Depth of anesthesia indicator using combination of complexity and frequency measures

Depth of anesthesia estimation with the Electroencephalogram (EEG) is a main current challenge in anesthesia studies. This paper proposes an original method founded on combination of permutation entropy and frequency measure to calculate an index, called Brain function index (BFI), to quantify depth of anesthesia. As EEG derived features characterize different aspects of EEG signal, it would be logical to utilize multiple features to evaluate the effect of anesthetic. Such a method implemented in the Saadat brain function assessment module (Saadat Co., Tehran, Iran). The BFI and commercial RE index as employed in the Datex-Ohmeda monitor are applied to EEG signals gathered from 18 patients during sevoflurane anesthesia. The results show that both BFI and RE indices track the changes in EEG especially at deep anesthesia state. However, the BFI index makes better response about the point of loss of consciousness and it can be derived with significantly less computational complexity. Taking into account the high accuracy of this method, an innovative EEG processing device may be extended to help the anesthetists to estimate the depth of anesthesia precisely.