Automatic detection and analysis of the EEG sharp wave-slow wave patterns evoked by fluorinated inhalation anesthetics.

Objective: The aim of this study was to develop a method for the automatic detection of sharp wave-slow wave (SWSW) patterns evoked in EEG by volatile anesthetics and to identify the patterns' characteristics.

Methods: The proposed method consisted in the k-NN classification with a reference set obtained using expert knowledge, the morphology of the EEG patterns and the condition for their synchronization. The decision rules were constructed and evaluated using 24h EEG records in ten patients.

Results: The sensitivity, specificity and selectivity of the method were 0.88 ± 0.10, 0.81 ± 0.13 and 0.42 ± 0.16, respectively. SWSW patterns' recruitment was strictly dependent on anesthetic concentration. SWSW patterns evoked by different types of anesthetics expressed different characteristics.

Conclusions: Synchronization criterion and adequately selected morphological features of "slow wave" were sufficient to achieve the high sensitivity and specificity of the method.

Significance: The monitoring of SWSW patterns is important in view of possible side effects of volatile anesthetics. The analysis of SWSW patterns' recruitment and morphology could be helpful in the diagnosis of the anesthesia effects on the CNS.