| 摘要: |
| 阐述相干平均技术、加权平均技术和小波变换技术用于麻醉监测诱发脑电信号的基本原理和具体实现过程,通过仿真实验实现用这3种方法来滤除被测信号的强噪声成分,提取中潜伏期听觉诱发脑电信号.相干平均技术简单明了,硬件容易实现;加权平均技术可以有效地减少叠代次数,但它们都需要上百次甚至上千次刺激才能提取出有效的诱发脑电信号,得到的信号有时还可能是畸变信号;而小波变换技术则在单次刺激的情况下,就能获得较高的信噪比及满意的波形特征,得到的信号的噪声仍然是白噪声,具有较高的可信度. |
| 关键词: 麻醉监测 中潜伏期听觉诱发脑电 相干平均 加权平均 小波变换 |
| DOI： |
| 投稿时间：2002-06-11修订日期：2003-09-13 |
| 基金项目:国家自然科学基金项目(69871010)资助;桂林工学院青年扶持基金资助。 |
|
| A Study of Techniques for Estimating the Evoked Potential During Anesthesia Monitoring |
|
Zhang Lieping, Mo Wei
|
| (Department of Electronic and Computer, Guilin Institute of Technology, 12 Jianganlu, Guilin, Guangxi, 541004, China) |
| Abstract: |
| This paper presents three methods for estimating signal:conventional average,weighted average and wavelet transform technology,the basic principle and realization processing of every method are also detailed The simulation results show that these methods can estimate the Mid Latency Auditory Evoked Potentials signal from strong noise background.The conventional average technology is simple and easy to be realized with hardware,The weighted average technology can reduce the iterative times effectively,but they all need hundreds of stimulation times to estimate evoked potentials which may be distortional sometimes.We can abstract signal based on single stimulation with wavelet transform technology.And the abstracted signal has a better credibility because it has better signal -to -noise ratio,satisfied shape,and above all,its noise is still white -noise. |
| Key words: anesthesia monitoring MLAEP conventional average weighted average wavelet transform |
