Monitoring the depth of anesthesia using discrete wavelet transform and power spectral density

Abstract

This method combines wavelet techniques and power spectral density to monitor the depth of anesthesia (DOA) based on simplified EEG signals. After decomposing electroencephalogram (EEG) signals, the power spectral density is chosen as a feature function for coefficients of discrete wavelet transform. By computing the mean and standard deviation of the power spectral density values, we can classify the EEG signals to three classes, corresponding with the BIS values of 0 to 40, 40 to 60, and 60 to 100. Finally, three linear functions are proposed to compute DOA values.