Abstract：
OBJECTIVE: We set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes.
METHODS: We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively.
RESULTS: After statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044).
CONCLUSIONS: This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse.
CONCLUSIONS: This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.
METHODS: We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively.
RESULTS: After statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044).
CONCLUSIONS: This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse.
CONCLUSIONS: This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.
摘要：
目的：我们着手评估癫痫性痉挛治疗的反应是否与特定的候选计算脑电图生物标志物相关，独立于临床属性。
方法：我们确定了50例癫痫性痉挛的儿童，治疗前后通宵视频脑电图。在以自动方式预处理EEG样本以去除伪影之后，我们计算了振幅，功率谱,功能连接，熵,和长程时间相关性(LRTC)。为了评估每个特征与反应和复发独立相关的程度，我们进行了逻辑和比例风险回归，分别。
结果：治疗前对癫痫性痉挛的持续时间进行统计学调整后，我们观察到反应与较强的基线和治疗后LRTC之间存在关联（分别为P=0.042和P=0.004），和较高的后处理熵（P=0.003）。在探索的基础上，无复发与较强的治疗后LRTC(P=0.006)和较高的治疗后熵(P=0.044)相关.
结论：这项研究表明，多种脑电图特征，尤其是LRTC和熵，可以预测反应和复发。
结论：这项研究代表了一个更精确的方法来测量和预测癫痫痉挛治疗反应的步骤。
方法：我们确定了50例癫痫性痉挛的儿童，治疗前后通宵视频脑电图。在以自动方式预处理EEG样本以去除伪影之后，我们计算了振幅，功率谱,功能连接，熵,和长程时间相关性(LRTC)。为了评估每个特征与反应和复发独立相关的程度，我们进行了逻辑和比例风险回归，分别。
结果：治疗前对癫痫性痉挛的持续时间进行统计学调整后，我们观察到反应与较强的基线和治疗后LRTC之间存在关联（分别为P=0.042和P=0.004），和较高的后处理熵（P=0.003）。在探索的基础上，无复发与较强的治疗后LRTC(P=0.006)和较高的治疗后熵(P=0.044)相关.
结论：这项研究表明，多种脑电图特征，尤其是LRTC和熵，可以预测反应和复发。
结论：这项研究代表了一个更精确的方法来测量和预测癫痫痉挛治疗反应的步骤。
