez MDCP (2016) An application of non-linear autoregressive neural networks to predict energy consumption in public buildings. Energies 9(9):684CrossRef
16.
Hamdi T, Ali JB, Fnaiech N, Di Costanzo V, Fnaiech F, Moreau E, Ginoux JM (2017) Artificial neural network for blood glucose level prediction. In: 2017 International Conference on Smart, Monitored and Controlled Cities (SM2C), pp. 91–95. IEEE
17.
Olney B, Mahmud S, Karam R (2022) Efficient nonlinear autoregressive neural network architecture for real-time biomedical applications. In: 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp. 411–414. IEEE
18.
Karam R, Bhunia S, Majerus S, Brose SW, Damaser MS, Bourbeau D (2016) Real-time, autonomous bladder event classification and closed-loop control from single-channel pressure data. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5789–5792. IEEE
19.
Karam R, Bourbeau D, Majerus S, Makovey I, Goldman HB, Damaser MS, Bhunia S (2015) Real-time classification of bladder events for effective diagnosis and treatment of urinary incontinence. IEEE Trans Biomed Eng 63(4):721–729
20.
Boughton CK, Hovorka R (2021) New closed-loop insulin systems. Diabetologia pp. 1–9
21.
Dumont GA, Ansermino JM (2013) Closed-loop control of anesthesia: a primer for anesthesiologists. Anesth Analg 117(5):1130–1138CrossRef
22.
Eberle W, Penders J, Yazicioglu RF (2011) Closing the loop for deep brain stimulation implants enables personalized healthcare for Parkinson’s disease patients. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1556–1558. IEEE
23.
Iqbal N, Ahmad S, Kim DH et al (2021) Health monitoring system for elderly patients using intelligent task mapping mechanism in closed loop healthcare environment. Symmetry 13(2):357CrossRef
24.
Lawton J, Kimbell B, Rankin D, Ashcroft N, Varghese L, Allen J, Boughton C, Campbell F, Randell T, Besser R et al (2020) Health professionals’ views about who would benefit from using a closed-loop system: a qualitative study. Diabet Med 37(6):1030–1037CrossRef
25.
Mahmud S, Zareen F, Olney B, Karam R et al (2022) Trojan resilience in implantable and wearable medical devices with virtual biosensing. In: 2022 IEEE 40th International Conference on Computer Design (ICCD), pp. 577–584. IEEE
26.
Boughton CK, Hovorka R (2019) Advances in artificial pancreas systems. Sci Transl Med 11(484)
27.
Kovatchev B (2018) Automated closed-loop control of diabetes: the artificial pancreas. Bioelectron Med 4(1):1–12CrossRef
28.
29.
Abdar M, Zomorodi-Moghadam M, Das R, Ting IH (2017) Performance analysis of classification algorithms on early detection of liver disease. Expert Syst Appl 67:239–251CrossRef
30.
Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879CrossRef
31.
Li K, Daniels J, Liu C, Herrero P, Georgiou P (2019) Convolutional recurrent neural networks for glucose prediction. IEEE J Biomed Health Inform 24(2):603–613CrossRef
32.
Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554CrossRef
33.
Dave D, DeSalvo DJ, Haridas B, McKay S, Shenoy A, Koh CJ, Lawley M, Erraguntla M (2021) Feature-based machine learning model for real-time hypoglycemia prediction. J Diabetes Sci Technol 15(4):842–855CrossRef
34.
Yadav SS, Jadhav SM (2021) Detection of common risk factors for diagnosis of cardiac arrhythmia using machine learning algorithm. Expert Syst Appl 163:113807CrossRef
35.
Bahrami M, Forouzanfar M (2022) Sleep apnea detection from single-lead ECG: a comprehensive analysis of machine learning and deep learning algorithms. IEEE Trans Instrum Meas 71:1–11
36.
Wang A, Wang J, Lin H, Zhang J, Yang Z, Xu K (2016) Biomedical event extraction based on distributed representation and deep learning. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 775. IEEE
37.
Olney B, Mahmud S, Zaman MA, Karam R (2022) An EDA framework for design space exploration of on-chip AI in bioimplantable applications. In: 2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1–4. IEEE
38.
