• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

癫痫发作检测与实验性治疗：综述

Epileptic Seizure Detection and Experimental Treatment: A Review.

作者信息

Kim Taeho, Nguyen Phuc, Pham Nhat, Bui Nam, Truong Hoang, Ha Sangtae, Vu Tam

机构信息

Department of Computer Science, University of Colorado, Boulder, CO, United States.

Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.

出版信息

Front Neurol. 2020 Jul 21;11:701. doi: 10.3389/fneur.2020.00701. eCollection 2020.

DOI:10.3389/fneur.2020.00701
PMID:32849189
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7396638/
Abstract

One-fourths of the patients have medication-resistant seizures and require seizure detection and treatment continuously to cope with sudden seizures. Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the human head and body. In this article, we first discuss recent advances in seizure sensing, signal processing, time- or frequency-domain analysis, and classification algorithms to detect and classify seizure stages. Then, we show a strong potential of applying recent advancements in non-invasive brain stimulation technology to treat seizures. In particular, we explain the fundamentals of brain stimulation approaches, including (1) transcranial magnetic stimulation (TMS), (2) transcranial direct current stimulation (tDCS), (3) transcranial focused ultrasound stimulation (tFUS), and how to use them to treat seizures. Through this review, we intend to provide a broad view of both recent seizure diagnoses and treatments. Such knowledge would help fresh and experienced researchers to capture the advancements in sensing, detection, classification, and treatment seizures. Last but not least, we provide potential research directions that would attract seizure researchers/engineers in the field.

摘要

四分之一的患者患有药物难治性癫痫发作，需要持续进行癫痫检测和治疗以应对突然发作。癫痫发作可以通过监测大脑和肌肉活动、心率、血氧水平、人为声音或视觉特征来检测，这些监测可通过脑电图（EEG）、肌电图（EMG）、心电图（ECG）、人体头部和身体上的运动或音频/视频记录来实现。在本文中，我们首先讨论癫痫发作传感、信号处理、时域或频域分析以及用于检测和分类癫痫发作阶段的分类算法方面的最新进展。然后，我们展示了应用非侵入性脑刺激技术的最新进展来治疗癫痫发作的强大潜力。特别是，我们解释了脑刺激方法的基本原理，包括（1）经颅磁刺激（TMS）、（2）经颅直流电刺激（tDCS）、（3）经颅聚焦超声刺激（tFUS）以及如何使用它们来治疗癫痫发作。通过这篇综述，我们旨在提供关于近期癫痫诊断和治疗的广泛观点。这些知识将有助于新手和经验丰富的研究人员了解癫痫发作的传感、检测、分类和治疗方面的进展。最后但同样重要的是，我们提供了可能吸引该领域癫痫研究人员/工程师的潜在研究方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/f7a906950db1/fneur-11-00701-g0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/56caa9f7b74d/fneur-11-00701-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/53e8319ca1e0/fneur-11-00701-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/81383d57bb94/fneur-11-00701-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/cc71bff13b3f/fneur-11-00701-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/48061ce75fa0/fneur-11-00701-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/7456a0864403/fneur-11-00701-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/e256d93cade0/fneur-11-00701-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/06bdc2d9099d/fneur-11-00701-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/6f151b9060df/fneur-11-00701-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/90696269e44e/fneur-11-00701-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/f7a906950db1/fneur-11-00701-g0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/56caa9f7b74d/fneur-11-00701-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/53e8319ca1e0/fneur-11-00701-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/81383d57bb94/fneur-11-00701-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/cc71bff13b3f/fneur-11-00701-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/48061ce75fa0/fneur-11-00701-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/7456a0864403/fneur-11-00701-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/e256d93cade0/fneur-11-00701-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/06bdc2d9099d/fneur-11-00701-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/6f151b9060df/fneur-11-00701-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/90696269e44e/fneur-11-00701-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c49d/7396638/f7a906950db1/fneur-11-00701-g0011.jpg

