Machine learning for (non–)epileptic tissue detection from the intraoperative electrocorticogram

IF 3.7 3区 医学 Q1 CLINICAL NEUROLOGY Clinical Neurophysiology Pub Date : 2024-08-24 DOI:10.1016/j.clinph.2024.08.012
{"title":"Machine learning for (non–)epileptic tissue detection from the intraoperative electrocorticogram","authors":"","doi":"10.1016/j.clinph.2024.08.012","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgroups affected performance, and which ioECoG features were most important.</p></div><div><h3>Methods</h3><p>We included 91 ioECoG-guided epilepsy surgery patients with Engel 1A outcome. We allocated 71 training and 20 test set patients. We trained an extra trees classifier (ETC) with 14 spectral features to classify ioECoG channels as covering resected or non-resected tissue. We compared the ETC’s performance with clinical ioECoG reading and assessed whether patient subgroups affected performance. Explainable artificial intelligence (xAI) unveiled the most important ioECoG features learnt by the ETC.</p></div><div><h3>Results</h3><p>The ETC outperformed clinical reading in five test set patients, was inferior in six, and both were inconclusive in nine. The ETC performed best in the tumor subgroup (area under ROC curve: 0.84 [95%CI 0.79–0.89]). xAI revealed predictors of resected (relative theta, alpha, and fast ripple power) and non-resected tissue (relative beta and gamma power).</p></div><div><h3>Conclusions</h3><p>Combinations of subtle spectral ioECoG changes, imperceptible by the human eye, can aid healthy and pathological tissue discrimination.</p></div><div><h3>Significance</h3><p>ML with spectral ioECoG features can support, rather than replace, clinical ioECoG reading, particularly in tumors.</p></div>","PeriodicalId":10671,"journal":{"name":"Clinical Neurophysiology","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Neurophysiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1388245724002396","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: 0

Abstract

Objective

Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgroups affected performance, and which ioECoG features were most important.

Methods

We included 91 ioECoG-guided epilepsy surgery patients with Engel 1A outcome. We allocated 71 training and 20 test set patients. We trained an extra trees classifier (ETC) with 14 spectral features to classify ioECoG channels as covering resected or non-resected tissue. We compared the ETC’s performance with clinical ioECoG reading and assessed whether patient subgroups affected performance. Explainable artificial intelligence (xAI) unveiled the most important ioECoG features learnt by the ETC.

Results

The ETC outperformed clinical reading in five test set patients, was inferior in six, and both were inconclusive in nine. The ETC performed best in the tumor subgroup (area under ROC curve: 0.84 [95%CI 0.79–0.89]). xAI revealed predictors of resected (relative theta, alpha, and fast ripple power) and non-resected tissue (relative beta and gamma power).

Conclusions

Combinations of subtle spectral ioECoG changes, imperceptible by the human eye, can aid healthy and pathological tissue discrimination.

Significance

ML with spectral ioECoG features can support, rather than replace, clinical ioECoG reading, particularly in tumors.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过机器学习从术中皮层电图检测(非)癫痫组织
目的临床视觉术中皮层电图(ioECoG)读取旨在定位癫痫组织并改善癫痫手术效果。我们旨在了解机器学习(ML)是否能补充ioECoG读图,亚组对读图性能有何影响,以及哪些ioECoG特征最重要。我们分配了 71 名训练集患者和 20 名测试集患者。我们使用 14 个频谱特征训练了一个额外树分类器(ETC),用于将 ioECoG 信道分类为覆盖切除组织或未切除组织。我们将 ETC 的性能与临床 ioECoG 读数进行了比较,并评估了患者亚群是否会影响性能。可解释人工智能(xAI)揭示了 ETC 学习到的最重要的 ioECoG 特征。结果在 5 例测试集患者中,ETC 的表现优于临床读图,在 6 例患者中不如临床读图,在 9 例患者中两者均无定论。ETC 在肿瘤亚组中表现最佳(ROC 曲线下面积:0.84 [95%CI 0.79-0.89])。xAI揭示了切除组织(相对θ、α和快速波纹功率)和未切除组织(相对β和γ功率)的预测因子。
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
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.
期刊最新文献
Polygenic mutations and their brain spatial expression contribute to presurgical evaluation in patients with refractory focal epilepsy: A case report Low-frequency EEG power and coherence differ between drug-induced parkinsonism and Parkinson’s disease Patterns of ictal surface EEG in occipital seizures: A simultaneous scalp and intracerebral recording study EEG-based responses of patients with disorders of consciousness and healthy controls to familiar and non-familiar emotional videos Effects of cervical transcutaneous spinal direct current stimulation on spinal excitability
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1