Automatic analysis and monitoring of burst suppression in anesthesia.

Objective: We studied the spectral characteristics of the EEG burst suppression patterns (BSP) of two intravenous anesthetics, propofol and thiopental. Based on the obtained results, we developed a method for automatic segmentation, classification and compact presentation of burst suppression patterns.
Methods: The spectral analysis was performed with the short time Fourier transform and with autoregressive modeling to provide information of frequency contents of bursts. This information was used when designing appropriate filters for segmentation algorithms. The adaptive segmentation was carried out using two different nonparametric methods. The first one was based on the absolute values of amplitudes and is referred to as the ADIF method. The second method used the absolute values of the Nonlinear Energy Operator (NLEO) and is referred to as the NLEO method. Both methods have been described earlier but they were modified for the purposes of BSP detection. The signal was classified to bursts, suppressions and artifacts. Automatic classification was compared with manual classification. Results. The NLEO method was more accurate, especially in the detection of artifacts. NLEO method classified correctly 94.0% of the propofol data and 92.8% of the thiopental data. With the ADIF method, the results were 90.5% and 88.1% respectively.
Conclusions: Our results show that burst suppression caused by the different anesthetics can be reliably detected with our segmentation and classification methods. The analysis of normal and pathological EEG, however, should include information of the anesthetic used. Knowledge of the normal variation of the EEG is necessary in order to detect the abnormal BSP of, for instance, seizure patients.