Goel V, Brambrink A M, Baykal A, Koehler R C, Hanley D F, Thakor N V
Department of Biomedical Engineering, Johns Hopkins University, School of Medicine, Baltimore, MD 21205, USA.
IEEE Trans Biomed Eng. 1996 Nov;43(11):1083-92. doi: 10.1109/10.541250.
A new method of monitoring an analyzing electroencephalogram (EEG) signals during brain injury is presented. EEG signals are modeled using the autoregressive (AR) technique to obtain the frequencies where there are peaks in the spectrum. The powers at these dominant frequencies are analyzed to reveal the state of brain injury during an experimental study involving progressive hypoxia, asphyxia, and recovery. Neonatal piglets (n = 8) were exposed to a sequence of 30 min of hypoxia (10% oxygen), 5 min of room air, and 7 min of asphyxia. They then received cardiopulmonary resuscitation and were subsequently monitored for 4 h. An optimal AR model order of six was obtained for these data, resulting in three dominant frequencies. These dominant frequencies, referred to as the low, medium, and high frequency components, fell in the bands 1.0-5.5 Hz, 9.0-14.0 Hz, and 18.0-21.0 Hz, respectively. A remarkable feature of our data is the spectral dispersion, or diverging trends in the three frequency bands. During hypoxia, the relative powers of the medium and high-frequency components of EEG increased up to 160% and 176%, from their respective baseline values. During the first minute of asphyxia the medium- and high-frequency powers (relative to baseline) increased by 280-400%. The power in three frequency components went down to nearly zero within 40-80 s of asphyxia. During recovery, the phenomenon of burst-suppression was clearly exhibited in the low-frequency component. A new index, called mean normalized separation, representing the degree of disproportionality in the recovery of powers of the three dominant components relative to their mean recovered power, is presented as a possible single indicator of electrical function recovery. In conclusion, dominant frequency analysis helps reveal the brain's graded electrical response to injury and recovery.
本文提出了一种在脑损伤期间监测和分析脑电图（EEG）信号的新方法。利用自回归（AR）技术对EEG信号进行建模，以获取频谱中出现峰值的频率。在一项涉及渐进性缺氧、窒息和恢复的实验研究中，分析这些主导频率处的功率，以揭示脑损伤的状态。将新生仔猪（n = 8）暴露于30分钟的缺氧（10%氧气）、5分钟的室内空气和7分钟的窒息序列中。然后对它们进行心肺复苏，并随后监测4小时。针对这些数据获得了最佳的六阶AR模型，产生了三个主导频率。这些主导频率分别称为低频、中频和高频成分，分别落在1.0 - 5.5 Hz、9.0 - 14.0 Hz和18.0 - 21.0 Hz频段内。我们数据的一个显著特征是频谱离散，即三个频段的发散趋势。在缺氧期间，EEG的中频和高频成分的相对功率相对于各自的基线值分别增加了160%和176%。在窒息的第一分钟内，中频和高频功率（相对于基线）增加了280 - 400%。在窒息40 - 80秒内，三个频率成分的功率降至几乎为零。在恢复期间，低频成分中明显出现了爆发抑制现象。提出了一个新的指标，称为平均归一化分离度，它表示三个主导成分的功率恢复相对于其平均恢复功率的不成比例程度，作为电功能恢复的一个可能的单一指标。总之，主导频率分析有助于揭示大脑对损伤和恢复的分级电反应。
