| 研究生: |
王鵬翔 Wang, Peng-Hsiang |
|---|---|
| 論文名稱: |
深腦限頻亂數電刺激抑制小鼠顳葉癲癇發作之成效 Efficacy of Deep Brain Band-limited Random Noise Stimulation on Suppressing Mesial Temporal Seizure in Mice |
| 指導教授: |
朱銘祥
Ju, Ming-Shaung |
| 共同指導教授: |
林宙晴
Lin, Chou-Ching |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 機械工程學系 Department of Mechanical Engineering |
| 論文出版年: | 2021 |
| 畢業學年度: | 109 |
| 語文別: | 中文 |
| 論文頁數: | 222 |
| 中文關鍵詞: | 癲癇 、深腦電刺激 、亂數電刺激 |
| 外文關鍵詞: | seizure, deep brain stimulation, random noise stimulation |
| 相關次數: | 點閱:97 下載:5 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
癲癇為一種常見的神經性疾病,全世界約有五千萬人受其影響。現今最常見的癲癇治療方式為抗癲癇藥物和切除手術,然而約有三成的病患無法透過藥物得到有效的治療。醫療人員因此改由其他方式如電刺激嘗試醫治癲癇。高頻電刺激抑制癲癇的機轉目前雖尚不明確,但已有許多動物和臨床實驗證實其可有效舒緩或抑制癲癇發作。近年來出現穿顱亂數電刺激的研究,以雜訊刺激實驗對象腦區,發現其可增強受試者的學習能力或是改善某些疾病,然而關於亂數電刺激治療癲癇之研究卻相對缺乏。本研究的目的即藉由動物實驗確認深腦限頻亂數電刺激治療癲癇發作的效果。
本研究使用海藻酸誘發小鼠癲癇發作後,以頻帶介於 101-640 Hz 之頻帶侷限白雜訊和 128 Hz 之規律高頻訊號刺激小鼠海馬迴 CA3 區,再由訊號能量帶功率、Teager 能量運算子、樣本熵、CSC 卡爾曼濾波器等演算法評估上述電刺激方式治療癲癇的成效。結果顯示限頻亂數電刺激於刺激過程中可舒緩癲癇,但於刺激結束後的短時間內可能加劇癲癇發作,規律高頻電刺激在刺激過程中同樣有舒緩癲癇的效果,在刺激結束後對於小鼠癲癇的影響本研究則需要進行更多次實驗才能確認。除了上述結果,本研究也應用支援向量機判定癲癇抑制成功與否和抑制持續時間,實驗結果顯示限頻亂數電刺激在刺激過程中抑制癲癇發作的成功率較規律高頻電刺激高,但持續抑制癲癇的時間較規律高頻電刺激短。總而言之,本研究認為限頻亂數電刺在治療小鼠癲癇發作上的效果不比規律高頻電刺激差,為未來值得繼續嘗試或研究的方法。
Epilepsy is a common neurological disorder that affects many people worldwide. The most common epilepsy treatments today include antiepileptic drugs and resection surgery, yet about 30% of patients cannot be effectively treated with drugs. Medical professionals have therefore been turning to other modalities such as electrical stimulation for controlling seizure. The mechanism of deep brain high-frequency stimulation to suppress seizures is still unknown, but many animal and clinical studies have demonstrated its effectiveness in relieving or suppressing seizures. In recent years, there have been studies about transcranial random noise stimulation (tRNS), where band-limited white noise is used to stimulate subjects' brain. It was found that tRNS can enhance the subjects' learning ability or ease certain disorders, however, there is a relative lack of research of random noise stimulation on treating epileptic seizure. The purpose of this study is to confirm the effect of deep brain band-limited random noise stimulation on controlling epileptic seizure through animal experiments. 20 kainic acid-induced mice were recruited in this experiment, and the CA3 region of the hippocampus were stimulated with band-limited white noise with a frequency band between 101-640 Hz, or regular high-frequency signal at 128 Hz for 5 seconds while the subjects were having seizures. The effectiveness of these deep brain stimulation modalities in treating seizure was evaluated by the band power of the electroencephalography signals, or algorithms such as Teager energy operator, sample entropy, and constrained square-root cubature Kalman filter. The results show that band-limited random noise stimulation can relieve seizures during stimulation but may exacerbate them within a short period of time after stimulation. Regular high-frequency stimulation also has the effect of alleviating seizures during the stimulation process. The effect of regular high-frequency stimulation on seizure in mice after the stimulation may need further experimental studies. In addition, this study also utilized support vector machine to determine if seizure is suppressed during stimulation. The results show that band-limited random noise stimulation is more successful in suppressing seizures than regular high-frequency stimulation, but the duration of seizure suppression is shorter than that of regular high-frequency stimulation.
