lhrkkk / anesthesia-monitoring

MATLAB; EEG; PCA; Clustering; LDS; ICA; Filtering; Discrete Wavelet Decomposition

Anesthesia-monitoring

MATLAB; EEG; Signal Processing; PCA; Clustering; LDS; ICA; Filtering; Discrete Wavelet Decomposition;

Anesthesia monitoring using EEG records based on the fact that there are some features from the EEG dependent on the anesthetic depth. One of them is the Bispectral Index Scale (BIS). As far as we know the algorithm behind BIS is based on a combination of time and frequency domain features of the EEG signal collected in the frontal scalp region. Here we propose to apply different signal processing and clustering techniques aiming to find and evaluate features related to tracking the anesthetic state and also to distinguish if a certain patient is well anaesthetised or not.