Seizure Control by a Learning Type Active Disturbance Rejection Approach
Epilepsy is one of the most common neurological disorders. Neuro-modulation becomes a promising way to address it. For an effective modulation, closed-loop mode is necessary but difficult. A control algorithm, which can adjust itself to get desired suppression of epileptic activity, is in great need...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8879550/ |
| _version_ | 1818349363558088704 |
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| author | Wei Wei Xiaofang Wei Pengfei Xia Min Zuo Dong Shen |
| author_facet | Wei Wei Xiaofang Wei Pengfei Xia Min Zuo Dong Shen |
| author_sort | Wei Wei |
| collection | DOAJ |
| description | Epilepsy is one of the most common neurological disorders. Neuro-modulation becomes a promising way to address it. For an effective modulation, closed-loop mode is necessary but difficult. A control algorithm, which can adjust itself to get desired suppression of epileptic activity, is in great need. In this paper, active disturbance rejection control (ADRC) is utilized for its satisfied disturbance rejection and regulation performance. However, fixed observer parameters are difficult to fit the time-varying electrophysiological signals. Therefore, based on the estimation errors, an iterative learning approach is designed to get the parameters of an extended state observer (ESO). By combining the advantages of ADRC and the iterative learning, a learning type ADRC (LTADRC) is proposed to suppress the high amplitude epileptiform waves generated by the Jansen's neural mass model (NMM). For those variable parameters of an ESO, scalable bandwidths can be obtained to adapt to time-varying disturbance signals. It is of great significance for both ADRC and the neuro-modulation of epilepsy. Simulation results show that, compared with ADRC, much better performance can be obtained. It may provide a promising closed-loop regulation way for epilepsy in clinics. |
| first_indexed | 2024-12-13T18:04:46Z |
| format | Article |
| id | doaj.art-edaf282e2fa3490eb50a1e971ba50624 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:04:46Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-edaf282e2fa3490eb50a1e971ba506242022-12-21T23:36:06ZengIEEEIEEE Access2169-35362019-01-01716479216480210.1109/ACCESS.2019.29489438879550Seizure Control by a Learning Type Active Disturbance Rejection ApproachWei Wei0https://orcid.org/0000-0001-6059-884XXiaofang Wei1Pengfei Xia2Min Zuo3Dong Shen4https://orcid.org/0000-0003-1063-1351School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaEpilepsy is one of the most common neurological disorders. Neuro-modulation becomes a promising way to address it. For an effective modulation, closed-loop mode is necessary but difficult. A control algorithm, which can adjust itself to get desired suppression of epileptic activity, is in great need. In this paper, active disturbance rejection control (ADRC) is utilized for its satisfied disturbance rejection and regulation performance. However, fixed observer parameters are difficult to fit the time-varying electrophysiological signals. Therefore, based on the estimation errors, an iterative learning approach is designed to get the parameters of an extended state observer (ESO). By combining the advantages of ADRC and the iterative learning, a learning type ADRC (LTADRC) is proposed to suppress the high amplitude epileptiform waves generated by the Jansen's neural mass model (NMM). For those variable parameters of an ESO, scalable bandwidths can be obtained to adapt to time-varying disturbance signals. It is of great significance for both ADRC and the neuro-modulation of epilepsy. Simulation results show that, compared with ADRC, much better performance can be obtained. It may provide a promising closed-loop regulation way for epilepsy in clinics.https://ieeexplore.ieee.org/document/8879550/ADRCa learning type ADRCepilepsyNMMclosed-loop modulation |
| spellingShingle | Wei Wei Xiaofang Wei Pengfei Xia Min Zuo Dong Shen Seizure Control by a Learning Type Active Disturbance Rejection Approach IEEE Access ADRC a learning type ADRC epilepsy NMM closed-loop modulation |
| title | Seizure Control by a Learning Type Active Disturbance Rejection Approach |
| title_full | Seizure Control by a Learning Type Active Disturbance Rejection Approach |
| title_fullStr | Seizure Control by a Learning Type Active Disturbance Rejection Approach |
| title_full_unstemmed | Seizure Control by a Learning Type Active Disturbance Rejection Approach |
| title_short | Seizure Control by a Learning Type Active Disturbance Rejection Approach |
| title_sort | seizure control by a learning type active disturbance rejection approach |
| topic | ADRC a learning type ADRC epilepsy NMM closed-loop modulation |
| url | https://ieeexplore.ieee.org/document/8879550/ |
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