Capitanio L, Jensen E W, Filligoi G C, Makovec B, Gagliardi M, Henneberg S W, Lindholm P, Cerutti S
Dept INFOCOM, Faculty of Engineering, University of Roma La Sapienza, Italy.
Methods Inf Med. 1997 Dec;36(4-5):311-4.
Achieving and monitoring adequate depth of anaesthesia is a challenge to the anaesthetist. With the introduction of muscle relaxing agents, the traditional signs of awareness are often obscured or difficult to interpret. These signs include blood pressure, heart rate, pupil size, etc. However, these factors do not describe the depth of anaesthesia, (DA), in a cerebral activity sense, hence there is a desire to achieve a better measure of the DA. Auditory Evoked Potentials (AEP) provide two aspects relevant to anaesthesia: (1) they have identifiable anatomical significance and, (2) their characteristics reflect the way in which the brain reacts to a stimulus. However, AEP is embedded in noise from the ongoing EEG background activity. Hence, processing is needed to improve the signal to noise ratio. The methods applied were moving time averaging (MTA) and ARX-modeling. The EEG was collected from the left hemisphere and analysed by FFT to 1 sec epochs and the spectral edge frequency was calculated. Both the changes in ARX extracted AEP and the spectral edge frequency of the EEG correlated well with the time interval between propofol induction and onset of anaesthesia measured by clinical signs (i.e., cessation of eye-lash reflex). The MTA extracted AEP was significantly slower in tracing the transition from consciousness to unconsciousness.
实现并监测足够的麻醉深度对麻醉医生来说是一项挑战。随着肌肉松弛剂的引入，传统的意识体征常常被掩盖或难以解读。这些体征包括血压、心率、瞳孔大小等。然而，从大脑活动的角度来看，这些因素并不能描述麻醉深度（DA），因此人们希望能更好地测量DA。听觉诱发电位（AEP）在麻醉方面有两个相关特性：（1）它们具有可识别的解剖学意义，（2）其特性反映了大脑对刺激的反应方式。然而，AEP被嵌入到持续脑电图背景活动的噪声中。因此，需要进行处理以提高信噪比。所应用的方法是移动时间平均（MTA）和自回归外生（ARX）建模。脑电图从左半球采集，通过快速傅里叶变换（FFT）分析为1秒的时段，并计算频谱边缘频率。ARX提取的AEP变化和脑电图的频谱边缘频率与通过临床体征（即睫毛反射消失）测量的丙泊酚诱导与麻醉起效之间的时间间隔都有很好的相关性。MTA提取的AEP在追踪从意识向无意识转变方面明显较慢。
