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
Sem Hoogteijling , Eline V. Schaft , Evi H.M. Dirks , Sven Straumann , Matteo Demuru , Pieter van Eijsden , Tineke Gebbink , Willem M. Otte , Geertjan M. Huiskamp , Maryse A. van ’t Klooster , Maeike Zijlmans , the RESPect database group
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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.

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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。
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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.
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