Home-Use and Real-Time Sleep-Staging System Based on Eye Masks and Mobile Devices with a Deep Learning Model.

PURPOSE: Sleep is an important human activity. Comfortable sensing and accurate analysis in sleep monitoring is beneficial to many healthcare and medical applications. From 2020, owing to the COVID­19 pandemic that spreads between people when they come into close physical contact with one another, the willingness to go to hospital for receiving care has reduced; care-at-home is the trend in modern healthcare. Therefore, a home-use and real-time sleep-staging system is developed in this paper. METHODS: We developed and implemented a real-time sleep staging system that integrates a wearable eye mask for high-quality electroencephalogram/electrooculogram measurement and a mobile device with MobileNETV2 deep learning model for sleep-stage identification. In the experiments, 25 all-night recordings were acquired, 17 of which were used for training, and the remaining eight were used for testing. RESULTS: The averaged scoring agreements for the wake, light sleep, deep sleep, and rapid eye movement stages were 85.20%, 87.17%, 82.87%, and 89.30%, respectively, for our system compared with the manual scoring of PSG recordings. In addition, the mean absolute errors of four objective sleep measurements, including sleep efficiency, total sleep time, sleep onset time, and wake after sleep onset time were 1.68%, 7.56 min, 5.50 min, and 3.94 min, respectively. No significant differences were observed between the proposed system and manual PSG scoring in terms of the percentage of each stage and the objective sleep measurements. CONCLUSION: These experimental results demonstrate that our system provides high scoring agreements in sleep staging and unbiased sleep measurements owing to the use of EEG and EOG signals and powerful mobile computing based on deep learning networks. These results also suggest that our system is applicable for home-use real-time sleep monitoring.

Development of a rule-based automatic five-sleep-stage scoring method for rats.

BACKGROUND: Sleep problem or disturbance often exists in pain or neurological/psychiatric diseases. However, sleep scoring is a time-consuming tedious labor. Very few studies discuss the 5-stage (wake/NREM1/NREM2/transition sleep/REM) automatic fine analysis of wake-sleep stages in rodent models. The present study aimed to develop and validate an automatic rule-based classification of 5-stage wake-sleep pattern in acid-induced widespread hyperalgesia model of the rat. RESULTS: The overall agreement between two experts' consensus and automatic scoring in the 5-stage and 3-stage analyses were 92.32% (κ = 0.88) and 94.97% (κ = 0.91), respectively. Standard deviation of the accuracy among all rats was only 2.93%. Both frontal-occipital EEG and parietal EEG data showed comparable accuracies. The results demonstrated the performance of the proposed method with high accuracy and reliability. Subtle changes exhibited in the 5-stage wake-sleep analysis but not in the 3-stage analysis during hyperalgesia development of the acid-induced pain model. Compared with existing methods, our method can automatically classify vigilance states into 5-stage or 3-stage wake-sleep pattern with a promising high agreement with sleep experts. CONCLUSIONS: In this study, we have performed and validated a reliable automated sleep scoring system in rats. The classification algorithm is less computation power, a high robustness, and consistency of results. The algorithm can be implanted into a versatile wireless portable monitoring system for real-time analysis in the future.

Estimation and prediction of drug therapy on the termination of atrial fibrillation by autoregressive model with exogenous inputs.

Atrial fibrillation (AF) is the most frequent cardiac arrhythmia seen in clinical practice. Several therapeutical approaches have been developed to terminate the AF and the effects are evaluated by the reduction of the wavelet number after the treatments. Most of the previous studies focus on modeling and analysis the mechanism, and the characteristic of AF. But no one discusses about the prediction of the result after the drug treatment. This paper is the first study to predict whether the drug treatment for AF is active or not. In this paper, the linear autoregressive model with exogenous inputs (ARX) that models the system output-input relationship by solving linear regression equations with least squares method was developed and applied to estimate the effects of pharmacological therapy on AF. Recordings (224-site bipolar recordings) of plaque electrode arrays placed on the right and left atria of pigs with sustained AF induced by rapid atrial-pacing were used to train and test the ARX models. The cardiac mapping data from twelve pigs treated with intravenous administration of antiarrhythmia drug, propafenone (PPF) or dl-sotalol (STL), was evaluated. The recordings of cardiac activity before the drug treatment were input to the model and the model output reported the estimated wavelet number of atria after the drug treatment. The results show that the predicting accuracy rate corresponding to the PPF and STL treatment was 100% and 92%, respectively. It is expected that the developed ARX model can be further extended to assist the clinical staffs to choose the effective treatments for the AF patients in the future.

A rule-based automatic sleep staging method.

In this paper, a rule-based automatic sleep staging method was proposed. Twelve features including temporal and spectrum analyses of the EEG, EOG, and EMG signals were utilized. Normalization was applied to each feature to eliminating individual differences. A hierarchical decision tree with fourteen rules was constructed for sleep stage classification. Finally, a smoothing process considering the temporal contextual information was applied for the continuity. The overall agreement and kappa coefficient of the proposed method applied to the all night polysomnography (PSG) of seventeen healthy subjects compared with the manual scorings by R&K rules can reach 86.68% and 0.79, respectively. This method can integrate with portable PSG system for sleep evaluation at-home in the near future.