Nguyen-Ky Tai, Wen Peng Paul, Li Yan, Gray Robert
Faculty of Engineering and Surveying, Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
IEEE Trans Inf Technol Biomed. 2011 Jul;15(4):630-9. doi: 10.1109/TITB.2011.2155081. Epub 2011 May 19.
This paper evaluates depth of anesthesia (DoA) monitoring using a new index. The proposed method preconditions raw EEG data using an adaptive threshold technique to remove spikes and low-frequency noise. We also propose an adaptive window length technique to adjust the length of the sliding window. The information pertinent to DoA is then extracted to develop a feature function using discrete wavelet transform and power spectral density. The evaluation demonstrates that the new index reflects the patient's transition from consciousness to unconsciousness with the induction of anesthesia in real time.
本文使用一种新指标评估麻醉深度（DoA）监测。所提出的方法采用自适应阈值技术对原始脑电图（EEG）数据进行预处理，以去除尖峰和低频噪声。我们还提出了一种自适应窗口长度技术来调整滑动窗口的长度。然后，利用离散小波变换和功率谱密度提取与DoA相关的信息，以开发一个特征函数。评估表明，该新指标能够实时反映患者在麻醉诱导过程中从清醒到无意识的转变。
