Shin Hyun-Chool, Jia Xiaofeng, Nickl Robert, Geocadin Romergryko G, Thakor Ast Nitish V
Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
IEEE Trans Biomed Eng. 2008 Aug;55(8):1985-90. doi: 10.1109/TBME.2008.921093.
We propose an improved quantitative measure of EEG during brain injury and recovery after cardiac arrest. In our previous studies, we proposed a measure, information quantity (IQ), to detect the early effects of temperature manipulation on the EEG signals recorded from the scalp. IQ incorporates the wavelet transform and the Shannon entropy in full bands from delta to gamma. Unlike IQ, here we separately calculate IQ in each subband, i.e., the new measure is IQ in each subband. We will call it subband IQ (SIQ). We demonstrate the performance of the proposed method by comparing SIQ with IQ in terms of how well the meausres predict actual neurological outcomes. Thirteen rats, based on 7-min cardiac arrest were used. The experimental results show that the proposed measure was more highly correlated to neurological outcome than IQ.
我们提出了一种用于测量心脏骤停后脑损伤及恢复过程中脑电图(EEG)的改进定量方法。在我们之前的研究中,我们提出了一种名为信息量(IQ)的测量方法,用于检测温度调控对头皮记录的EEG信号的早期影响。IQ结合了小波变换和从δ波到γ波全频段的香农熵。与IQ不同,在此我们分别计算每个子频段的IQ,即新的测量方法是每个子频段的IQ。我们将其称为子频段IQ(SIQ)。通过比较SIQ和IQ在预测实际神经学结果方面的表现,我们展示了所提出方法的性能。使用了基于7分钟心脏骤停的13只大鼠。实验结果表明,所提出的测量方法比IQ与神经学结果的相关性更高。