In conclusion, band-limited random noise stimulation is no less effective than regular high-frequency stimulation in the treatment of epileptic seizure in mice and is a method worthy of further investigation or study in the future.
R. S. Fisher et al., “ILAE Official Report: A Practical Clinical Definition of Epilepsy,” Epilepsia, vol. 55, no. 4, pp. 475–482, 2014.
“Epilepsy,” Jun. 19, 2019. https://www.who.int/news-room/fact-sheets/detail/epilepsy (accessed Sep. 01, 2021).
R. S. Fisher et al., “Operational Classification of Seizure Types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology,” Epilepsia, vol. 58, no. 4, pp. 522–530, Apr. 2017.
I. E. Scheffer et al., “ILAE Classification of the Epilepsies: Position Paper of the ILAE Commission for Classification and Terminology,” Epilepsia, vol. 58, no. 4, pp. 512–521, 2017.
J. F. Téllez-Zenteno and L. Hernández-Ronquillo, “A Review of the Epidemiology of Temporal Lobe Epilepsy,” Epilepsy Research and Treatment, vol. 2012, p. Artical ID 630853, Dec. 2011.
C. P. Panayiotopoulos, A Clinical Guide to Epileptic Syndromes and their Treatment, 2nd ed. London: Springer-Verlag, 2010.
W. O. I. Tatum, “Mesial Temporal Lobe Epilepsy,” Journal of Clinical Neurophysiology, vol. 29, no. 5, pp. 356–365, Oct. 2012.
T. N. Alotaiby, S. A. Alshebeili, T. Alshawi, I. Ahmad, and F. E. Abd El-Samie, “EEG Seizure Detection and Prediction Algorithms: A Survey,” EURASIP Journal on Advances in Signal Processing, vol. 2014, no. 1, p. 183, Dec. 2014.
V. Srinivasan, C. Eswaran, and and N. Sriraam, “Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features,” Journal of Medical Systems, vol. 29, no. 6, pp. 647–660, Dec. 2005.
V. Srinivasan, C. Eswaran, and N. Sriraam, “Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks,” IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 3, pp. 288–295, May 2007.
Y. Kumar, M. L. Dewal, and R. S. Anand, “Epileptic Seizures Detection in EEG Using DWT-based ApEn and Artificial Neural Network,” Signal, Image and Video Processing, vol. 8, no. 7, pp. 1323–1334, Oct. 2014.
R. Djemili, H. Bourouba, and M. C. Amara Korba, “Application of Empirical Mode Decomposition and Artificial Neural Network for the Classification of Normal and Epileptic EEG Signals,” Biocybernetics and Biomedical Engineering, vol. 36, no. 1, pp. 285–291, Jan. 2016.
U. R. Acharya, S. Vinitha Sree, G. Swapna, R. J. Martis, and J. S. Suri, “Automated EEG Analysis of Epilepsy: A Review,” Knowledge-Based Systems, vol. 45, pp. 147–165, Jun. 2013.
P. Kwan and M. J. Brodie, “Early Identification of Refractory Epilepsy,” New England Journal of Medicine, vol. 342, no. 5, pp. 314–319, Feb. 2000.
