Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency

In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.

A Proposal for Sleep Scoring Analysis Designed by Computer Assisted using Physiological Signals

Sleep is utterly regarded as compulsory component for a person’s prosperity and is an exceedingly important element for wellbeing of a healthy person. It is a condition in which an individual is physically and mentally at rest. The conception of sleep is considered extremely peculiar and is a topic of discussion and researchers all over the world has been attracted by this concept. Sleep analysis and its stages is analyzed to be useful in sleep research and sleep medicine area. By properly analyzing the sleep scoring system and its different stages has proven helpful for diagnosing sleep disorders. As it’s seen, sleep stage classification by manual process is a hectic procedure as it takes sufficient time for sleep experts to perform data analysis. Besides, mistakes and irregularities in between classification of same data can be recurrent. Therefore, the use of automatic scoring system in order to support reliable classification is highly in greater use. The scheduled work provides an insight to use the automatic scheme which is based on real time EMG signals and Artificial neural network. EMG is an electro neurological diagnostic tool which evaluates and records the electrical activity generated by muscle cells. The sleep scoring analysis can be applied by recording Electroencephalogram (EEG), Electromyogram (EMG), and Electrooculogram (EOG) based on epoch and this method is termed as PSG test or polysomnography test. The epoch measured has length segments for a period of 30 seconds. The standard database of EMG records was gathered from various hospitals in sleep laboratory which gives the different stages of sleep. These are Waking, Non-REM1 (stage-1), NonREM2 (stage-2), Non-REM3 (stage-3), REM. The collection of data was done for the period of 30 second known as epoch, for seven hours. The dataset obtained from the biological signal was managed so that necessary data is to be extracted from degenerated signal utilized for the purpose of study. As a matter of fact, it is known electrical signals are distributed throughout the body and is needed to be removed. These unwanted signals are termed as artifacts and they are removed with the help of filters. In this proposed work, the signal is filtered by making use of low-pass filter called Butterworth. The withdrawn characteristics were instructed and categorized by utilizing Artificial Neural Network (ANN). ANN, on the other hand is highly complicated network and utilizing same in the field of biomedical when contracted with electrical signals, acquired from human body is itself a novel. The precision obtained by the help of the procedure was discovered to be satisfactory and hence the process is very useful in clinics of sleep, especially helpful for neuro-scientists for discovering the disturbance in sleep.

A universal, open-source, high-performance tool for automated sleep staging

The creation of a completely automated sleep-scoring system that is highly accurate, flexible, well validated, free and simple to use by anyone has yet to be accomplished. In part, this is due to the difficulty of use of existing algorithms, algorithms having been trained on too small samples, and paywall demotivation. Here we describe a novel algorithm trained and validated on +27,000 hours of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy matching or exceeding human accuracy and interscorer agreement no matter the population kind. The software is easy to use, computationally low-demanding, open source, and free. Such software has the potential to facilitate broad adoption of automated sleep staging with the hope of becoming an industry standard.

253 Better and faster automatic sleep staging with artificial intelligence

Introduction Sleep staging of polysomnography (PSG) is a time-consuming task, it requires significant training, and significant variability among scorers is expected. A new software (MEBsleep by Medibio Limited) was developed to automatically perform sleep scoring based on machine learning algorithms. This study aimed to perform an extensive investigation of its agreement with expert sleep technicians. Methods Forty polysomnography recordings of patients that were referred for sleep evaluation to three sleep clinics were retrospectively collected. Three experienced technicians independently staged the recording complying with the scoring rules of the American Academy of Sleep Medicine guidelines. Positive Percent Agreement (PPA), Positive Predictive Value (PPV), and other agreement statistics between the automatic and manual staging, among the staging performed by the three technicians, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and statistical significance of the differences. Results Automatic staging took less than two minutes per PSG on a consumer laptop. The automatic staging resulted for the most comparable (PPA difference of N1, N3, and REM; PPV difference of N1, N2, N3, and REM) or statistically significantly more in agreement with the technicians’ staging than the between-technician agreement (PPA difference of N2: 3.90%, 95% bootstrap CI 1.79%-6.01%; PPV difference of Wake: 1.16%, 95% bootstrap CI 0.64%/1.67%), with the sole exception of a partial reduction in the positive percent agreement of the Wake stage (PPA difference of Wake -7.04%, 95% bootstrap CI -10.40%/-3.85%). The automatic staging also demonstrated very high accuracy in an indirect comparison with other similar software. Conclusion Given these promising results, the use of this software may support sleep clinicians by improving efficiency in sleep scoring. Support (if any):