Combining multiple qeeg features to estimate drug-independent sedation level using machine learning

Abstract

The present disclosure describes systems and methods of estimating sedation level of a patient using machine learning. For example, the integration of multiple QEEG features into a single sedation level estimation system using machine learning could result in a significant improvement in the predictability of the levels of sedation, independent of the sedative drug used. The present disclosure advantageously allows for the incorporation of large numbers of QEEG features and machine learning into the next-generation monitors of sedation level. Different QEEG features may be selected for different sedation drugs, such as propofol, sevoflurane and dexmedetomidine groups. The sedation level estimation system can maintain a high performance for detecting MOAA/S, independent of the drug used.

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