Neonatal EEG signal characteristics using time frequency analysis

Time-frequency analysis is a way to represent the energy contents of a signal in the joint time-frequency domain. It provides a good visual way to separate the frequency contents of a multi-component signal, and display the changes of these components with respect to time. This paper outlines investigative work on neonatal EEG signals using time-frequency analysis. The Cohen’s class distributions are discussed, and kernel optimisation for the Cohen’s class distributions is outlined. Segments of EEG with different background continuity states are analysed using a Cohen’s class distribution, and their characteristics are discussed. Through this paper, interesting information that offers insight towards the EEG signal can be visualized from the time frequency analysis.