# Cyclic Alternating Patterns of Encephalopathy (CAPE) in Acute Brain Injury Through a Quantitative Electroencephalogram (QEEG) Perspective: A Case Series

## TL;DR

This case series explores how quantitative EEG helps identify cyclic brain activity patterns in patients with acute brain injury.

## Contribution

The study demonstrates the utility of QEEG in detecting CAPE patterns in critically ill patients.

## Key findings

- QEEG effectively identifies CAPE by detecting changes in spectral power density and rhythmicity.
- Adjusting temporal resolution on QEEG enhances visibility of CAPE patterns.
- Four cases showed consistent CAPE patterns using QEEG visualization techniques.

## Abstract

Continuous EEG (cEEG) is a non-invasive bedside tool used to detect causative or contributory conditions of the encephalopathic state. By continuously recording electrical brain activity, it provides insights into background patterns, seizures, and dynamic cerebral activity, thereby aiding in the management of critically ill patients with acute brain injury.

The term 'cyclic alternating pattern of encephalopathy' (CAPE) was recently introduced to describe alternating changes in brain electrical activity observed on EEG in critically ill patients. CAPE is characterized by electrocerebral background pattern shifts lasting at least ten seconds and repeating regularly for a minimum of six cycles.

Quantitative EEG (QEEG) facilitates the interpretation of extensive cEEG datasets by applying mathematical algorithms to transform raw EEG data into time-compressed, frequency- or amplitude-based visualizations. Through Fourier analysis, QEEG decomposes the EEG signals, plotting the amplitude of different frequency bands over time, enabling easier identification of state changes such as CAPE across extended periods.

This case series highlights four critically ill patients exhibiting CAPE on cEEG, with corresponding findings illustrated via QEEG. These cases demonstrate that QEEG effectively identifies CAPE by detecting changes in spectral power density and rhythmicity across distinct states. Adjusting the temporal resolution on QEEG enhances the visibility of CAPE patterns, facilitating their recognition.