جستجوی مقالات مرتبط با کلیدواژه "مراحل خواب" در نشریات گروه "پزشکی"

Accurate separation of awakening from sleep increases the accuracy of detecting sleep stages and determining sleep efficiency index that is important for medical diagnosis. In this study, 3 new bispectrum-based features were extracted, and combination of them with Bi-Phase correlation was used to detect awakening from sleep.
A gray scale image was made of electroencephalogram bispectrum amounts and converted to binary image with Otsu’s thresholding. Then, 3 features were extracted from it: total numbers of one bits, ratio of one bit in the above of the secondary diagonal to the down of it, and entropy. By combining these features with the average Bi-Phase correlation, an efficient and new way to distinguish the awakening of sleep was proposed.
Findings: The accuracy, specificity, and sensitivity were calculated. Awakening intervals could be distinguished from sleep using Bi-Phase correlation feature by the accuracy of 77.53%, and using ratio of one bit in the above of the secondary diagonal to the down of it, by the accuracy of 88.12%. Finally, combining 3 mentioned features with Bi-Phase correlation gave the ability to separate awakening from sleeping with the accuracy of 92.42%, sensitivity of 91.82%, and specificity of 93.10%.
New features have this capability to use in sleep staging. The proposed method in awakening and sleep discrimination, by combining bispectrum image-based features with Bi-Phase correlation, is better than other existing ýapproaches because of high accuracy and low calculation complexity.

Night-shift work has adverse effects on workers’ health, productivity, and occupational safety. Night workers have complaints about sleepiness, decreased performance, and sleep disorders, due to disruption of circadian rhythm function. This research aimed to determine the level and process of sleepiness among night workers of a hot industry and evaluate its correlation with oral temperature. This study was performed as a cross-sectional study on 80 night-shift workers who were exposed to heat. The sleepiness level of the subjects was measured by Stanford Sleepiness Scale and their oral temperature were recorded at different hours (from 22 pm to 4 am) for two consecutive nights. Data analysis was performed using independent and paired t-tests, one-way ANOVA, repeated measures ANOVA, and Pearson correlation coefficient. The significance level was considered to be p<0.05. In this study, an increasing linear trend over time was obsereved for sleepiness index (p<0.001). The mean of oral temperature had an increasing trend from 11 pm to 1 am and a decreasing trend afterward (p<0.001). Also, the mean sleepiness index in the measurement hours of the second night was higher than that of the first night (p<0.001), but, the mean oral temperature in the second night was onsistent with the results obtained in the first night. The sleepiness index among night-shift workers was significantly decreased with increasing oral temperature (r=0.24, p=0.03). According to the findings of this study, there was an inverse correlation between sleepiness and oral temperature after a few hours of exposure to heat. In this study, sleepiness symptoms were subjectively recorded based on the workers’ self-expression; therefore, it is suggested that paraclinical trials, such as hormonal assessments be considered.