Cendejas-Zaragoza Leopoldo, Newey Christopher R, Rossi Marvin A, Wood Harrison, Hepburn Madihah
Neurology, Comprehensive Epilepsy Center, Summa Health, Akron, USA.
Neurocritical Care, Sanford USD Medical Center, Sioux Falls, USA.
Cureus. 2025 Jan 14;17(1):e77436. doi: 10.7759/cureus.77436. eCollection 2025 Jan.
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.
连续脑电图(cEEG)是一种用于检测脑病状态病因或促成因素的非侵入性床边工具。通过持续记录脑电活动,它能深入了解背景模式、癫痫发作及动态脑活动,从而有助于对急性脑损伤的重症患者进行管理。“脑病的周期性交替模式”(CAPE)这一术语最近被引入,用于描述在重症患者脑电图上观察到的脑电活动的交替变化。CAPE的特征是脑电背景模式至少持续十秒的转变,并以最少六个周期有规律地重复。定量脑电图(QEEG)通过应用数学算法将原始脑电图数据转换为基于时间压缩、频率或振幅的可视化图像,便于对大量的cEEG数据集进行解读。通过傅里叶分析,QEEG分解脑电图信号,绘制不同频段的振幅随时间变化的图,从而能更轻松地识别长时间内如CAPE等状态变化。本病例系列突出了四名在cEEG上表现出CAPE的重症患者,并通过QEEG展示了相应的发现。这些病例表明,QEEG通过检测不同状态下频谱功率密度和节律性的变化,有效地识别了CAPE。调整QEEG上的时间分辨率可增强CAPE模式的可见性,便于其识别。