Association Between Sleep Quality and Deep Learning-Based Sleep Onset Latency Distribution Using an Electroencephalogram.

To evaluate sleep quality, it is necessary to monitor overnight sleep duration. However, sleep monitoring typically requires more than 7 hours, which can be inefficient in termxs of data size and analysis. Therefore, we proposed to develop a deep learning-based model using a 30 sec sleep electroencephalogram (EEG) early in the sleep cycle to predict sleep onset latency (SOL) distribution and explore associations with sleep quality (SQ). We propose a deep learning model composed of a structure that decomposes and restores the signal in epoch units and a structure that predicts the SOL distribution. We used the Sleep Heart Health Study public dataset, which includes a large number of study subjects, to estimate and evaluate the proposed model. The proposed model estimated the SOL distribution and divided it into four clusters. The advantage of the proposed model is that it shows the process of falling asleep for individual participants as a probability graph over time. Furthermore, we compared the baseline of good SQ and SOL and showed that less than 10 minutes SOL correlated better with good SQ. Moreover, it was the most suitable sleep feature that could be predicted using early EEG, compared with the total sleep time, sleep efficiency, and actual sleep time. Our study showed the feasibility of estimating SOL distribution using deep learning with an early EEG and showed that SOL distribution within 10 minutes was associated with good SQ.

MEDi-SOL: Multi Ensemble Distribution Model for Estimating Sleep Onset Latency.

Sleep onset latency (SOL) is an important factor relating to the sleep quality of a subject. Therefore, accurate prediction of SOL is useful to identify individuals at risk of sleep disorders and to improve sleep quality. In this study, we estimate SOL distribution and falling asleep function using an electroencephalogram (EEG), which can measure the electric field of brain activity. We proposed a Multi Ensemble Distribution model for estimating Sleep Onset Latency (MEDi-SOL), consisting of a temporal encoder and a time distribution decoder. We evaluated the performance of the proposed model using a public dataset from the Sleep Heart Health Study. We considered four distributions, Normal, log-Normal, Weibull, and log-Logistic, and compared them with a survival model and a regression model. The temporal encoder with the ensemble log-Logistic and log-Normal distribution showed the best and second-best scores in the concordance index (C-index) and mean absolute error (MAE). Our MEDi-SOL, multi ensemble distribution with combining log-Logistic and log-Normal distribution, shows the best score in C-index and MAE, with a fast training time. Furthermore, our model can visualize the process of falling asleep for individual subjects. As a result, a distribution-based ensemble approach with appropriate distribution is more useful than point estimation.

Identification of an effective and safe bolus dose and lockout time for patient-controlled sedation (PCS) using dexmedetomidine in dental treatments: a randomized clinical trial.

Background: This study investigated a safe and effective bolus dose and lockout time for patient-controlled sedation (PCS) with dexmedetomidine for dental treatments. The depth of sedation, vital signs, and patient satisfaction were investigated to demonstrate safety. Methods: Thirty patients requiring dental scaling were enrolled and randomly divided into three groups based on bolus doses and lockout times: group 1 (low dose group, bolus dose 0.05 µg/kg, 1-minute lockout time), group 2 (middle dose group, 0.1 µg/kg, 1-minute), and group 3 (high dose group, 0.2 µg/kg, 3-minute) (n = 10 each). ECG, pulse, oxygen saturation, blood pressure, end-tidal CO2, respiratory rate, and bispectral index scores (BIS) were measured and recorded. The study was conducted in two stages: the first involved sedation without dental treatment and the second included sedation with dental scaling. Patients were instructed to press the drug demand button every 10 s, and the process of falling asleep and waking up was repeated 1-5 times. In the second stage, during dental scaling, patients were instructed to press the drug demand button. Loss of responsiveness (LOR) was defined as failure to respond to auditory stimuli six times, determining sleep onset. Patient and dentist satisfaction were assessed before and after experimentation. Results: Thirty patients (22 males) participated in the study. Scaling was performed in 29 patients after excluding one who experienced dizziness during the first stage. The average number of drug administrations until first LOR was significantly lower in group 3 (2.8 times) than groups 1 and 2 (8.0 and 6.5 times, respectively). The time taken to reach the LOR showed no difference between groups. During the second stage, the average time required to reach the LOR during scaling was 583.4 seconds. The effect site concentrations (Ce) was significantly lower in group 1 than groups 2 and 3. In the participant survey on PCS, 8/10 in group 3 reported partial memory loss, whereas 17/20 in groups 1 and 2 recalled the procedure fully or partially. Conclusion: PCS with dexmedetomidine can provide a rapid onset of sedation, safe vital sign management, and minimal side effects, thus facilitating smooth dental sedation.

Neurophysiological Response Based on Auditory Sense for Brain Modulation Using Monaural Beat.

Brain modulation is a modification process of brain activity through external stimulations. However, which condition can induce the activation is still unclear. Therefore, we aimed to identify brain activation conditions using 40 Hz monaural beat (MB). Under this stimulation, auditory sense status which is determined by frequency and power range is the condition to consider. Hence, we designed five sessions to compare; no stimulation, audible (AB), inaudible in frequency, inaudible in power, and inaudible in frequency and power. Ten healthy participants underwent each stimulation session for ten minutes with electroencephalogram (EEG) recording. For analysis, we calculated the power spectral density (PSD) of EEG for each session and compared them in frequency, time, and five brain regions. As a result, we observed the prominent power peak at 40 Hz in only AB. The induced EEG amplitude increase started at one minute and increased until the end of the session. These results of AB had significant differences in frontal, central, temporal, parietal, and occipital regions compared to other stimulations. From the statistical analysis, the PSD of the right temporal region was significantly higher than the left. We figure out the role that the auditory sense is important to lead brain activation. These findings help to understand the neurophysiological principle and effects of auditory stimulation.

2020 International brain-computer interface competition: A review.

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.