Measuring and Reflecting Depth of Anesthesia Using Wavelet and Power Spectral Density

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.