Application of machine learning and statistics to anaesthesia detection from EEG data

Background and Objectives: The purpose of the research is to establish whether it is possible to determine the degree of anaesthesia that a laboratory animal is experiencing noninvasively. For this objective the usage of such methods of electroencephalogram (EEG) signal analysis as fast Fourier transform, K-Means machine learning method and statistical analysis is discussed. Models and Methods: The EEG data was obtained through an experiment where two groups of laboratory rats received different types of anaesthetic agent. The EEG data was normalised, then the power spectra were computed using fast Fourier transform. Next, the K-Means method was applied to classify the data in accordance with the anaesthesia degree. Statistical analysis was also conducted to describe prominent characteristics of each stage. Results: It has been shown that the proposed data analysis methods allow to distinguish between normal state, anaesthesia, and death with increasing anaesthesia dosages in laboratory animals.