计算机辅助分析常规脑电图以识别癫痫的隐藏生物标志物：系统评价方案。
Computer-assisted analysis of routine electroencephalogram to identify hidden biomarkers of epilepsy: protocol for a systematic review.
机构信息
Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, Québec, Canada.
出版信息
BMJ Open. 2023 Jan 24;13(1):e066932. doi: 10.1136/bmjopen-2022-066932.
INTRODUCTION
The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy.
METHODS AND ANALYSIS
The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies.
ETHICS AND DISSEMINATION
Ethical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field.
PROSPERO REGISTRATION NUMBER
CRD42022292261.
简介
癫痫的诊断通常依赖于神经科医生对脑电图（EEG）的视觉解读。癫痫在脑电图上的标志是发作间期癫痫样放电（IED）。这个标志物的灵敏度不足：它仅在癫痫患者的 30 分钟常规 EEG 中捕捉到一小部分。在过去的三十年中，人们越来越感兴趣地使用计算方法来分析 EEG，而不依赖于 IED 的检测，但没有一种方法能应用于临床实践。我们旨在综述定量方法在动态脑电图分析中的诊断准确性，以指导癫痫的诊断和管理。
方法和分析
该方案符合 Cochrane 对诊断性测试准确性系统评价的建议。我们将搜索 MEDLINE、EMBASE、EBM 综述、IEEE Explore 以及 1961 年后发表的灰色文献中的文章、会议论文和会议摘要。我们将包括观察性研究，这些研究提出了一种计算方法来分析 EEG，以诊断成人或儿童的癫痫，而不依赖于 IED 或癫痫发作的识别。参考标准是由医生诊断为癫痫。我们将报告每个标志物的估计汇总敏感性和特异性，以及接收者操作特征曲线下面积（ROC AUC）。如果可能，我们将对每个单独标志物的敏感性、特异性和 ROC AUC 进行荟萃分析。我们将使用改编后的 QUADAS-2 工具评估偏倚风险。我们还将描述用于信号处理、特征提取和预测建模的算法，并评论不同研究的可重复性。
伦理和传播
不需要伦理批准。研究结果将通过同行评审出版物发表，并在与该领域相关的会议上展示。
PROSPERO 注册号：CRD42022292261。
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