S. Spencer and L. Huh, “Outcomes of Epilepsy Surgery in Adults and Children,” The Lancet Neurology, vol. 7, no. 6, pp. 525–537, Jun. 2008.
J. F. Téllez-Zenteno, R. Dhar, and S. Wiebe, “Long-term seizure outcomes Following Epilepsy Surgery: A Systematic Review and Meta-analysis,” Brain, vol. 128, no. 5, pp. 1188–1198, May 2005.
E. G. Neal et al., “The Ketogenic Diet for the Treatment of Childhood Epilepsy: A Randomised Controlled Trial,” The Lancet Neurology, vol. 7, no. 6, pp. 500–506, Jun. 2008.
J. Freeman, P. Veggiotti, G. Lanzi, A. Tagliabue, E. Perucca, and Institute of Neurology IRCCS C. Mondino Foundation, “The Ketogenic Diet: From Molecular Mechanisms to Clinical Effects,” Epilepsy Research, vol. 68, no. 2, pp. 145–180, Feb. 2006.
G. K. Bergey, “Neurostimulation in the Treatment of Epilepsy,” Experimental Neurology, vol. 244, pp. 87–95, Jun. 2013.
P. Boon, E. De Cock, A. Mertens, and E. Trinka, “Neurostimulation for Drug-resistant Epilepsy: A Systematic Review of Clinical Evidence for Efficacy, Safety, Contraindications and Predictors for Response,” Current Opinion in Neurology, vol. 31, no. 2, pp. 198–210, Apr. 2018.
T. Wyckhuys, P. J. Geerts, R. Raedt, K. Vonck, W. Wadman, and P. Boon, “Deep Brain Stimulation for Epilepsy: Knowledge Gained from Experimental Animal Models,” Acta Neurologica Belgica, vol. 109, no. 2, pp. 63–80, Jun. 2009.
K. D. Graber and R. S. Fisher, “Deep Brain Stimulation for Epilepsy: Animal Models,” in Jasper’s Basic Mechanisms of the Epilepsies, 4th ed., J. L. Noebels, M. Avoli, M. A. Rogawski, R. W. Olsen, and A. V. Delgado-Escueta, Eds. Bethesda (MD): National Center for Biotechnology Information (US), 2012.
J. N. Bentley, C. Chestek, W. C. Stacey, and P. G. Patil, “Optogenetics in Epilepsy,” Neurosurgical Focus, vol. 34, no. 6, p. E4, Jun. 2013.
J. T. Paz and J. R. Huguenard, “Optogenetics and Epilepsy: Past, Present and Future: Shedding Light on Seizure Mechanisms and Potential Treatments,” Epilepsy Currents, vol. 15, no. 1, pp. 34–38, Jan. 2015.
江嘉駒, “高頻電刺激抑制癲癇之研究,” 博士論文, 機械工程學系, 國立成功大學, 臺灣, 2013.
謝汶宏, “θ 波段間歇性電刺激於腹側海馬連合以抑制癲癇,” 碩士論文, 機械工程學系, 國立成功大學, 臺灣, 2013.
陳穎俞, “以光基因刺激適應性抑制 4-AP 誘發之癲癇波,” 碩士論文, 機械工程學系, 國立成功大學, 臺灣, 2015.
詹傑凱, “以 α 頻率突發波抑制光基因轉殖小鼠之癲癇活動,” 碩士論文, 機械工程學系, 國立成功大學, 臺灣, 2016.
林秉弘, “應用樣本熵與光基因刺激發展即時控制系統抑制光基因轉殖小鼠之癲癇活動,” 碩士論文, 機械工程學系, 國立成功大學, 臺灣, 2017.
王誠鴻, “整合電與光刺激於光基因轉殖小鼠癲癇抑制之研究,” 碩士論文, 機械工程學系, 國立成功大學, 臺灣, 2018.
