Presenting an effective EEG-based index to monitor the depth of anesthesia

Recently, several attempts have been made to find the depth of anesthesia (DOA) by analyzing the ongoing electroencephalogram (EEG) signals during surgical operations. Nevertheless, specialists still do not rely on these indexes because they cannot accurately track the transitions of anesthetic depth. This paper presents an effective EEG-based index that is fast to compute and acts very accurate in practice. To determine the proposed index, first EEG signals are denoised with an adaptive thresholding method. The wavelet transform is then applied to the clean EEG signals in order to decompose the signal into brain-match subspaces and the proposed feature extracted from each subspace to monitor the DOA. EEG signals of 8 subjects were recorded during the surgical operation. Experimental results exhibit the proposed features highly correlated with the BIS index (the most popular EEG-based index)through different anesthetic levels. Moreover, in some cases the introduced index outperformed the BIS and the clinical observation confirmed this superiority.