The goal of this observational study is to collect data to develop a complete package (hardware, user interface software and algorithms) that can monitor sleep-wake stages in neonates. Real-time EEG data will be used to develop and refine the prototype monitor's ability to provide direct real-time information about sleep-wake state. The study design includes multiple iterative training/testing stages to refine the prototype. The study is divided into multiple sub-aims conducted in parallel: data acquisition, algorithm development (including comparison between gold-standard polysomnogram vs. novel algorithm markings of sleep-stages), and graphical user interface software development. The data acquisition and algorithm development are iterative and linked, such that the prototype algorithm from one iteration will be deployed real-time during the next iteration of data acquisition. This allows verification that the algorithm can perform real-time and provides prospective testing data, which is later folded into the training data for the next iteration, for verification and validation of the system.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Negative predictive value (NPV) for detection of sleep (any stage)
Timeframe: 12 hours