Cerebral function

The toolbox allows to calculate the amplitude integrated EEG (aEEG) signal from any already recorded EEG signal. The aEEG signal is also known as cerebral function monitor signal (CFM) and is used in the neonatal intensive care unit. The aEEG signal gives a time compressed activity report of the ongoing EEG signal and has gained a widespread popularity as an alternative to the conventional EEG monitoring in neonates. aEEG monitoring is very useful for the assessment of ischemic encephalopathy, seizures and intraventricular haemorrhage.

Of any EEG channel and specific intervals the toolbox allows to calculate the aEEG signal. Therefore the EEG data is first filtered by an asymetric bandpass filter to compensate the 1/f damping of the EEG signal. This filter enhances signal components with higher frequencies. Normally the lower cut-off frequency is 2 Hz and the upper cut-off frequency is 15 Hz. Then each sample value is rectified and averaged over a time interval - this is the aEEG or CFM signal. The aEEG is plotted between 1µV and 10 µV on a linear and between 10 µV and 100 µV on a logarithmic scale. Therefore lower amplitudes are easier to interpret. Traditionally the aEEG is plotted as 6 cm per hour. Thus a whole night can be visualized on only 3 A4 pages and easily be investigated.
 

The toolbox allows to classify the aEEG signal automatically into specific patterns. Therefore the aEEG signal is divided into segments of e.g. 10 minutes. The maximum of each interval is defined as the 95 % percentile and the minimum as the 5 % percentile and both are indicated by the red horizontal line in the figure above. Then one pattern is assigned to each of the segments according to definition published by Guger, Klebermass and Olischar:

A new and improved classification algorithm was implemented in order to assign these patterns automatically. Therefore initially 10 training subjects were scored by experts and then the new algorithm was trained to classify as accurate as possible the aEEG traces. After reaching an accuracy of >80% the algorithm was applied on CFM signals from 10 testing subjects (S11-S20). The testing subjects were also scored by the experts.

The colors indicate the assigned patterns to the aEEG signal. About 15 % were CVP, 31 % DHVP,...

The table shows the overlap between the automatic classification with the experts. 77 % overlap was achieved for a segment length of 10 min for all subjects. 80.9 % were reached for a 5 min segmentation.

Very important is that even unseen data show high overlap with the classification done by experts (>80 %). The new method can easily be used in practice and improves and simplifies the aEEG annotation. This supports also the fast interpretation of the CFM signals. The expert identifies immediately the interesting parts of the CFM trace and can more easily investigate these parts in detail.

C. Guger, K. Klebermass, M. Olischar, G. Edlinger, Automatic classification of amplitude integrated EEG patterns, Biomedical Engineering, submitted, 2008.