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Detection of generalized tonic-clonic seizures using surface electromyographic monitoring.使用表面肌电图监测检测全身性强直阵挛性发作。
Epilepsia. 2017 Nov;58(11):1861-1869. doi: 10.1111/epi.13897. Epub 2017 Oct 5.
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Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.改进型可穿戴多模式惊厥发作探测器的多中心临床评估
Epilepsia. 2017 Nov;58(11):1870-1879. doi: 10.1111/epi.13899. Epub 2017 Oct 4.
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Simulating Clinical Trials With and Without Intracranial EEG Data.模拟有无颅内脑电图数据的临床试验。
Epilepsia Open. 2017 Jun;2(2):156-161. doi: 10.1002/epi4.12038. Epub 2017 Jan 18.
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A big data approach to the development of mixed-effects models for seizure count data.一种用于癫痫发作计数数据的混合效应模型开发的大数据方法。
Epilepsia. 2017 May;58(5):835-844. doi: 10.1111/epi.13727. Epub 2017 Mar 30.
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Instruction manual for the ILAE 2017 operational classification of seizure types.国际抗癫痫联盟(ILAE)2017年癫痫发作类型操作分类指南
Epilepsia. 2017 Apr;58(4):531-542. doi: 10.1111/epi.13671. Epub 2017 Mar 8.
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Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology.国际抗癫痫联盟对癫痫发作类型的操作性分类:国际抗癫痫联盟分类和术语委员会立场文件
Epilepsia. 2017 Apr;58(4):522-530. doi: 10.1111/epi.13670. Epub 2017 Mar 8.

癫痫移动医疗系统的通用数据元素。

Common data elements for epilepsy mobile health systems.

机构信息

Division of Epilepsy, Beth Israel Deaconess Medical Center, Boston, MA, USA.

Clinical Epilepsy Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.

出版信息

Epilepsia. 2018 May;59(5):1020-1026. doi: 10.1111/epi.14066. Epub 2018 Mar 31.

DOI:10.1111/epi.14066
PMID:29604050
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5934312/
Abstract

OBJECTIVE

Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data.

METHODS

Using a group of stakeholders from industry, academia, and patient advocacy organizations, we offer a consensus statement for the elements that may facilitate communication among different systems.

RESULTS

A consensus statement is presented for epilepsy mHealth CDEs.

SIGNIFICANCE

Although it is not exclusive, we believe that the use of a minimal common information denominator, specifically these CDEs, will promote innovation, accelerate scientific discovery, and enhance clinical usage across applications and devices in the epilepsy mHealth space. As a consequence, people with epilepsy will have greater flexibility and ultimately more powerful tools to improve their lives.

摘要

目的

移动医疗(mHealth)在癫痫设备和相关应用中缺乏通用数据元素(CDEs)。因此,尽管针对癫痫患者的新数字服务广泛增长,但所收集的信息通常不可互操作或无法直接比较。我们旨在通过为 mHealth 癫痫数据制定行业标准来纠正这个问题。

方法

我们使用来自行业、学术界和患者倡导组织的一组利益相关者,为可能促进不同系统之间通信的元素提供了共识声明。

结果

提出了癫痫 mHealth CDE 的共识声明。

意义

尽管不是排他性的,但我们相信使用最小的通用信息基准,特别是这些 CDEs,将促进创新,加速科学发现,并增强癫痫 mHealth 领域的应用程序和设备中的临床使用。因此,癫痫患者将拥有更大的灵活性,并最终拥有更强大的工具来改善他们的生活。