Dax Josef F, Müller-Putz Gernot R, Pfurtscheller Klaus, Urlesberger Berndt, Müller Wilhelm, Pfurtscheller Gert
Institut für Maschinelles Sehen und Darstellen, BCI-Lab, Technische Universität Graz, Inffeldgasse 16a, 8010 Graz, Osterreich.
Biomed Tech (Berl). 2005 Jan-Feb;50(1-2):19-24. doi: 10.1515/BMT.2005.004.
Recordings of the electroencephalogram (EEG) and of the heart rate variability (HRV) of preterm neonates can give important information on the actual state of the nervous system. Both signals, EEG and HRV, are affected by parameters such as gestational age, stage of maturation and behavioral state. This work describes a method for automatic detection of slow wave EEG-bursts and a tool to average changes in the EEG and the corresponding heart rate. The detection is based on the hjorth activity (HA), calculated from the EEG. HA spikes (HAS) are identified by the determination of the beginning and end of existing spikes. HAS maxima and the time between two consecutive HAS are the basis for the triggering of the bursts. EEG power and time synchronized HR changes are averaged with a time window length of 20 s. Resultant, HR increase and duration are determined. These parameters, obtained by the automatic detection, proved to be comparable to the results of an expert.
对早产儿的脑电图(EEG)和心率变异性(HRV)进行记录,可以提供有关神经系统实际状态的重要信息。EEG和HRV这两种信号都会受到胎龄、成熟阶段和行为状态等参数的影响。这项工作描述了一种自动检测慢波EEG爆发的方法以及一种对EEG和相应心率变化进行平均的工具。该检测基于从EEG计算得出的约尔特活动(HA)。通过确定现有尖峰的开始和结束来识别HA尖峰(HAS)。HAS最大值以及两个连续HAS之间的时间是触发爆发的基础。EEG功率和时间同步的心率变化以20秒的时间窗长度进行平均。由此确定心率增加和持续时间。通过自动检测获得的这些参数被证明与专家的结果具有可比性。