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A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

机译:量化相位同步的宽带方法,用于区分癫痫性脑电信号

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The nonlinear nature of phase coupling enables rich and context-sensitive interactions that characterize real brain dynamics, playing an important role in brain dysfunction such as epileptic disorders. Numerous phase synchronization (PS) measurements have been developed for seizure detection and prediction. However, the performance remains low for minor seizures in epileptic patients with an intellectual disability (ID), who are characterized by complex EEG signals associated with brain development disorders. The traditional PS measurements, e.g., phase locking index (PLI), are limited by the inability in detecting the nonlinear coupling of EEG signals and are sensitive to the background noise. This study focuses on developing a new EEG feature that can measure the nonlinear coupling, which thus would help improve seizure detection performance. We employ the correlation between probabilities of recurrence (CPR) to measure the PS on broadband EEG signals. CPR can capture the underlying nonlinear coupling of EEG signals and is robust to signal frequency and amplitude variance. The effectiveness of CPR-based features on identifying seizure EEG was evaluated on 26 epileptic patients with ID. Results show that the PS changes in seizures depend on the EEG discharge patterns including fast spike (SP), spike-wave (SPWA), wave (WA) and discharge with EMG activity (EMG). CPR-based PS decreased significantly in seizures with SP and EMG, (-0.1845 and -0.4278, with 95% CI [-0.1850, -0.1839] and [-0.4283, -0.4273], respectively), while it increases significantly in the SPWA seizures (+0.0746, with 95% CI [0.0744, 0.0749]). In addition, CPR-based PS shows potential for predicting SPWA and EMG seizures in an early manner. We conclude that CPR measurement is promising to improve seizure detection in ID patients and provides a promising method for modeling epilepsy-related brain functional networks. (C) 2018 Elsevier Ltd. All rights reserved.
机译:相耦合的非线性性质使得丰富的,对上下文敏感的交互具有真实的大脑动力学特性,在诸如癫痫病等脑功能障碍中发挥着重要作用。已经开发出许多相位同步(PS)测量方法用于癫痫发作的检测和预测。但是,对于以智力障碍(ID)为特征的癫痫患者(以脑发育障碍相关的复杂EEG信号为特征)的轻度癫痫发作,其性能仍然很低。传统的PS测量(例如锁相指数(PLI))受无法检测EEG信号的非线性耦合的限制,并且对背景噪声敏感。这项研究致力于开发一种新的EEG功能,该功能可以测量非线性耦合,从而有助于改善癫痫发作的检测性能。我们采用复发概率(CPR)之间的相关性来测量宽带EEG信号上的PS。 CPR可以捕获脑电信号的潜在非线性耦合,并且对信号频率和幅度变化具有鲁棒性。在26例ID癫痫患者中评估了基于CPR的功能在识别癫痫性脑电图中的有效性。结果表明,癫痫发作的PS变化取决于脑电图放电模式,包括快速突波(SP),突波(SPWA),波(WA)和具有EMG活动的放电(EMG)。 SP和EMG发作时,基于CPR的PS显着降低(分别为-0.1845和-0.4278,CI分别为95%[-0.1850,-0.1839]和[-0.4283,-0.4273]),而在SPWA中显着升高癫痫发作(+ 0.0746,95%CI [0.0744,0.0749])。此外,基于CPR的PS表现出早期预测SPWA和EMG发作的潜力。我们得出的结论是,CPR测量有望改善ID患者的癫痫发作检测,并为建模与癫痫相关的脑功能网络提供有希望的方法。 (C)2018 Elsevier Ltd.保留所有权利。

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