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.