Liu A, Hahn J S, Heldt G P, Coen R W
University of California, San Diego, School of Medicine, Department of Neurosciences.
Electroencephalogr Clin Neurophysiol. 1992 Jan;82(1):30-7. doi: 10.1016/0013-4694(92)90179-l.
Neonatal seizures are a symptom of central nervous system disturbances. Neonatal seizures may be identified by direct clinical observation by the majority of electrographic seizures are clinically silent or subtle. Electrographic seizures in the newborn consist of periodic or rhythmic discharges that are distinctively different from normal background cerebral activity. Utilizing these differences, we have developed a technique to identify electrographic seizure activity. In this study, autocorrelation analysis was used to distinguish seizures from background electrocerebral activity. Autocorrelation data were scored to quantify the periodicity using a newly developed scoring system. This method, Scored Autocorrelation Moment (SAM) analysis, successfully distinguished epochs of EEGs with seizures from those without (N = 117 epochs, 58 with seizure and 59 without). SAM analysis showed a sensitivity of 84% and a specificity of 98%. SAM analysis of EEG may provide a method for monitoring electrographic seizures in high-risk newborns.
新生儿惊厥是中枢神经系统紊乱的一种症状。大多数惊厥可通过直接临床观察来识别，但多数脑电图惊厥在临床上并无表现或很隐匿。新生儿的脑电图惊厥由周期性或节律性放电组成，这些放电与正常的背景脑电活动明显不同。利用这些差异，我们开发了一种识别脑电图惊厥活动的技术。在本研究中，自相关分析被用于区分惊厥与背景脑电活动。使用新开发的评分系统对自相关数据进行评分以量化周期性。这种方法，即评分自相关矩（SAM）分析，成功地区分了有惊厥的脑电图时段和无惊厥的脑电图时段（N = 117个时段，58个有惊厥，59个无惊厥）。SAM分析显示敏感性为84%，特异性为98%。脑电图的SAM分析可能为监测高危新生儿的脑电图惊厥提供一种方法。