相似文献

1
Epileptic Seizure Detection and Experimental Treatment: A Review.癫痫发作检测与实验性治疗：综述
Front Neurol. 2020 Jul 21;11:701. doi: 10.3389/fneur.2020.00701. eCollection 2020.
2
Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping.记录用于神经科学研究和实时功能性皮层图谱绘制的人类皮层脑电图（ECoG）信号。
J Vis Exp. 2012 Jun 26(64):3993. doi: 10.3791/3993.
3
Transcranial direct current stimulation alleviates seizure severity in kainic acid-induced status epilepticus rats.经颅直流电刺激可减轻红藻氨酸诱导癫痫持续状态大鼠的癫痫发作严重程度。
Exp Neurol. 2020 Jun;328:113264. doi: 10.1016/j.expneurol.2020.113264. Epub 2020 Feb 29.
4
Case Report: Prolonged Effects of Short-Term Transcranial Magnetic Stimulation on EEG Biomarkers, Spectral Power, and Seizure Frequency.病例报告：短期经颅磁刺激对脑电图生物标志物、频谱功率和癫痫发作频率的长期影响
Front Neurosci. 2022 Jun 10;16:866212. doi: 10.3389/fnins.2022.866212. eCollection 2022.
5
Multi-modal intelligent seizure acquisition (MISA) system--a new approach towards seizure detection based on full body motion measures.多模态智能癫痫发作采集（MISA）系统——一种基于全身运动测量的癫痫发作检测新方法。
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2591-5. doi: 10.1109/IEMBS.2009.5335334.
6
Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals.基于随机神经网络的利用脑电图信号的癫痫发作检测。
Sensors (Basel). 2022 Mar 23;22(7):2466. doi: 10.3390/s22072466.
7
Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings.基于肌电图的癫痫发作探测器：广义强直阵挛性癫痫发作检测算法与视频-脑电图记录比较的初步结果。
Epilepsia. 2015 Sep;56(9):1432-7. doi: 10.1111/epi.13083. Epub 2015 Jul 20.
8
Transcranial Magnetic Stimulation (TMS) Safety with Respect to Seizures: A Literature Review.经颅磁刺激（TMS）与癫痫发作相关的安全性：文献综述
Neuropsychiatr Dis Treat. 2020 Dec 7;16:2989-3000. doi: 10.2147/NDT.S276635. eCollection 2020.
9
A channel independent generalized seizure detection method for pediatric epileptic seizures.一种用于儿科癫痫发作的通道无关的广义癫痫发作检测方法。
Comput Methods Programs Biomed. 2021 Sep;209:106335. doi: 10.1016/j.cmpb.2021.106335. Epub 2021 Aug 5.
10
Clinical utility of a video/audio-based epilepsy monitoring system Nelli.基于视频/音频的癫痫监测系统 Nelli 的临床实用性。
Epilepsy Behav. 2022 Aug;133:108804. doi: 10.1016/j.yebeh.2022.108804. Epub 2022 Jun 23.

引用本文的文献

1
Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives.使用CHB-MIT数据集进行癫痫发作检测：被忽视的观点。
R Soc Open Sci. 2024 May 29;11(5):230601. doi: 10.1098/rsos.230601. eCollection 2024 May.
2
Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning.使用机器学习优化可穿戴 EEG 癫痫检测的电极配置。
Sensors (Basel). 2023 Jun 21;23(13):5805. doi: 10.3390/s23135805.
3
Electroceutical and Bioelectric Therapy: Its Advantages and Limitations.电制药与生物电疗法：其优势与局限性

