The Brain function index as a depth of anesthesia indicator using complexity measures

Abstract

Monitoring depth of anesthesia using the Electroencephalogram (EEG) is a major ongoing challenge in anesthesia research. This paper offers a real-time method based on combination of permutation entropy and burst suppression pattern ratio to calculate an index, called Brain function index (BFI), to quantify the effect of anesthetic drug on brain activity quickly and accurately. Such a method implemented in the Saadat brain function assessment module (Saadat Co., Tehran, Iran). The BFI and commercial Bispectral index (BIS) are applied to EEG signals collected from 25 patients during general surgery. The results show that both BFI and BIS track the gross changes in EEG especially at high doses of anesthetics. However, the BFI index has significant advantages as; it has an open source algorithm and doesn't involve a complex mixture of three unrelated sub-indices; it is less sensitive to the noise embedded in the EEG signal and it considerably reduces computational complexity.