• Journal of Biomedical Engineering Research
• /
• v.35 no.3
• /
• pp.55-61
• /
• 2014

In sleep monitoring system, polysomnography (PSG) is the gold-standard but previous studies revealed that attaching numerous amount of sensors disturb sleep during the test which is the fundamental disadvantage of PSG. We suggest an unconstrained rapid-eye-movement (REM) sleep monitoring method measured with polyvinylidene (PVDF) film-based sensor for the normal and the obstructive sleep apnea (OSA) patients. Nine normal subjects and seventeen OSA patients have participated in the study. During REM sleep, rate and variability of respiration are known to be greater than in other sleep stages. Based on this phenomena, respiratory signals of participants were unconstrainedly measured using the PVDF-based sensor with the PSG and REM sleep were extracted from the average rate and variability of respiration. In epoch-by-epoch REM sleep detection, proposed method classified REM sleep with an average sensitivity of 72.3%, specificity of 92.5%, accuracy of 88.9%, and kappa statistic of 0.60 compared to the results of PSG. Student's t-test showed no significant difference between the results of normal and OSA group. This method is potentially applicable to REM sleep detection in homing environment or ambulatory monitoring.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.3
• /
• pp.433-444
• /
• 2020

This study proposes a method to improve the sleep stage and efficiency estimation of sleep apnea patients using a UWB (Ultra-Wideband) radar. Motion and respiration extracted from the radar signal were used. Respiratory signal disturbances by motion artifacts and irregular respiration patterns of sleep apnea patients are compensated for in the preprocessing stage. Preprocessing calculates the standard deviation of the respiration signal for a shift window of 15 seconds to estimate thresholds for compensation and applies it to the breathing signal. The method for estimating the sleep stage is based on the difference in amplitude of two kinds of smoothed respirations signals. In smoothing, the window size is set to 10 seconds and 34 seconds, respectively. The estimated feature was processed by the k-nearest neighbor classifier and the feature filtering model to discriminate between the sleep periods of the rapid eye movement (REM) and non-rapid eye movement (NREM). The feature filtering model reflects the characteristics of the REM sleep that occur continuously and the characteristics that mainly occur in the latter part of this stage. The sleep efficiency is estimated by using the sleep onset time and motion events. Sleep onset time uses estimated features from the gradient changes of the breathing signal. A motion event was applied based on the estimated energy change in the UWB signal. Sleep efficiency and sleep stage accuracy were assessed with polysomnography. The average sleep efficiency and sleep stage accuracy were estimated respectively to be about 96.3% and 88.8% in 18 sleep apnea subjects.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1273-1279
• /
• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.

• Journal of Biomedical Engineering Research
• /
• v.35 no.6
• /
• pp.211-218
• /
• 2014

The objective of this research is to develop an automatic algorithm based on electrocardiogram (ECG) to estimate slow-wave sleep (SWS). An algorithm is based on 7 indices extracted from heart rate on ECG which simultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were then applied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. Overall Cohen's kappa, accuracy, sensitivity and specificity of the algorithm to detect 30s epochs of SWS were 0.52, 0.87, 0.70 and 0.90, respectively. The automatic SWS detection algorithm could be useful combining with existing REM and wake estimation technique on unattended home-based sleep monitoring.

• Tuberculosis and Respiratory Diseases
• /
• v.48 no.6
• /
• pp.956-963
• /
• 2000