本文引用的文献

1
False alarms reduction in non-convulsive status epilepticus detection via continuous EEG analysis.通过连续 EEG 分析减少非惊厥性癫痫持续状态检测中的假警报。
Physiol Meas. 2020 Jun 10;41(5):055009. doi: 10.1088/1361-6579/ab8cb3.
2
Sounds of seizures.癫痫发作的声音。
Seizure. 2020 May;78:86-90. doi: 10.1016/j.seizure.2020.03.008. Epub 2020 Mar 18.
3
Seizure detection based on heart rate variability using a wearable electrocardiography device.基于可穿戴心电图设备的心电信号变异性的癫痫发作检测。
Clin Psychopharmacol Neurosci. 2023 Feb 28;21(1):19-31. doi: 10.9758/cpn.2023.21.1.19.
4
Ictal fMRI: Mapping Seizure Topography with Rhythmic BOLD Oscillations.发作期功能磁共振成像：通过节律性血氧水平依赖振荡绘制癫痫发作地形图。
Brain Sci. 2022 Dec 13;12(12):1710. doi: 10.3390/brainsci12121710.
5
Accurate detection of heart rate using in-ear photoplethysmography in a clinical setting.在临床环境中使用入耳式光电容积脉搏波描记法准确检测心率。
Front Digit Health. 2022 Aug 17;4:909519. doi: 10.3389/fdgth.2022.909519. eCollection 2022.
6
Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method.用于癫痫发作检测的压电纳米发电机供电自偏置放大器的优化设计：元启发式算法与人工神经网络辅助目标达成方法
Micromachines (Basel). 2022 Jul 14;13(7):1104. doi: 10.3390/mi13071104.
7
Future opportunities for research in rescue treatments.救援治疗研究的未来机会。
Epilepsia. 2022 Sep;63 Suppl 1(Suppl 1):S55-S68. doi: 10.1111/epi.17363.
8
An On-Demand Drug Delivery System for Control of Epileptiform Seizures.一种用于控制癫痫样发作的按需给药系统。
Pharmaceutics. 2022 Feb 21;14(2):468. doi: 10.3390/pharmaceutics14020468.
9
Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection.具有延迟选项和低信任度过滤的自动癫痫发作检测分类
Sensors (Basel). 2021 Feb 4;21(4):1046. doi: 10.3390/s21041046.
10
A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control.微电子系统与电路技术在闭环癫痫控制中的电神经记录应用综述
Sensors (Basel). 2020 Oct 8;20(19):5716. doi: 10.3390/s20195716.
Epilepsia. 2019 Oct;60(10):2105-2113. doi: 10.1111/epi.16343. Epub 2019 Sep 20.
4
Efficient Epileptic Seizure Prediction Based on Deep Learning.基于深度学习的高效癫痫发作预测。
IEEE Trans Biomed Circuits Syst. 2019 Oct;13(5):804-813. doi: 10.1109/TBCAS.2019.2929053. Epub 2019 Jul 17.
5
Deep learning for electroencephalogram (EEG) classification tasks: a review.深度学习在脑电图（EEG）分类任务中的应用：综述。
J Neural Eng. 2019 Jun;16(3):031001. doi: 10.1088/1741-2552/ab0ab5. Epub 2019 Feb 26.
6
Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images.基于卷积神经网络的头皮脑电图图谱图像分析的癫痫发作检测。
Neuroimage Clin. 2019;22:101684. doi: 10.1016/j.nicl.2019.101684. Epub 2019 Jan 22.
7
Multimodal seizure detection: A review.多模态癫痫发作检测：综述。
Epilepsia. 2018 Jun;59 Suppl 1:42-47. doi: 10.1111/epi.14047.
8
Seizure detection using scalp-EEG.头皮 EEG 检测癫痫发作。
Epilepsia. 2018 Jun;59 Suppl 1:14-22. doi: 10.1111/epi.14052.
9
Studying and modifying brain function with non-invasive brain stimulation.用非侵入性脑刺激研究和修饰大脑功能。
Nat Neurosci. 2018 Feb;21(2):174-187. doi: 10.1038/s41593-017-0054-4. Epub 2018 Jan 8.
10
Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy.头皮 EEG 与耳后 EEG 比较：为局灶性癫痫患者开发可穿戴式癫痫发作检测系统。
Sensors (Basel). 2017 Dec 23;18(1):29. doi: 10.3390/s18010029.