Mathematical modeling of level of anaesthesia from EEG measurements
Fedotenkova, Mariia (2013)
Diplomityö
Fedotenkova, Mariia
2013
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe201309175894
https://urn.fi/URN:NBN:fi-fe201309175894
Tiivistelmä
Problem of modeling of anaesthesia depth level is studied in this Master Thesis.
It applies analysis of EEG signals with nonlinear dynamics theory and further
classification of obtained values.
The main stages of this study are the following: data preprocessing; calculation
of optimal embedding parameters for phase space reconstruction; obtaining
reconstructed phase portraits of each EEG signal; formation of the
feature set to characterise obtained phase portraits; classification of four different
anaesthesia levels basing on previously estimated features. Classification
was performed with: Linear and quadratic Discriminant Analysis, k Nearest
Neighbours method and online clustering.
In addition, this work provides overview of existing approaches to anaesthesia
depth monitoring, description of basic concepts of nonlinear dynamics theory
used in this Master Thesis and comparative analysis of several different
classification methods.
It applies analysis of EEG signals with nonlinear dynamics theory and further
classification of obtained values.
The main stages of this study are the following: data preprocessing; calculation
of optimal embedding parameters for phase space reconstruction; obtaining
reconstructed phase portraits of each EEG signal; formation of the
feature set to characterise obtained phase portraits; classification of four different
anaesthesia levels basing on previously estimated features. Classification
was performed with: Linear and quadratic Discriminant Analysis, k Nearest
Neighbours method and online clustering.
In addition, this work provides overview of existing approaches to anaesthesia
depth monitoring, description of basic concepts of nonlinear dynamics theory
used in this Master Thesis and comparative analysis of several different
classification methods.