A novel sleep aid device to reduce sleep latency using air-CO2 mixed gas.

Introduction: Sleep is an indispensable component of human life. However, in modern times, the number of people suffering from sleep disorders, such as insomnia and sleep deprivation, has increased significantly. Therefore, to alleviate the discomfort to the patient due to lack of sleep, sleeping pills and various sleep aids are being introduced and used. However, sleeping drugs are prescribed only to a limited extent due to the side effects posed by them and resistance to such drugs developed by patients in the long term, and the majority of sleep aids are scientifically groundless products. This study aimed to develop a device that induced sleep by spraying a mixed gas of carbon dioxide and air to create an environment that could induce sleep, similar to the inside of a sealed vehicle, to control oxygen saturation in the body. Methods: Based on the stipulated safety standards and the human tidal volume, the target concentration of carbon dioxide was determined to be of three types: 15,000, 20,000, and 25,000 ppm. After analyzing diverse structures for safely mixing gases, the most appropriate shape, the reserve tank, was selected as the best suited structure. Various variables, such as spraying angle and distance, flow rate, atmospheric temperature, and nozzle length, were comprehensively measured and tested. Furthermore based on this aspect, diffusion simulation of carbon dioxide concentration and actual experiments were conducted. To secure the stability and reliability of the developed product, an accredited test was performed to investigate the error rate of carbon dioxide concentration. Furthermore, clinical trials comprising polysomnography and questionnaires confirmed the effectiveness of the developed product not only in reducing sleep latency but also in enhancing the overall sleep quality. Results: When the developed device was put to use in reality, sleep latency was decreased by 29.01%, on average, for those with a sleep latency of 5 min or more, compared to when the device was not in use. Moreover, the total sleep time was increased by 29.19 min, WASO was decreased by 13.17%, and sleep efficiency was increased by 5.48%. We also affirmed that the ODI and 90% ODI did not decrease when the device was used. Although different questions may be raised about the safety of using a gas such as carbon dioxide (CO2), the result that tODI was not reduced shows that sleep aids using CO2 mixtures do not adversely affect human health. Discussion: The results of this study suggest a new method that can be used to treat sleep disorders including insomnia.

The simplification of the insomnia severity index and epworth sleepiness scale using machine learning models.

Insomnia and excessive daytime sleepiness (EDS) are the most common complaints in sleep clinics, and the cost of healthcare services associated with them have also increased significantly. Though the brief questionnaires such as the Insomnia Severity Index (ISI) and Epworth Sleepiness Scale (ESS) can be useful to assess insomnia and EDS, there are some limitations to apply for large numbers of patients. As the researches using the Internet of Things technology become more common, the need for the simplification of sleep questionnaires has been also growing. We aimed to simplify ISI and ESS using machine learning algorithms and deep neural networks with attention models. The medical records of 1,241 patients who examined polysomnography for insomnia or EDS were analyzed. All patients are classified into five groups according to the severity of insomnia and EDS. To develop the model, six machine learning algorithms were firstly applied. After going through normalization, the process with the CNN+ Attention model was applied. We classified a group with an accuracy of 93% even with only the results of 6 items (ISI1a, ISI1b, ISI3, ISI5, ESS4, ESS7). We simplified the sleep questionnaires with maintaining high accuracy by using machine learning models.

Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy.

Introduction: Sleep is a fundamental and essential physiological process for recovering physiological function. Sleep disturbance or deprivation has been known to be a causative factor of various physiological and psychological disorders. Therefore, sleep evaluation is vital for diagnosing or monitoring those disorders. Although PSG (polysomnography) has been the gold standard for assessing sleep quality and classifying sleep stages, PSG has various limitations for common uses. In substitution for PSG, there has been vigorous research using actigraphy. Methods: For classifying sleep stages automatically, we propose machine learning models with HRV (heart rate variability)-related features and acceleration features, which were processed from the actigraphy (Maxim band) data. Those classification results were transformed into a binary classification for estimating sleep efficiency. With 30 subjects, we conducted PSG, and they slept overnight with wrist-type actigraphy. We assessed the performance of four proposed machine learning models. Results: With HRV-related and raw features of actigraphy, Cohen's kappa was 0.974 (p < 0.001) for classifying sleep stages into five stages: wake (W), REM (Rapid Eye Movement) (R), Sleep N1 (Non-Rapid Eye Movement Stage 1, S1), Sleep N2 (Non-Rapid Eye Movement Stage 2, S2), Sleep N3 (Non-Rapid Eye Movement Stage 3, S3). In addition, our machine learning model for the estimation of sleep efficiency showed an accuracy of 0.86. Discussion: Our model demonstrated that automated sleep classification results could perfectly match the PSG results. Since models with acceleration features showed modest performance in differentiating some sleep stages, further research on acceleration features must be done. In addition, the sleep efficiency model demonstrated modest results. However, an investigation into the effects of HRV-derived and acceleration features is required.