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Electroencephalography-Derived Prognosis of Functional Recovery in Acute Stroke Through Machine Learning Approaches
International Journal of Neural Systems ( IF 8 ) Pub Date : 2020-09-08 , DOI: 10.1142/s0129065720500677
Antonio Maria Chiarelli 1 , Pierpaolo Croce 1 , Giovanni Assenza 2 , Arcangelo Merla 1 , Giuseppe Granata 3 , Nadia Mariagrazia Giannantoni 4 , Vittorio Pizzella 1 , Franca Tecchio 5 , Filippo Zappasodi 1
Affiliation  

Stroke, if not lethal, is a primary cause of disability. Early assessment of markers of recovery can allow personalized interventions; however, it is difficult to deliver indexes in the acute phase able to predict recovery. In this perspective, evaluation of electrical brain activity may provide useful information. A machine learning approach was explored here to predict post-stroke recovery relying on multi-channel electroencephalographic (EEG) recordings of few minutes performed at rest. A data-driven model, based on partial least square (PLS) regression, was trained on 19-channel EEG recordings performed within 10 days after mono-hemispheric stroke in 101 patients. The band-wise (delta: 1–4[Formula: see text]Hz, theta: 4–7[Formula: see text]Hz, alpha: 8–14[Formula: see text]Hz and beta: 15–30[Formula: see text]Hz) EEG effective powers were used as features to predict the recovery at 6 months (based on clinical status evaluated through the NIH Stroke Scale, NIHSS) in an optimized and cross-validated framework. In order to exploit the multimodal contribution to prognosis, the EEG-based prediction of recovery was combined with NIHSS scores in the acute phase and both were fed to a nonlinear support vector regressor (SVR). The prediction performance of EEG was at least as good as that of the acute clinical status scores. A posteriori evaluation of the features exploited by the analysis highlighted a lower delta and higher alpha activity in patients showing a positive outcome, independently of the affected hemisphere. The multimodal approach showed better prediction capabilities compared to the acute NIHSS scores alone ([Formula: see text] versus [Formula: see text], AUC = 0.80 versus AUC = 0.70, [Formula: see text]). The multimodal and multivariate model can be used in acute phase to infer recovery relying on standard EEG recordings of few minutes performed at rest together with clinical assessment, to be exploited for early and personalized therapies. The easiness of performing EEG may allow such an approach to become a standard-of-care and, thanks to the increasing number of labeled samples, further improving the model predictive power.

中文翻译:

通过机器学习方法对急性中风功能恢复的脑电图推导预测

中风,即使不是致命的,也是导致残疾的主要原因。早期评估恢复标志物可以进行个性化干预;然而,很难在急性期提供能够预测恢复的指标。从这个角度来看,对脑电活动的评估可能会提供有用的信息。此处探索了一种机器学习方法,以依靠休息时进行的几分钟的多通道脑电图 (EEG) 记录来预测中风后的恢复。基于偏最小二乘 (PLS) 回归的数据驱动模型在 101 名患者单半球卒中后 10 天内进行的 19 通道 EEG 记录上进行了训练。频带(delta:1-4[公式:见文本]Hz,θ:4-7[公式:见文本]Hz,α:8-14[公式:见文本]Hz和β:15-30[公式:见文本]Hz) 在优化和交叉验证的框架中,EEG 有效功率被用作预测 6 个月时恢复的特征(基于通过 NIH 中风量表评估的临床状态,NIHSS)。为了利用多模式对预后的贡献,将基于脑电图的恢复预测与急性期的 NIHSS 评分相结合,并将两者都输入非线性支持向量回归器 (SVR)。EEG 的预测性能至少与急性临床状态评分一样好。对分析所利用的特征的后验评估强调了在显示阳性结果的患者中较低的 delta 和较高的 alpha 活动,与受影响的半球无关。与单独的急性 NIHSS 评分相比,多模式方法显示出更好的预测能力([公式:见正文] 与 [公式:见正文],AUC = 0.80 与 AUC = 0.70,[公式:见正文])。多模式和多变量模型可用于急性期,依靠在休息时进行的几分钟标准脑电图记录以及临床评估来推断恢复,以用于早期和个性化治疗。执行 EEG 的简便性可能使这种方法成为一种护理标准,并且由于标记样本数量的增加,进一步提高了模型的预测能力。
更新日期:2020-09-08
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