Miyakoshi Makoto, Nariai Hiroki, Rajaraman Rajsekar R, Bernardo Danilo, Shrey Daniel W, Lopour Beth A, Sim Myung Shin, Staba Richard J, Hussain Shaun A
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, United States.
David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States.
Epilepsy Res. 2021 Dec;178:106809. doi: 10.1016/j.eplepsyres.2021.106809. Epub 2021 Nov 7.
Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake.
Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC).
The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85).
We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.
脑电图中的δ-γ相位-振幅耦合有助于定位癫痫源并评估婴儿痉挛症患儿的严重程度。我们（1）开发了一种自动化脑电图预处理流程，使用伪迹子空间重建（ASR）和独立成分分析（ICA）来清理数据，以及（2）评估δ-γ调制指数（MI），作为区分癫痫痉挛患儿（病例组）与正常对照组在睡眠和清醒状态下的一种方法。
使用来自100名受试者的400个头皮脑电图数据集（200个睡眠数据，200个清醒数据），我们在应用高通和线噪声滤波器后（Clean 0），以及在ASR之后，采用保守（Clean 1）或严格（Clean 2）的伪迹独立成分剔除方法后，计算MI。使用MI对病例组和对照组进行分类，并通过受试者操作特征（ROC）进行评估，以获得曲线下面积（AUC）。
伪迹剔除算法使原始信号方差分别降低了29%-45%（Clean 1）和38%-60%（Clean 2）。无论有无预处理，从睡眠数据中得出的MI都能可靠地对两组进行分类（所有AUC>0.98）。相比之下，只有在Clean 2之后，使用从清醒数据中得出的MI进行组分类才成功（AUC = 0.85）。
我们开发了一种自动化脑电图预处理流程，以进行伪迹剔除并量化δ-γ调制指数。
