Estimation of temporal scales of variation in long-term scalp electroencephalograms from epilepsy patients.
Estimation of temporal scales of variation in long-term scalp electroencephalograms from epilepsy patients.
复制标题
癫痫患者长期头皮脑电图变化时间尺度的估计。
DOI：
10.1109/embc.2013.6610145
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Chang,BernardS
中科院分区：
文献类型：
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作者：
Stamoulis,Catherine;Chang,BernardS
长期神经生理学记录，如头皮脑电图（EEG），已被常规用于旨在表征与正常生物过程（如睡眠）相关的脑活动动态变化的研究，但也越来越普遍地用于神经系统疾病（如癫痫）患者的临床评估。来自多天的非平稳记录的分析在算法效率和计算成本以及足够的维数数据减少方面提出了新的信号处理挑战。我们比较了四种方法，用于估计医学难治性癫痫患者长期记录的潜在时间动态：（i）使用最小描述长度原则的模型阶数选择，（ii）近似熵，（iii）互信息，（iv）去趋势波动分析（DFA）。个别的方法被认为是敏感的，只有特定的尺度的变化。近似熵和互信息对局部动态敏感，而动态模型阶次估计仅捕获缓慢变化的动态。DFA对多个时间尺度敏感。
Long-term neurophysiological recordings, such as scalp encephalograms (EEG), have been routinely used in studies that aim to characterize dynamic changes in brain activity associated with normal biological processes, such as sleep, but are also becoming increasingly common for clinical evaluation of patients with neurological disorders, such as epilepsy. Analysis of non-stationary recordings from multiple days poses new signal processing challenges, in regard to algorithm efficiency and computational cost, as well as adequate dimensionality data reduction. We compared four approaches for estimating the underlying temporal dynamics of long-term recordings from patients with medically refractory epilepsy: (i) model order selection using the minimum description length principle, (ii) approximate entropy, (iii) mutual information, and (iv) Detrended Fluctuation Analysis (DFA). Individual approaches were found to be sensitive only to specific scales of variation. Approximate entropy and mutual information were sensitive to local dynamics, whereas dynamic model order estimation captured only slowly varying dynamics. DFA was sensitive to multiple temporal scales.