Backgrounds : Obstructive sleep apnea syndrome(OSA) can divided into two groups, positional(PP) and non-positional(NPP) obstructive sleep apnea syndrome, according to the body position while sleeping. In this study, we evaluated the differences of anthropometric data and polysomnographic recordings between the two types of sleep apnea syndrome. Materials : Fifty patients with OSA were divided two groups by Cartwright's criteria. The supine respiratory disturbance index (RDI) was at least two times higher than the lateral RDI in the PP group, and the supine RDI was less than twice the lateral RDI in the NPP group. This patients underwent standardized polysomnographic recordings. The anthropometric data and polysomnographic data were analyzed, statistically. Results : Of all 50 patients, 30% were found to be positional OSA. BMI was significantly higher in the PP group(p<0.05). Total sleep time was significantly longer in the PP group (350.6$\pm$28.2min, 333.3$\pm$46.0min, (p<0.05). Sleep efficiency was high in the PP group(89.6$\pm$6.4%, 85.6$\pm$9.9%, p<0.05). Deep sleep was significantly higher and light sleep was lower in the PP group than in the NPP group but no difference was observed in REM sleep between the two groups. Apnea index(AI) and RDI were significantly lower( 17.0$\pm$10.6, 28.5$\pm$13.3, p<0.05) and mean arterial oxygen saturation was higher in the PP group(92.7$\pm$1.8%. p<0.05) than in the NPP group. Conclusion : Body position during sleep has a profound effect on the frequency and severity of breathing abnormalities in OSA patients. A polysomnographic evaluation for suspected OSA patients must include monitoring of the body position. Breathing function in OSA patients can be improved by controlling their obesity and through postural therapy.

• Sleep Medicine and Psychophysiology
• /
• v.9 no.1
• /
• pp.24-33
• /
• 2002

Objectives: Nasal continuous positive airway pressure (CPAP) corrected elevated blood pressure (BP) in some studies of obstructive sleep apnea syndrome (OSAS) but not in others. Such inconsistent results in previous studies might be due to differences in factors influencing the effects of CPAP on BP. The factors referred to include BP monitoring techniques, the characteristics of subjects, and method of CPAP application. Therefore, we evaluated the effects of one night CPAP application on BP and heart rate (HR) reactivity using non-invasive beat-to-beat BP measurement in normotensive and hypertensive subjects with OSAS. Methods: Finger arterial BP and oxygen saturation monitoring with nocturnal polysomnography were performed on 10 OSAS patients (mean age $52.2{\pm}12.4\;years$; 9 males, 1 female; respiratory disturbance index (RDI)>5) for one baseline night and another CPAP night. Beat-to-beat measurement of BP and HR was done with finger arterial BP monitor ($Finapres^{(R)}$) and mean arterial oxygen saturation ($SaO_2$) was also measured at 2-second intervals for both nights. We compared the mean values of cardiovascular and respiratory variables between baseline and CPAP nights using Wilcoxon signed ranks test. Delta ($\Delta$) BP, defined as the subtracted value of CPAP night BP from baseline night BP, was correlated with age, body mass index (BMI), baseline night values of BP, BP variability, HR, HR variability, mean $SaO_2$ and respiratory disturbance index (RDI), and CPAP night values of TWT% (total wake time%) and CPAP pressure, using Spearman's correlation. Results: 1) Although increase of mean $SaO_2$ (p<.01) and decrease of RDI (p<.01) were observed on the CPAP night, there were no significant differences in other variables between two nights. 2) However, delta BP tended to increase or decease depending on BP values of the baseline night and age. Delta systolic BP and baseline systolic BP showed a significant positive correlation (p<.01), but delta diastolic BP and baseline diastolic BP did not show a significant correlation except for a positive correlation in wake stage (p<.01). Delta diastolic BP and age showed a significant negative correlation (p<.05) during all stages except for REM stage, but delta systolic BP and age did not. 3) Delta systolic and diastolic BPs did not significantly correlate with other factors, such as BMI, baseline night values of BP variability, HR, HR variability, mean SaO2 and RDI, and CPAP night values of TWT% and CPAP pressure, except for a positive correlation of delta diastolic pressure and TWT% of CPAP night (p<.01). Conclusions: We observed that systolic BP and diastolic BP tended to decrease, increase or remain still in accordance with the systolic BP level of baseline night and aging. We suggest that BP reactivity by CPAP be dealt with as a complex phenomenon rather than a simple undifferentiated BP decrease.