Classification and automatic scoring of arousal intensity during sleep stages using machine learning.

Arousal during sleep can result in sleep fragmentation and various physiological effects, impairing cognitive function and raising blood pressure and heart rate. However, the current definition of arousal has limitations in assessing both amplitude and duration, making it challenging to measure sleep fragmentation accurately. Moreover, there is inconsistency among inter-raters in arousal scoring, which renders it susceptible to subjective variability. Therefore, this study aims to identify a highly accurate classifier for each sleep stage by employing optimized feature selection and machine learning models. According to electroencephalography (EEG) signals during the arousal phase, the intensity level was categorized into four levels. For control, the non-arousal cases were used as level 0 and referred as sham arousal, resulting in five arousal intensity levels. Wavelet transform was applied to analyze sleep arousal to extract features from EEG. Based on these features, we classified arousal intensity levels through machine learning algorithms. Due to the different characteristics of EEG in each sleep stage, the classification model was optimized for the four sleep stages. Excluding sham arousals, a total of 13,532 arousal events were used. The lowest intensity in the entire data, level 1, was computed to be 3107, level 2 was 3384, level 3 was 3472, and the highest intensity of level 4 was 3,569. The optimized classification model for each sleep stage achieved an average sensitivity of 82.68%, specificity of 95.68%, and AUROC of 96.30%. The sensitivity of the control, arousal intensity level 0, was 83.07%, a 1.25% increase over the unoptimized model and a 14.22% increase over previous research. This study used machine learning techniques to develop classifiers for each sleep stage, improving the accuracy of arousal intensity classification. The classifiers showed high sensitivity and specificity and revealed the unique characteristics of arousal intensity during different sleep stages. These findings represent a novel approach to arousal research and have implications for developing more accurate predictive models in sleep research.

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.