Automatic detection of burst suppression.
Burst suppression pattern (BSP) as a common diffuse abnormal electroencephalographic (EEG) pattern requires close monitoring in the intensive care unit (ICU) environments. Automatic detection of individual BS events has a clinical and practical importance for brain function monitoring in the neurological ICUs (NICUs) using Continuous EEG (CEEG). In this paper, we present a novel method to automatically detect burst suppression events. The method is based on segmentation and detection of the suppression component of the BS event using integrated EEG signal across the channels of interest. Decisional rules are then applied to the suppression segments to identify the actual BS events. Additionally, algorithms were developed to identify EEG containing loose electrodes as well as those with EMG and large amplitude contaminations. The overall BS event detection sensitivity is greater than 92% with a specificity of 83% on data from 4 ICU recordings.
Wang
Yunhua
Y
Stellate, 376 Victoria Avenue, Montreal, Quebec, Canada.
Agarwal
Rajeev
R
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography; D001330:Electronic Data Processing / Q000379:methods*; D006801:Humans; D007362:Intensive Care Units; D008991:Monitoring, Physiologic / Q000379:methods*; D012680:Sensitivity and Specificity