Implementation of Distracted Estimation System based on Sensor Fusion through Correlation Analysis with Concentration.

Sitting for an extended time may cause a serious chronic disease such as a musculoskeletal disorder, or a cardiovascular disease, diabetes, or obesity. Because a consistently improper posture from early childhood to adolescence can have a number of undesirable effects on the formation of the musculoskeletal structure, learning to maintain a correct posture should be emphasized. A consistently improper posture can not only cause physical problems, it may also lead to emotional issues such as distractions, an attention deficit, and hyperactivity, and the possibility of a low efficiency and performance on assignments is high when the students have a low concentration. The present study implemented a distracted estimation system based on sensor fusion through correlation analysis with concentration that could estimate the level of distraction and prevent musculoskeletal diseases caused by poor sitting posture habits in daily life. The implemented system was designed in the form of a sitting cushion to reflect the ethological movements and characteristics of a sitting position that modern people spend a large amount of time in, and can be easily applied to existing chairs. Both algorithms installed in the system detected the center of gravity of the seated person and displayed positional changes that occurred based on the intensity of the postural changes when moving; thus, simultaneous determination of posture and impulsive behavior was possible. To evaluate the system performance, a posture determination evaluation was conducted, along with distraction estimation according to the rate of changes in posture that occur in everyday life. In addition, to evaluate performance in daily life, a movie-watching scenario was set up, and the distracted-limit estimation and concentration indices according to the rate of changes in posture were comparatively evaluated by reviewing a video of the subjects. The results of the posture determination performance evaluation through 100 posture repetitions on 10 subjects showed a high detection performance of 99.04%. The Pearson's correlation coefficient results showed a high correlation coefficient (inverse) of r = -0.975076 and a P-VALUE =   1.654 × 10 - 6 . This experiment objectively confirmed the correlation between the DLE Index (based on postural change) and the CI Index (based on EEG).

An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare.

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

Human Heart Rhythms Synchronize While Co-sleeping.

Human physiological systems have a major role in maintenance of internal stability. Previous studies have found that these systems are regulated by various types of interactions associated with physiological homeostasis. However, whether there is any interaction between these systems in different individuals is not well-understood. The aim of this research was to determine whether or not there is any interaction between the physiological systems of independent individuals in an environment where they are connected with one another. We investigated the heart rhythms of co-sleeping individuals and found evidence that in co-sleepers, not only do independent heart rhythms appear in the same relative phase for prolonged periods, but also that their occurrence has a bidirectional causal relationship. Under controlled experimental conditions, this finding may be attributed to weak cardiac vibration delivered from one individual to the other via a mechanical bed connection. Our experimental approach could help in understanding how sharing behaviors or social relationships between individuals are associated with interactions of physiological systems.

Decreased regional brain activity in response to sleep-related sounds after cognitive behavioral therapy for psychophysiological insomnia.

AIM: Patients with psychophysiological insomnia (PI) experience hyperarousal, especially as a reaction to sound stimuli. In the current study, we explored brain activity changes in response to sleep-related sounds (SS) in patients with insomnia after cognitive behavioral therapy for insomnia (CBT-I). METHODS: In 14 drug-free PI patients, regional brain activity in response to SS, and to white noise sound (NS) as neutral stimuli, was investigated before and after individual CBT-I using functional magnetic resonance imaging. Blood oxygen level-dependent (BOLD) signals to SS and NS were compared before and after CBT-I. In addition, the association between clinical improvement after CBT-I and changes in brain activity in response to SS and NS was analyzed. RESULTS: Compared with baseline, regional brain activity in response to SS after CBT-I decreased in the left middle temporal and left middle occipital gyrus. In regression analysis, a reduction in the Dysfunctional Beliefs and Attitudes about Sleep (DBAS) Scale score after CBT-I was associated with decrease in brain activity in response to SS in both thalami. However, brain activity in response to NS showed no BOLD signal changes and no association with DBAS change. CONCLUSION: Cortical hyperactivity, which may cause hyperarousal in PI, was found to decrease after CBT-I. CBT-I targeting changes in beliefs and attitudes about sleep may induce its therapeutic effects by reducing thalamic brain activity in response to sleep-related stimuli.

