Automating the analysis of EEG recordings from prematurely-born infants: A Bayesian approach

IF 3.6 3区医学 Q1 CLINICAL NEUROLOGY Clinical Neurophysiology Pub Date : 2013-03-01 DOI:10.1016/j.clinph.2012.09.003

Timothy J. Mitchell , Jeffrey J. Neil , John M. Zempel , Liu Lin Thio , Terrie E. Inder , G. Larry Bretthorst

{"title":"Automating the analysis of EEG recordings from prematurely-born infants: A Bayesian approach","authors":"Timothy J. Mitchell , Jeffrey J. Neil , John M. Zempel , Liu Lin Thio , Terrie E. Inder , G. Larry Bretthorst","doi":"10.1016/j.clinph.2012.09.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>To implement an automated analysis of EEG recordings from prematurely-born infants and thus provide objective, reproducible results.</p></div><div><h3>Methods</h3><p><span>Bayesian probability theory is employed to compute the posterior probability for developmental features of interest in EEG recordings. Currently, these features include smooth delta waves (0.5–1.5</span>       <!-->μV), though the approach taken can be generalized to identify other EEG features of interest.</p></div><div><h3>Results</h3><p>When compared with experienced electroencephalographers, the algorithm had a true positive rate between 72% and 79% for the identification of delta waves (smooth or “brush”) and interburst intervals, which is comparable to the inter-rater reliability. When distinguishing between smooth delta waves and delta brushes, the algorithm’s true positive rate was between 53% and 88%, which is slightly less than the inter-rater reliability.</p></div><div><h3>Conclusion</h3><p>Bayesian probability theory can be employed to consistently identify features of EEG recordings from premature infants.</p></div><div><h3>Significance</h3><p>The identification of features in EEG recordings provides a first step towards the automated analysis of EEG recordings from premature infants.</p></div>","PeriodicalId":10671,"journal":{"name":"Clinical Neurophysiology","volume":"124 3","pages":"Pages 452-461"},"PeriodicalIF":3.6000,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.clinph.2012.09.003","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Neurophysiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1388245712006001","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}

引用次数: 7

Abstract

Objective

To implement an automated analysis of EEG recordings from prematurely-born infants and thus provide objective, reproducible results.

Methods

Bayesian probability theory is employed to compute the posterior probability for developmental features of interest in EEG recordings. Currently, these features include smooth delta waves (0.5–1.5 Hz, >100 μV), delta brushes (delta portion: 0.5–1.5 Hz, >100 μV; “brush” portion: 8–22 Hz, <75 μV), and interburst intervals (<10 μV), though the approach taken can be generalized to identify other EEG features of interest.

Results

When compared with experienced electroencephalographers, the algorithm had a true positive rate between 72% and 79% for the identification of delta waves (smooth or “brush”) and interburst intervals, which is comparable to the inter-rater reliability. When distinguishing between smooth delta waves and delta brushes, the algorithm’s true positive rate was between 53% and 88%, which is slightly less than the inter-rater reliability.

Conclusion

Bayesian probability theory can be employed to consistently identify features of EEG recordings from premature infants.

Significance

The identification of features in EEG recordings provides a first step towards the automated analysis of EEG recordings from premature infants.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

自动分析早产儿的脑电图记录:贝叶斯方法

目的实现对早产儿脑电图记录的自动分析，从而提供客观、可重复的结果。方法采用贝叶斯概率理论计算脑电记录中感兴趣的发展特征的后验概率。目前，这些特性包括平滑δ波(0.5-1.5 Hz， >100 μV)， δ刷(δ部分:0.5-1.5 Hz， >100 μV;“刷”部分:8-22 Hz， <75 μV)和突发间隔(<10 μV)，尽管所采用的方法可以推广到识别其他感兴趣的EEG特征。结果与经验丰富的脑电图专家相比，该算法对三角波(平滑或“刷状”)和间歇时间的识别真阳性率在72% ~ 79%之间，与脑电间可靠性相当。在区分平滑δ波和δ刷时，算法的真阳性率在53% ~ 88%之间，略低于rater间的可靠性。结论贝叶斯概率理论可用于早产儿脑电图记录特征的一致性识别。脑电图记录特征的识别为早产儿脑电图记录的自动分析提供了第一步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Clinical Neurophysiology 医学-临床神经学

CiteScore

8.70

自引率

6.40%

发文量

932

审稿时长

59 days

期刊介绍： As of January 1999, The journal Electroencephalography and Clinical Neurophysiology, and its two sections Electromyography and Motor Control and Evoked Potentials have amalgamated to become this journal - Clinical Neurophysiology. Clinical Neurophysiology is the official journal of the International Federation of Clinical Neurophysiology, the Brazilian Society of Clinical Neurophysiology, the Czech Society of Clinical Neurophysiology, the Italian Clinical Neurophysiology Society and the International Society of Intraoperative Neurophysiology.The journal is dedicated to fostering research and disseminating information on all aspects of both normal and abnormal functioning of the nervous system. The key aim of the publication is to disseminate scholarly reports on the pathophysiology underlying diseases of the central and peripheral nervous system of human patients. Clinical trials that use neurophysiological measures to document change are encouraged, as are manuscripts reporting data on integrated neuroimaging of central nervous function including, but not limited to, functional MRI, MEG, EEG, PET and other neuroimaging modalities.