呂柏諺, “方根容積卡爾曼濾波器於癲癇光刺激抑制之研究,” 碩士論文, 機械工程學系, 國立成功大學, 臺灣, 2021.
湯登棋, “基於神經群模型之容積卡爾曼濾波器於慢性癲癇發作之研究,” 碩士論文, 機械工程學系, 國立成功大學, 臺灣, 2019.
R. S. Fisher, “Animal Models of the Epilepsies,” Brain Research Reviews, vol. 14, no. 3, pp. 245–278, Jul. 1989.
L. Kandratavicius et al., “Animal Models of Epilepsy: Use and Limitations,” Neuropsychiatric Disease and Treatment, vol. 10, pp. 1693–1705, Sep. 2014.
M. Lévesque and M. Avoli, “The Kainic Acid Model of Temporal Lobe Epilepsy,” Neuroscience & Biobehavioral Reviews, vol. 37, no. 10, pp. 2887–2899, Dec. 2013.
G. Curia, D. Longo, G. Biagini, R. S. G. Jones, and M. Avoli, “The Pilocarpine Model of Temporal Lobe Epilepsy,” Journal of Neuroscience Methods, vol. 172, no. 2, pp. 143–157, Jul. 2008.
M. Lévesque, M. Avoli, and C. Bernard, “Animal Models of Temporal Lobe Epilepsy Following Systemic Chemoconvulsant Administration,” Journal of Neuroscience Methods, vol. 260, pp. 45–52, Feb. 2016.
K. Tse, S. Puttachary, E. Beamer, G. J. Sills, and T. Thippeswamy, “Advantages of Repeated Low Dose Against Single High Dose of Kainate in C57BL/6J Mouse Model of Status Epilepticus: Behavioral and Electroencephalographic Studies,” PLOS ONE, vol. 9, no. 5, p. e96622, May 2014.
A. D. Umpierre et al., “Repeated Low-dose Kainate Administration in C57BL/6J Mice Produces Temporal Lobe Epilepsy Pathology but Infrequent Spontaneous Seizures,” Experimental Neurology, vol. 279, pp. 116–126, May 2016.
F. Wendling, P. Benquet, F. Bartolomei, and V. Jirsa, “Computational Models of Epileptiform Activity,” Journal of Neuroscience Methods, vol. 260, pp. 233–251, Feb. 2016.
F. H. Lopes da Silva, A. van Rotterdam, P. Barts, E. van Heusden, and W. Burr, “Models of Neuronal Populations: The Basic Mechanisms of Rhythmicity,” in Progress in Brain Research, vol. 45, M. A. Corner and D. F. Swaab, Eds. Elsevier, 1976, pp. 281–308.
F. Wendling, F. Bartolomei, J. J. Bellanger, and P. Chauvel, “Epileptic Fast Activity Can Be Explained by a Model of Impaired GABAergic Dendritic Inhibition,” European Journal of Neuroscience, vol. 15, no. 9, pp. 1499–1508, 2002.
F. Wendling, A. Hernandez, J.-J. Bellanger, P. Chauvel, and F. Bartolomei, “Interictal to Ictal Transition in Human Temporal Lobe Epilepsy: Insights From a Computational Model of Intracerebral EEG,” Journal of Clinical Neurophysiology, vol. 22, no. 5, pp. 343–356, Oct. 2005.
L. Kuhlmann et al., “Neural Mass Model-based Tracking of Anesthetic Brain States,” NeuroImage, vol. 133, pp. 438–456, Jun. 2016.
A. López-Cuevas, B. Castillo-Toledo, L. Medina-Ceja, and C. Ventura-Mejía, “State and Parameter Estimation of a Neural Mass Model From Electrophysiological Signals During the Status Epilepticus,” NeuroImage, vol. 113, pp. 374–386, Jun. 2015.
