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Online Learning Koopman Operator for Closed-Loop Electrical Neurostimulation in Epilepsy
- Source :
- IEEE Journal of Biomedical and Health Informatics. 27:492-503
- Publication Year :
- 2023
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2023.
-
Abstract
- Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies.
- Subjects :
- Quantitative Biology::Neurons and Cognition
Health Information Management
FOS: Biological sciences
Quantitative Biology - Neurons and Cognition
FOS: Electrical engineering, electronic engineering, information engineering
Neurons and Cognition (q-bio.NC)
Health Informatics
Systems and Control (eess.SY)
Electrical and Electronic Engineering
Electrical Engineering and Systems Science - Systems and Control
Computer Science Applications
Subjects
Details
- ISSN :
- 21682208 and 21682194
- Volume :
- 27
- Database :
- OpenAIRE
- Journal :
- IEEE Journal of Biomedical and Health Informatics
- Accession number :
- edsair.doi.dedup.....8e43205a9d2ccb69dd0ae981c6710708