Sleep, REM

The ŌURA ring is a commercially available “sleep tracker” measuring and processing information from several users’ bio-signals. Rings are waterproof, made in ceramic, and come with a dedicated mobile App. They come in different sizes (US standard ring sizes 6–13) and weigh about 15 g with a battery life of about 3 days. The ring automatically connectsvia Bluetooth and transfers data to a mobile platform via the dedicatedApp.
In the current study we used the first version of Ōuraring algorithm whichwas not changed or updated during the course of the validation.We purchasedtwo ring sizes (US 7 and 11). For each participant, the finger demonstrating the best, snug fit for the ring was chosen.Twenty-one participants had the ŌURA ring on the index, 2 on the middle, 2 on the pinky, 11 on the ring and 5 on the thumb.
Sleep lab technicians assured that the PSG recording was synchronized with the ŌURA mobile App time and that there was a connection between the ŌURA ring and the ŌURA mobile App. All data from the ŌURA ring and the PSG were anonymized using ad-hoc created codes. The app allows access to the summary night data but not the EBE data. Therefore, we requested the raw data from the Ōuraring company, which agreed to provide 30s EBE data for each recording as well as technical information/support on the ŌURA ring and associated mobile App, allowing us to accurately perform EBE analysis. Each morning, the ŌURA ring data were sent to ŌURA tech staff, who subsequently provided 30s-by-30s data. Ōuraring was not involved in any other aspects of the study; Ōuraring did not have access to participant information nor access to the PSG staging.
Participants worethe ŌURA ring from the time they arrived at the lab until to the next morning and no action was required by them. The ŌURA ring collected data from the participants’ finger continuously and a proprietary algorithm determined sleep stages (wake, “light”, “deep” and REM sleep). For each night, we calculated the following parameters, which were all aligned with PSG lights-off and lights-on time to match the PSG sleep staging): sleep onset latency (ŌURA-SOL, min), time spent in “deep sleep” (ŌURA-N3, min; equivalent of PSG N3 sleep), time spent in REM sleep (ŌURA-REM, min), time spent in “light sleep” (ŌURA-N1+N2, min; equivalent of PSG N1+N2 sleep), total time spent asleep (ŌURA-TST, min; equivalent of PSG TST) and periods of wakefulness after the sleep onset (ŌURA-WASO, min; equivalent of PSG WASO). An example of a typicalparticipant’s PSG and ŌURA hypnogram (stages of sleep plotted as a function of time of the night) is provided in Figure 1.

Animals were housed on a 12-hr dark/12-hr light cycle (light on between 7:00 and 19:00). Behavioral experiments were carried out between 13:30 and 18:30. EEG and EMG electrodes were connected to flexible recording cables via a mini-connector, and recordings were made in the animal's home cage placed in a sound-attenuation box. Recordings started after >1 hr of habituation. The signals were recorded with a TDT RZ5 amplifier, filtered (1-750 Hz) and digitized at 1500 Hz. Brain states were classified into NREM sleep, REM sleep, and wakefulness using a custom-written MATLAB software. The classification was performed without any information regarding the identity of the animal or laser stimulation timing. First, we calculated the power spectrum of the EEG and EMG using a 5 s sliding window, sequentially shifted by 2.5 s increments. Next, we summed the EEG power in the ranges from 1 to 4 Hz and from 6 to 12 Hz, yielding a time dependent delta and theta power, respectively. For further analysis, we divided the theta by the delta power (theta/delta ratio). We also computed the total EMG power from 20 to 300 Hz. For each time point, we determined the brain state using a threshold algorithm. A state was classified as NREM if the delta power was lower than its mean (averaged over the whole recording session) and if the EMG power was lower than its mean plus one standard deviation. A state was classified as REM if (1) the delta power was lower than the average, (2) the theta/delta ratio deviated more than one standard deviation from its mean, and (3) the EMG power was lower than its mean plus one standard deviation. All remaining states were classified as wake. The wake state thus encompassed states with high EMG power (active awake), or low delta power without elevated EMG activity or theta/delta ratio (quiet awake). Finally, we manually verified the automatic classification to ensure that all states were correctly assigned.