G. Hocepied, B. Legros, P. Van Bogaert, F. Grenez, and A. Nonclercq, “Early Detection of Epileptic Seizures Based on Parameter Identification of Neural Mass Model,” Computers in Biology and Medicine, vol. 43, no. 11, pp. 1773–1782, Nov. 2013.
R. M. Borisyuk and A. B. Kirillov, “Bifurcation Analysis of a Neural Network Model,” Biological Cybernetics, vol. 66, no. 4, pp. 319–325, Feb. 1992.
D. Terney, L. Chaieb, V. Moliadze, A. Antal, and W. Paulus, “Increasing Human Brain Excitability by Transcranial High-Frequency Random Noise Stimulation,” Journal of Neuroscience, vol. 28, no. 52, pp. 14147–14155, Dec. 2008.
A. Antal and C. S. Herrmann, “Transcranial Alternating Current and Random Noise Stimulation: Possible Mechanisms,” Neural Plasticity, vol. 2016, p. Article ID 3616807, May 2016.
L. Chaieb, A. Antal, and W. Paulus, “Transcranial Random Noise Stimulation-induced Plasticity is NMDA-receptor Independent but Sodium-channel Blocker and Benzodiazepines Sensitive,” Frontiers in Neuroscience, vol. 9, p. 125, 2015.
A. Romanska, C. Rezlescu, T. Susilo, B. Duchaine, and M. J. Banissy, “High-Frequency Transcranial Random Noise Stimulation Enhances Perception of Facial Identity,” Cerebral Cortex, vol. 25, no. 11, pp. 4334–4340, 11/12015.
O. van der Groen and N. Wenderoth, “Transcranial Random Noise Stimulation of Visual Cortex: Stochastic Resonance Enhances Central Mechanisms of Perception,” Journal of Neuroscience, vol. 36, no. 19, pp. 5289–5298, May 2016.
G. Campana, R. Camilleri, A. Pavan, A. Veronese, and G. Lo Giudice, “Improving Visual Functions in Adult Amblyopia with Combined Perceptual Training and Transcranial Random Noise Stimulation (tRNS): A Pilot Study,” Frontiers in Psychology, vol. 5, p. 1402, 2014.
A. Snowball et al., “Long-Term Enhancement of Brain Function and Cognition Using Cognitive Training and Brain Stimulation,” Current Biology, vol. 23, no. 11, pp. 987–992, Jun. 2013.
C. Y. Looi et al., “Transcranial Random Noise Stimulation and Cognitive Training to Improve Learning and Cognition of the Atypically Developing Brain: A Pilot Study,” Scientific Reports, vol. 7, no. 1, p. 4633, Jul. 2017.
S. Vanneste, F. Fregni, and D. De Ridder, “Head-to-Head Comparison of Transcranial Random Noise Stimulation, Transcranial AC Stimulation, and Transcranial DC Stimulation for Tinnitus,” Frontiers in Psychiatry, vol. 4, p. 158, 2013.
J. Van Doren, B. Langguth, and M. Schecklmann, “Electroencephalographic Effects of Transcranial Random Noise Stimulation in the Auditory Cortex,” Brain Stimulation, vol. 7, no. 6, pp. 807–812, 11/12014.
P. G. Mulquiney, K. E. Hoy, Z. J. Daskalakis, and P. B. Fitzgerald, “Improving Working Memory: Exploring the Effect of Transcranial Random Noise Stimulation and Transcranial Direct Current Stimulation on the Dorsolateral Prefrontal Cortex,” Clinical Neurophysiology, vol. 122, no. 12, pp. 2384–2389, 2011.
M. Schecklmann et al., “Bifrontal High-frequency Transcranial Random Noise Stimulation Is Not Effective as an Add-on Treatment in Depression,” Journal of Psychiatric Research, vol. 132, pp. 116–122, Jan. 2021.
G. Paxinos and K. B. J. Franklin, Paxinos and Franklin’s the Mouse Brain in Stereotaxic Coordinates. Academic Press, 2019.
