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Wang LH, Zhang ZN, Xie CX, Jiang H, Yang T, Ran QP, Fan MH, Kuo IC, Lee ZJ, Chen JB, Chen TY, Chen SL, Abu PAR. A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System. SENSORS (BASEL, SWITZERLAND) 2024; 25:33. [PMID: 39796823 PMCID: PMC11723055 DOI: 10.3390/s25010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2024] [Revised: 12/18/2024] [Accepted: 12/23/2024] [Indexed: 01/13/2025]
Abstract
Epilepsy, as a common brain disease, causes great pain and stress to patients around the world. At present, the main treatment methods are drug, surgical, and electrical stimulation therapies. Electrical stimulation has recently emerged as an alternative treatment for reducing symptomatic seizures. This study proposes a novel closed-loop epilepsy detection system and stimulation control chip. A time-domain detection algorithm based on amplitude, slope, line length, and signal energy characteristics is introduced. A new threshold calculation method is proposed; that is, the threshold is updated by means of the mean and standard deviation of four consecutive eigenvalues through parameter combination. Once a seizure is detected, the system begins to control the stimulation of a two-phase pulse current with an amplitude and frequency of 34 μA and 200 Hz, respectively. The system is physically designed on the basis of the UMC 55 nm process and verified by a field programmable gate array verification board. This research is conducted through innovative algorithms to reduce power consumption and the area of the circuit. It can maintain a high accuracy of more than 90% and perform seizure detection every 64 ms. It is expected to provide a new treatment for patients with epilepsy.
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Affiliation(s)
- Liang-Hung Wang
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; (L.-H.W.); (Z.-N.Z.); (Q.-P.R.); (M.-H.F.)
| | - Zhen-Nan Zhang
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; (L.-H.W.); (Z.-N.Z.); (Q.-P.R.); (M.-H.F.)
| | - Chao-Xin Xie
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; (L.-H.W.); (Z.-N.Z.); (Q.-P.R.); (M.-H.F.)
| | - Hao Jiang
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; (L.-H.W.); (Z.-N.Z.); (Q.-P.R.); (M.-H.F.)
| | - Tao Yang
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; (L.-H.W.); (Z.-N.Z.); (Q.-P.R.); (M.-H.F.)
| | - Qi-Peng Ran
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; (L.-H.W.); (Z.-N.Z.); (Q.-P.R.); (M.-H.F.)
| | - Ming-Hui Fan
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; (L.-H.W.); (Z.-N.Z.); (Q.-P.R.); (M.-H.F.)
| | - I-Chun Kuo
- College of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, China;
| | - Zne-Jung Lee
- School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China;
| | - Jian-Bo Chen
- Department of Information and Telecommunications Engineering, Ming Chuan University, Taoyuan 32023, Taiwan;
| | - Tsung-Yi Chen
- Department of Electronic Engineering, Feng Chia University, Taichung 40724, Taiwan;
| | - Shih-Lun Chen
- The Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan;
| | - Patricia Angela R. Abu
- The Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines;
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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