使用爆发间检测方法识别新生儿EEG中的痕量交替活动
Identifying trace alternant activity in neonatal EEG using an inter-burst detection approach
摘要：
脑电图（EEG）是一种重要的临床工具，审查睡眠-觉醒周期的新生儿在重症监护。迹线交替（TA）-在长期睡眠中安静睡眠期间EEG活动的特征模式-由短持续时间的交替周期定义，高电压活动（突发）被低电压活动（突发间）隔开。本研究提出了一种新的方法来检测TA活动，首先检测的爆发间，然后处理的爆发和爆发间的时间地图。使用来自72名健康足月新生儿的EEG记录来开发和评估1）爆发间检测方法的性能，然后将其用于2）检测TA活性。首先，使用支持向量机（SVM）组合多个振幅和频谱特征，以将TA活动内的爆发与爆发间分类，得到0.95（95%置信区间，CI：0.93至0.98）的工作特征曲线（AUC）下的中位面积。其次，连续SVM输出的后处理，置信度得分，用于产生TA包络。该包络用于检测连续EEG中的TA活性，中位AUC为0.84（95% CI：0.80 - 0.88）。这些结果验证了如何可以使用与后处理相结合的突发间检测方法来对TA活动进行分类。检测TA的存在或不存在将有助于量化临床上重要的睡眠-觉醒周期的中断。
英文摘要：
Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Trace alternant (TA)-a characteristic pattern of EEG activity during quiet sleep in term neonates-is defined by alternating periods of short-duration, high-voltage activity (bursts) separated by lower-voltage activity (inter-bursts). This study presents a novel approach for detecting TA activity by first detecting the inter-bursts and then processing the temporal map of the bursts and inter-bursts. EEG recordings from 72 healthy term neonates were used to develop and evaluate performance of 1) an inter-burst detection method which is then used for 2) detection of TA activity. First, multiple amplitude and spectral features were combined using a support vector machine (SVM) to classify bursts from inter-bursts within TA activity, resulting in a median area under the operating characteristic curve (AUC) of 0.95 (95% confidence interval, CI: 0.93 to 0.98). Second, post-processing of the continuous SVM output, the confidence score, was used to produce a TA envelope. This envelope was used to detect TA activity within the continuous EEG with a median AUC of 0.84 (95% CI: 0.80 to 0.88). These results validate how an inter-burst detection approach combined with post processing can be used to classify TA activity. Detecting the presence or absence of TA will help quantify disruption of the clinically important sleep-wake cycle.
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