Real-time State of Vigilance Monitor for the Neonatal Intensive Care Unit

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.

Use Case: Disruption of sleep is a common experience of hospitalized patients of all ages, especially if they are in an intensive care unit (ICU); infants in the NICU often stay weeks to months. The quality of neonatal sleep is strongly associated with later neurodevelopment. Abnormal sleep quality in neonates is associated with less attention orienting at 4 months, increased distractibility, decreased developmental function at 12-24 months, and lower emotional regulation and cognitive development at age 5. Yet disruptions to sleep are frequent: the study team has found that NICU neonates are handled by staff with a median interval of 2.3 min. Handling occurred across all sleep-wake stages and frequently resulted in arousals, awakenings, and respiratory events.

The study will employ multiple levels of noise reduction and signal quality assessment, tailored to the specific needs of the algorithm and to the NICU environment. The final algorithm will include four sleep-wake stages (awake, quiet/non-REM sleep, active/REM sleep, indeterminate/transitional sleep). The complete algorithm is intended to do what no existing algorithm does, as it uniquely combines the assessment of data quality, ability to run real-time on un-curated data, and identification of four sleep-wake stages. In this observational study, validation of the monitor "read-out" in comparison to the gold standard polysomnogram happens offline, after the completion of data collection from the subject.

Because this is an observational study to develop and validate a new physiologic monitoring modality (the bedside sleep monitor), there is no subject intervention. No change in care for these study subjects is envisioned to result from use of this prototype monitor. It is hoped that the proposed technology will create a unique solution that will one day be deployed in the NICU.

Potential Future Research Use of Monitor: The study team hypothesizes that adjusting the timing of NICU patient contact to avoid multiple sleep disruptions might improve sleep in the NICU, which could then lead to better neurodevelopmental outcomes. However, the efficacy of this approach would need to be proved in randomized controlled trials. The conduct of such a future randomized clinical trial would be facilitated by availability of a real-time, objective monitor of sleep-wake states like what the study team proposes to develop. However, the present observational study serves as an initial stage of research that would lead up to that kind of trial.

Eligibility Criteria:
Inclusion Criteria:

* Admitted to the Newborn ICU in C\&W Mott Hospital
* \>/= 30 weeks gestational age at the time of birth (\>/= 33 weeks post-conceptional age at enrollment)

Exclusion Criteria:

* Any diagnosis, patient care, or anticipated patient care that is likely to interfere with the 12 hour recording or would make the recording dangerous to the participant