Sleep-Dependent Directional Coupling of Cardiorespiratory System in Patients With Obstructive Sleep Apnea.

OBJECTIVE: Cardiorespiratory interactions have been widely investigated in different physiological states and conditions. Various types of coupling characteristics have been observed in the cardiorespiratory system; however, it is difficult to identify and quantify details of their interaction. In this study, we investigate directional coupling of the cardiorespiratory system in different physiological states (sleep stages) and conditions, i.e., severity of obstructive sleep apnea (OSA). METHODS: Directionality analysis is performed using the evolution map approach with heartbeats acquired from electrocardiogram and abdominal respiratory effort measured from the polysomnographic data of 39 healthy individuals and 24 mild, 21 moderate, and 23 severe patients with OSA. The mean phase coherence is used to confirm the weak and strong coupling of cardiorespiratory system. RESULTS: We find that unidirectional coupling from the respiratory to the cardiac system increases during wakefulness (average value of -0.61) and rapid eye movement sleep (-0.55). Furthermore, unidirectional coupling between the two systems significantly decreases during light (-0.52) and deep sleep, which is further decreased in deep sleep (-0.46), approaching bidirectional coupling. In addition, unidirectional coupling from the respiratory to the cardiac system also significantly increases according to the severity of OSA. CONCLUSION: These coupling characteristics in different states and conditions are believed to be linked with autonomic nervous modulation. SIGNIFICANCE: Our approach could provide an opportunity to understand how integrated systems cooperate for physiological functions under internal and external environmental changes, and how abnormality in one physiological system could develop to increase the risk of other systemic dysfunctions and/or disorders.

Wakefulness evaluation during sleep for healthy subjects and OSA patients using a patch-type device.

OBJECTIVES: Obstructive sleep apnea (OSA) is a major sleep disorder that causes insufficient sleep, which is linked with daytime fatigue and accidents. Long-term sleep monitoring can provide meaningful information for patients with OSA to prevent and manage their symptoms. Even though various methods have been proposed to objectively measure sleep in ambulatory environments, less reliable information was provided in comparison with standard polysomnography (PSG). Therefore, this paper proposes an algorithm for distinguishing wakefulness from sleep using a patch-type device, which is applicable for both healthy individuals and patients with OSA. METHODS: Electrocardiogram (ECG) and 3-axis accelerometer signals were gathered from the single device. Wakefulness was determined with six parallel methods based on information about movement and autonomic nervous activity. The performance evaluation was conducted with five-fold cross validation using the data from 15 subjects with a low respiratory disturbance index (RDI) and 10 subjects with high RDI. In addition, wakefulness information, including total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO), were extracted from the proposed algorithm and compared with those from PSG. RESULTS: According to epoch-by-epoch (30 s) analysis, the performance results of detecting wakefulness were an average Cohen's kappa of 0.60, accuracy of 91.24%, sensitivity of 64.12%, and specificity of 95.73%. Moreover, significant correlations were observed in TST, SE, SOL, and WASO between the proposed algorithm and PSG (p < 0.001). CONCLUSIONS: Wakefulness-related information was successfully provided using data from the patch-type device. In addition, the performance results of the proposed algorithm for wakefulness detection were competitive with those from previous studies. Therefore, the proposed system could be an appropriate solution for long-term objective sleep monitoring in both healthy individuals and patients with OSA.

Slow-Wave Sleep Estimation for Healthy Subjects and OSA Patients Using R-R Intervals.

We developed an automatic slow-wave sleep (SWS) detection algorithm that can be applied to groups of healthy subjects and patients with obstructive sleep apnea (OSA). This algorithm detected SWS based on autonomic activations derived from the heart rate variations of a single sensor. An autonomic stability, which is an SWS characteristic, was evaluated and quantified using R-R intervals from an electrocardiogram (ECG). The thresholds and the heuristic rule to determine SWS were designed based on the physiological backgrounds for sleep process and distribution across the night. The automatic algorithm was evaluated based on a fivefold cross validation using data from 21 healthy subjects and 24 patients with OSA. An epoch-by-epoch (30 s) analysis showed that the overall Cohen's kappa, accuracy, sensitivity, and specificity of our method were 0.56, 89.97%, 68.71%, and 93.75%, respectively. SWS-related information, including SWS duration (min) and percentage (%), were also calculated. A significant correlation in these parameters was found between automatic and polysomnography scorings. Compared with similar methods, the proposed algorithm convincingly discriminated SWS from non-SWS. The simple method using only R-R intervals has the potential to be utilized in mobile and wearable devices that can easily measure this information. Moreover, when combined with other sleep staging methods, the proposed method is expected to be applicable to long-term sleep monitoring at home and ambulatory environments.

