Approximate Entropy and Wavelet Entropy based Depth of Anesthesia monitoring

Abstract

This study aims to measure Depth of Anesthesia(DoA) using non linear Electroencephalogram (EEG) signal analysis and Wavelet Analysis during anesthesia. Two features Approximate Entropy(ApEn) and Wavelet Entropy(WE) of the EEG signals were extracted as a measure of DoA from the EEG signals during four phases of general anesthesia called awake, induction, maintenance and recovery. In order to find out wavelet entropy, EEG signals during anesthesia were decomposed into its constituent frequency bands, then WE is calculated from the approximation and detail coefficients. Approximate Entropy is calculated from the respective algorithm. Finally these two extracted DoA measures were compared with BIS index, which is a commercially available DoA monitor.