Electroencephalogram Based Detection of Deep Sedation in ICU Patients Using Atomic Decomposition

Objective: This study was performed to evaluate how well states of deep sedation in 'CU patients can be detected from the frontal electroencephalogram (EEG) using features based on the method of atomic decomposition (AD). Methods: We analyzed a clinical dataset of 20 min of EEG recordings per patient from 44 mechanically ventilated adult patients receiving sedatives in an intensive care unit ('CU) setting. Several features derived from AD of the EEG signal were used to discriminate between awake and sedated states. We trained support vector machine (SVM) classifiers using AD features and compared the classification performance with SVM classifiers trained using standard spectral and entropy features using leave-one-subject-out validation. The potential of each feature to discriminate between awake and sedated states was quantified using area under the receiver operating characteristic curve (AUC). Results: The sedation level classification system using AD was able to reliably discriminate between sedated and awake states achieving an average AUC of 0.90, which was significantly better (p <; 0.05) than performance achieved using spectral (AUC = 0.86) and entropy (AUC = 0.81) domain features. A combined feature set consisting of AD, entropy, and spectral features provided better discrimination (AUC = 0.91, p <; 0.05) than any individual feature set. Conclusions: Features derived from the atomic decomposition of EEG signals provide useful discriminative information about the depth of sedation in 'CU patients. Significance: With further refinement and external validation, the proposed system may be able to assist clinical staff with continuous surveillance of sedation levels in mechanically ventilated critically ill 'CU patients.