T. Wyckhuys, R. Raedt, K. Vonck, W. Wadman, and P. Boon, “Comparison of Hippocampal Deep Brain Stimulation With High (130 Hz) and Low Frequency (5 Hz) on Afterdischarges in Kindled Rats,” Epilepsy Research, vol. 88, no. 2, pp. 239–246, Feb. 2010.
T. Hashimoto, C. M. Elder, and J. L. Vitek, “A Template Subtraction Method for Stimulus Artifact Removal in High-frequency Deep Brain Stimulation,” Journal of Neuroscience Methods, vol. 113, no. 2, pp. 181–186, Jan. 2002.
Y. Sun et al., “A Novel Method for Removal of Deep Brain Stimulation Artifact From Electroencephalography,” Journal of Neuroscience Methods, vol. 237, pp. 33–40, Nov. 2014.
D. P. Allen, E. L. Stegemöller, C. Zadikoff, J. M. Rosenow, and C. D. MacKinnon, “Suppression of Deep Brain Stimulation Artifacts From the Electroencephalogram by Frequency-domain Hampel Filtering,” Clinical Neurophysiology, vol. 121, no. 8, pp. 1227–1232, Aug. 2010.
I. Arasaratnam, S. Haykin, and T. R. Hurd, “Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations,” IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 4977–4993, Oct. 2010.
C.-H. Im, Ed., Computational EEG Analysis: Methods and Applications. Springer Singapore, 2018.
J. F. Kaiser, “On a Simple Algorithm to Calculate the ‘Energy’ of a Signal,” in International Conference on Acoustics, Speech, and Signal Processing, Apr. 1990, pp. 381–384 vol.1.
M. Bahoura and J. Rouat, “Wavelet Speech Enhancement Based on the Teager Energy Operator,” IEEE Signal Processing Letters, vol. 8, no. 1, pp. 10–12, Jan. 2001.
F. Jabloun, A. E. Cetin, and E. Erzin, “Teager Energy Based Feature Parameters for Speech Recognition in Car Noise,” IEEE Signal Processing Letters, vol. 6, no. 10, pp. 259–261, Oct. 1999.
P. Henríquez Rodríguez, J. B. Alonso, M. A. Ferrer, and C. M. Travieso, “Application of the Teager–Kaiser Energy Operator in Bearing Fault Diagnosis,” ISA Transactions, vol. 52, no. 2, pp. 278–284, Mar. 2013.
M. Pineda-Sanchez et al., “Application of the Teager–Kaiser Energy Operator to the Fault Diagnosis of Induction Motors,” IEEE Transactions on Energy Conversion, vol. 28, no. 4, pp. 1036–1044, Dec. 2013.
M. A. Hanson et al., “Teager Energy Assessment of Tremor Severity in Clinical Application of Wearable Inertial Sensors,” in 2007 IEEE/NIH Life Science Systems and Applications Workshop, Nov. 2007, pp. 136–139.
C. Kamath, “A New Approach to Detect Congestive Heart Failure Using Teager Energy Nonlinear Scatter Plot of R–R Interval Series,” Medical Engineering & Physics, vol. 34, no. 7, pp. 841–848, Sep. 2012.
R. Nelson et al., “Detection of High Frequency Oscillations with Teager Energy in an Animal Model of Limbic Epilepsy,” in 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 2578–2580.
N. Gaspard, R. Alkawadri, P. Farooque, I. I. Goncharova, and H. P. Zaveri, “Automatic Detection of Prominent Interictal Spikes in Intracranial EEG: Validation of an Algorithm and Relationsip to the Seizure Onset Zone,” Clinical Neurophysiology, vol. 125, no. 6, pp. 1095–1103, Jun. 2014.
E. I. Plotkin and M. N. S. Swamy, “Signal Processing Based on Parameter Structural Modeling and Separation of Highly Correlated Signals of Known Structure,” Circuits, Systems and Signal Processing, vol. 17, no. 1, pp. 51–68, Jan. 1998.
