• 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

非神经生理学家使用新型中位数功率谱图进行癫痫发作检测的评估。

Evaluation of a novel median power spectrogram for seizure detection by non-neurophysiologists.

作者信息

Yan Peter, Melman Tamar, Yan Sherry, Otgonsuren Munkhzul, Grinspan Zachary

机构信息

Weill Cornell Medical College, Department of Neurology, United States; Weill Cornell Medical College, Department of Health Policy and Research, United States.

Weill Cornell Medical College, Feil Family Brain and Mind Research Institute, United States.

出版信息

Seizure. 2017 Aug;50:109-117. doi: 10.1016/j.seizure.2017.06.016. Epub 2017 Jun 15.

DOI:10.1016/j.seizure.2017.06.016
PMID:28732280
Abstract

PURPOSE

(1) To evaluate how well resident physicians use a novel EEG spectral analysis tool (the median power spectrogram; MPS) to detect seizures. (2) To assess the capability of the MPS to identify different seizure types.

METHODS

120 EEG records from children with intractable seizures were converted to MPS by taking the median power across leads and using multi-taper spectral estimation. Twelve blinded neurology residents were trained to interpret the spectrogram with a five-minute video tutorial and post-test. Two residents independently assessed each set for presence of seizures. Their performance was compared to seizures identified using conventional EEG. Two blinded neurologists separately reviewed the EEGs and spectrograms to independently categorize the seizures. Their results were used to determine the spectrogram's capability to reveal seizures and visualize different seizure types for the user.

RESULTS

Three key MPS features distinguished seizures from inter-ictal background: power difference relative to background, down-sloping resonance bands, and power in high frequencies. Using these features, residents identified seizures with 77% sensitivity and 72% specificity. 86% (51/59) of focal seizures and 81% (22/27) of generalized seizures were detected by at least one resident. Missed seizures included brief (<60s) seizures, tonic seizures, seizures with predominant delta (0-4Hz) activity, and seizures evident primarily in supplementary low temporal leads.

CONCLUSIONS

The MPS is a novel qEEG modality that requires minimal training to interpret. It enables physicians without extensive neurophysiology training to identify seizures with sensitivity and specificity comparable to more complex multi-modal qEEG displays.

摘要

目的

(1)评估住院医师使用新型脑电图频谱分析工具(中位数功率谱图;MPS)检测癫痫发作的效果。(2)评估MPS识别不同癫痫发作类型的能力。

方法

通过跨导联取中位数功率并使用多窗谱估计,将120例难治性癫痫患儿的脑电图记录转换为MPS。12名不知情的神经科住院医师通过5分钟的视频教程和测试后培训来解读频谱图。两名住院医师独立评估每组是否存在癫痫发作。将他们的表现与使用传统脑电图识别的癫痫发作进行比较。两名不知情的神经科医生分别审查脑电图和频谱图,以独立对癫痫发作进行分类。他们的结果用于确定频谱图对用户揭示癫痫发作和可视化不同癫痫发作类型的能力。

结果

MPS的三个关键特征将癫痫发作与发作间期背景区分开来:相对于背景的功率差异、向下倾斜的共振带和高频功率。利用这些特征,住院医师识别癫痫发作的灵敏度为77%,特异度为72%。至少一名住院医师检测到86%(51/59)的局灶性癫痫发作和81%(22/27)的全身性癫痫发作。漏诊的癫痫发作包括短暂(<60秒)癫痫发作、强直发作、以δ波(0-4Hz)活动为主的癫痫发作以及主要在颞叶下部辅助导联明显的癫痫发作。

结论

MPS是一种新型定量脑电图模式,解读所需培训极少。它使没有广泛神经生理学培训的医生能够以与更复杂的多模式定量脑电图显示相当的灵敏度和特异度识别癫痫发作。

相似文献

1
Evaluation of a novel median power spectrogram for seizure detection by non-neurophysiologists.非神经生理学家使用新型中位数功率谱图进行癫痫发作检测的评估。
Seizure. 2017 Aug;50:109-117. doi: 10.1016/j.seizure.2017.06.016. Epub 2017 Jun 15.
2
Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes.定量脑电图(QEEG)频谱图检测癫痫发作亚型的比较敏感性。
Seizure. 2018 Feb;55:70-75. doi: 10.1016/j.seizure.2018.01.008. Epub 2018 Jan 31.
3
Diagnostic Accuracy of Electrographic Seizure Detection by Neurophysiologists and Non-Neurophysiologists in the Adult ICU Using a Panel of Quantitative EEG Trends.使用一组定量脑电图趋势,神经生理学家和非神经生理学家在成人重症监护病房中进行脑电图癫痫发作检测的诊断准确性。
J Clin Neurophysiol. 2015 Aug;32(4):324-30. doi: 10.1097/WNP.0000000000000144.
4
Assessing quantitative EEG spectrograms to identify non-epileptic events.评估定量脑电图频谱图以识别非癫痫性事件。
Epileptic Disord. 2017 Sep 1;19(3):299-306. doi: 10.1684/epd.2017.0921.
5
Seizure detection: an assessment of time- and frequency-based features in a unified two-dimensional decisional space using nonlinear decision functions.癫痫发作检测:使用非线性决策函数在统一的二维决策空间中对基于时间和频率的特征进行评估。
J Clin Neurophysiol. 2009 Dec;26(6):381-91. doi: 10.1097/WNP.0b013e3181c29928.
6
Non-expert use of quantitative EEG displays for seizure identification in the adult neuro-intensive care unit.成人神经重症监护病房中癫痫发作识别的定量脑电图显示的非专业使用
Epilepsy Res. 2015 Jan;109:48-56. doi: 10.1016/j.eplepsyres.2014.10.013. Epub 2014 Oct 28.
7
A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends.神经重症监护病房护士使用一组定量脑电图趋势实时电描记术检测发作的试验。
Neurocrit Care. 2019 Oct;31(2):312-320. doi: 10.1007/s12028-019-00673-z.
8
Seizure identification in the ICU using quantitative EEG displays.使用定量脑电图显示在 ICU 中识别癫痫发作。
Neurology. 2010 Oct 26;75(17):1501-8. doi: 10.1212/WNL.0b013e3181f9619e. Epub 2010 Sep 22.
9
Sensitivity of quantitative EEG for seizure identification in the intensive care unit.重症监护病房中定量脑电图对癫痫识别的敏感性。
Neurology. 2016 Aug 30;87(9):935-44. doi: 10.1212/WNL.0000000000003034. Epub 2016 Jul 27.
10
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.