Ghazaly NM, Abdel-Fattah MA, El-Aziz A et al (2020) Novel coronavirus forecasting model using nonlinear autoregressive artificial neural network. Int J Adv Sci Technol 29(5):19
39.
Khan FM, Gupta R (2020) ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India. Journal of Safety Science and Resilience 1(1):12–18CrossRef
40.
Wang K, Deng C, Li J, Zhang Y, Li X, Wu M (2017) Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiol Infect 145(6):1118–1129CrossRef
41.
Asad M, Qamar U, Abbas M (2021) Blood glucose level prediction of diabetic type 1 patients using nonlinear autoregressive neural networks. J Healthc Eng 2021
42.
Ali JB, Hamdi T, Fnaiech N, Di Costanzo V, Fnaiech F, Ginoux JM (2018) Continuous blood glucose level prediction of type 1 diabetes based on artificial neural network. Bioprocess Biosyst Eng 38(4):828–840
43.
Mahmud S, Keller M, Ahmed S, Karam R (2023) FAMID: false alarms mitigation in IoMT devices. In: IFIP International Internet of Things Conference, pp. 199–217. Springer
44.
Herrero R, St-Pierre C (2017) Dynamic forward error correction in wireless real-time internet of things networks. IET Networks 6(6):218–223CrossRef
45.
Rakovic V, Adamovski R, Risteski A, Gavrilovska L (2020) Improving energy efficiency and reliability in WuR-based IoT systems: an error correction approach. Wirel Pers Commun pp. 1–12
46.
de Oliveira JFL, Pacífico LDS, de Mattos Neto PSG, Barreiros, EFS, de Oliveira Rodrigues CM, de Almeida Filho AT (2020) A hybrid optimized error correction system for time series forecasting. Appl Soft Comput 87:105970
47.
Duan J, Zuo H, Bai Y, Duan J, Chang M, Chen B (2021) Short-term wind speed forecasting using recurrent neural networks with error correction. Energy 217:119397
48.
Hirai Y, Matsuoka T, Tani S, Isami S, Tatsumi K, Ueda M, Kamata T (2019) A biomedical sensor system with stochastic A/D conversion and error correction by machine learning. IEEE Access 7:21990–22001CrossRef
49.
Rahman MS, Khalil I, Yi X (2020) Reversible biosignal steganography approach for authenticating biosignals using extended binary Golay code. IEEE J Biomed Health Inform 25(1):35–46CrossRef
50.
51.
Food U, Administration D et al (2017) Cfr-code of federal regulations title 21
52.
53.
Li C, Raghunathan A, Jha NK (2011) Hijacking an insulin pump: security attacks and defenses for a diabetes therapy system. In: 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, pp. 150–156. IEEE
54.
Pycroft L, Boccard SG, Owen SL, Stein JF, Fitzgerald JJ, Green AL, Aziz TZ (2016) Brainjacking: implant security issues in invasive neuromodulation. World Neurosurg 92:454–462CrossRef
55.
Rushanan M, Rubin AD, Kune DF, Swanson CM (2014) SoK: security and privacy in implantable medical devices and body area networks. In: 2014 IEEE symposium on security and privacy, pp. 524–539. IEEE
56.
Gavin HP (2019) The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems. Department of Civil and Environmental Engineering, Duke University pp, pp 1–19
57.
58.
Xu Q, Arafin MT, Qu G (2021) Security of neural networks from hardware perspective: a survey and beyond. In: Proceedings of the 26th Asia and South Pacific Design Automation Conference, pp. 449–454
59.
60.
Liu Z, Ye J, Hu X, Li H, Li X, Hu Y (2020) Sequence triggered hardware Trojan in neural network accelerator. In: 2020 IEEE 38th VLSI Test Symposium (VTS), pp. 1–6. IEEE
Metadaten
Titel
Enhancing the Reliability of Closed-Loop Medical Systems with Real-Time Biosignal Modeling
verfasst von
Shakil Mahmud
Farhath Zareen
Brooks Olney
Robert Karam
Publikationsdatum
04.01.2024
Verlag
Springer International Publishing
Erschienen in
Journal of Hardware and Systems Security
Print ISSN: 2509-3428
Elektronische ISSN: 2509-3436
DOI
https://doi.org/10.1007/s41635-023-00140-4