Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis
Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis
复制标题
DOI：
10.3389/fnhum.2015.00189
复制
发表时间：
2015-04-23
影响因子：
2.9
通讯作者：
Vanhatalo, Sampsa
中科院分区：
文献类型：
--
作者：
Matic, Vladimir;Cherian, Perumpillichira Joseph;Vanhatalo, Sampsa
定量和客观的评估背景脑电图（EEG）在生病的新生儿仍然是一个日常的临床挑战。我们研究了是否可以在新生儿EEG中使用去趋势波动分析（DFA）量化的长程时间相关性来区分背景EEG活动中不同级别的异常。收集34例围产期窒息新生儿的长期脑电图记录，并在1h时段（每个新生儿8h）将其背景评分为轻度、中度和重度。我们将DFA应用于15分钟长的非重叠EEG时期（n = 1088），从3到8 Hz过滤。我们的正式可行性研究表明，DFA指数可以可靠地评估只有部分的EEG时期，只有在相对较短的时间尺度（10-60秒），而它变得模糊，如果考虑更长的时间尺度。这促使进一步探索用于量化多重分形DFA（MF-DFA）的范例是否可以以更有效的方式应用，以及MF-DFA范例的度量是否可以产生与现有临床EEG分级的有用基准。MF-DFA指标的比较显示三个视觉评估的背景EEG等级之间存在显著差异。MF-DFA参数也显着相关的间歇期定量与我们以前开发的自动检测器。最后，我们试行了MF-DFA指标的监测应用程序，并显示了它们在患者从窒息中恢复过程中的演变。我们的探索性研究表明，新生儿脑电图可以量化使用多重分形度量，这可能提供一个合适的参数来量化的EEG背景的等级，或监测在长期的大脑监测过程中发生的大脑状态的变化。
A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.