ABSTRACT
We proposed a multi-resolution-wavelet-transform based method to extract brainstem auditory evoked potential (BAEP) from the background noise and then to identify its characteristics correctly. Firstly we discussed the mother wavelet and wavelet transform algorithm and proved that bi-orthogonal wavelet bior5. 5 and stationary discrete wavelet transform (SWT) were more suitable for BAEP signals. The correlation analysis of D6 scale wavelet coefficients between single trails and the ensemble average of all trails showed that the trails with good correlation (> 0. 4) had higher signal-to-noise ratio, so that we could get a clear BAEP from a few trails by an average and wavelet filter method. Finally, we used this method to select desirable trails, extracted BAEP from every 10 trails and calculated the I-V inter-waves' latency. The results showed that this strategy of trail selection was efficient. This method can not only achieve better de-noising effect, but also greatly reduce the stimulation time needed as well.
Subject(s)
Humans , Algorithms , Brain Mapping , Evoked Potentials, Auditory, Brain Stem , Wavelet AnalysisABSTRACT
Anesthesia as a necessary procedure in the process of surgical operation could restrain the response of patients to the damage stimulation; However, improper anesthesia could also result in severe misfortune for patients. At the present time, one kind of monitor technology assuring highly effectual anesthesia is exigently required in clinical practice and many researchers have actively undertaken investigations to seek the parameters predicting the depth of anesthesia (DOA). Electroencephalogram (EEG) assumes a dominant position in the current researches on detecting the depth of anesthesia. In this paper, the achievements of detecting the depth of anesthesia by means of EEG are systematically reviewed and the potentials are anticipated.