J. S. Richman and J. R. Moorman, “Physiological Time-series Analysis Using Approximate Entropy and Sample Entropy,” American Journal of Physiology Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039-2049, Jun. 2000.
Y. Song, J. Crowcroft, and J. Zhang, “Automatic Epileptic Seizure Detection in Eegs Based on Optimized Sample Entropy and Extreme Learning Machine,” Journal of Neuroscience Methods, vol. 210, no. 2, pp. 132–146, Sep. 2012.
S. Pravin Kumar, N. Sriraam, P. G. Benakop, and B. C. Jinaga, “Entropies Based Detection of Epileptic Seizures With Artificial Neural Network Classifiers,” Expert Systems with Applications, vol. 37, no. 4, pp. 3284–3291, Apr. 2010.
A. Delgado-Bonal and A. Marshak, “Approximate Entropy and Sample Entropy: A Comprehensive Tutorial,” Entropy, vol. 21, no. 6, p. 541, Jun. 2019.
J. Durbin and S. J. Koopman, Time Series Analysis by State Space Methods: Second Edition. Oxford: Oxford University Press, 2012.
K. Staley, “Molecular Mechanisms of Epilepsy,” Nature Neuroscience, vol. 18, no. 3, pp. 367–372, Mar. 2015.
B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A Training Algorithm for Optimal Margin Classifiers,” in Proceedings of the Fifth Annual Workshop on Computational Learning Theory, New York, Jul. 1992, pp. 144–152.
C.-C. Chang and C.-J. Lin, “LIBSVM: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, p. 27:1-27:27, May 2011.
M. Pecha and D. Horák, “Analyzing l1-loss and l2-loss Support Vector Machines Implemented in PERMON Toolbox,” in AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application, Cham, 2020, pp. 13–23.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, 2nd ed. New York: Springer-Verlag, 2009.
F. Wendling, J. J. Bellanger, F. Bartolomei, and P. Chauvel, “Relevance of Nonlinear Lumped-parameter Models in the Analysis of Depth-EEG Epileptic Signals,” Biological Cybernetics, vol. 83, no. 4, pp. 367–378, Sep. 2000.
B. H. Jansen and V. G. Rit, “Electroencephalogram and Visual Evoked Potential Generation in a Mathematical Model of Coupled Cortical Columns,” Biological Cybernetics, vol. 73, no. 4, pp. 357–366, Sep. 1995.
C.-C. Chiang, T. P. Ladas, L. E. Gonzalez-Reyes, and D. M. Durand, “Seizure Suppression by High Frequency Optogenetic Stimulation Using in vitro and in vivo Animal Models of Epilepsy,” Brain Stimulation, vol. 7, no. 6, pp. 890–899, Nov. 2014.
S. C. Dhamne et al., “Acute Seizure Suppression by Transcranial Direct Current Stimulation in Rats,” Annals of Clinical and Translational Neurology, vol. 2, no. 8, pp. 843–856, 2015.
M. Kinoshita et al., “Electric Cortical Stimulation Suppresses Epileptic and Background Activities in Neocortical Epilepsy and Mesial Temporal Lobe Epilepsy,” Clinical Neurophysiology, vol. 116, no. 6, pp. 1291–1299, Jun. 2005.
Y. Tang and D. M. Durand, “A Novel Electrical Stimulation Paradigm for the Suppression of Epileptiform Activity in an in vivo Model of Mesial Temporal Lobe Status Epilepticus,” International Journal of Neural Systems, vol. 22, no. 03, p. 1250006, Jun. 2012.
W. J. Chang, W. P. Chang, and B. C. Shyu, “Suppression of Cortical Seizures by Optic Stimulation of the Reticular Thalamus in PV-mhChR2-YFP BAC Transgenic Mice,” Molecular Brain, vol. 10, no. 1, p. 42, Sep. 2017.