Real-Time Automatic Apneic Event Detection Using Nocturnal Pulse Oximetry.

OBJECTIVE: Nocturnal pulse oximetry has been proposed as a simpler alternative to polysomnography in diagnosing sleep apnea. However, existing techniques are limited in terms of inability to provide time information on sleep apnea occurrence. This study aimed to propose a new strategy for near real-time automatic detection of apneic events and reliable estimation of apnea-hypopnea index using nocturnal pulse oximetry. METHODS: Among 230 polysomnographic recordings with apnea-hypopnea index values ranging from 0 to 86.5 events/h, 138 (60%) and the remaining 92 recordings (40%) were categorized as training and test sets, respectively. By extracting the quantitative characteristics caused by the apneic event for the amount and duration of the change in blood oxygen saturation value, we established the criteria to determine the occurrence of apneic event. Regression modeling was used to estimate the apnea-hypopnea index from the apneic event detection results. RESULTS: The minute-by-minute apneic segment detection exhibited an average accuracy of 91.0% and an average Cohen's kappa coefficient of 0.71. Between the apnea-hypopnea index estimations and reference values, the mean absolute error was 2.30 events/h. The average accuracy of our diagnosis of sleep apnea was 96.7% for apnea-hypopnea index cutoff values of ≥5, 10, 15, and 30 events/h. CONCLUSION: We developed an effective strategy to detect apneic events by using morphometric characteristics in the fluctuation of blood oxygen saturation values. SIGNIFICANCE: Our study could be potentially useful in home-based multinight apneic event monitoring for purposes of therapeutic intervention and follow-up study on sleep apnea.

Differential Effects of Obesity on Obstructive Sleep Apnea Syndrome according to Age.

OBJECTIVE: To evaluate the effect of obesity on obstructive sleep apnea syndrome (OSAS) by age in relation to anthropometric measurements. METHODS: The medical records of 1,110 participants diagnosed with OSAS were analyzed according to age. All participants underwent nocturnal polysomnography and had their body mass index, waist circumference, neck circumference (NC), and waist-to-hip ratio measured. RESULTS: According to the multiple linear regression analysis model for the natural logarithm of the apnea-hypopnea index treating all four anthropometric measurements and gender as covariates, the final stepwise model accounted for an increasing percentage of the variability in the severity of OSAS as a function of age: 7.0, 9.1, 14.5, and 25.6% for those aged <30, 30-39, 40-49, and 50-59 yrs, respectively. It accounted for a decreased percentage among those 60-69 (20.3%) and 70 yrs or older (3.9%). The correlation between NC and the severity of OSAS linearly increased as a function of age for those aged 30-59 yrs, peaked among those in their 60s, and dramatically decreased thereafter. CONCLUSION: Middle-aged patients with OSAS were more likely to be obese, as measured by anthropometric measurements, than were younger or older OSAS patients. In particular, the predictive value of NC was significantly lower for younger and older OSAS patients.

Periodic Limb Movements in Sleep is Associated with Increased Mortality.

OBJECTIVE: To elucidate the association between periodic limb movements in sleep (PLMS) and mortality. METHODS: Nocturnal polysomnographic recordings of 1,344 subjects obtained from 1995 to 2008 were reviewed. The subjects were divided into four groups based on PLMS and insomnia: reference group (PLMS≤5), insomnia group (PLMS≤5 with insomnia symptoms), 515 group. We searched each subject's Identification Number in the death records from the Statistics of Korea, the national bureau of statistics, to determine deaths in the cohort that occurred prior to December, 2013. Cox-proportional hazard regression and Kaplan-Meier survival curve analyses were used to compare mortality among the four groups. RESULTS: Hazard ratios (HRs) in the 515 groups were significantly higher than that in the reference group before adjusting for age and gender [HR, 3.37; 95% confidence interval (CI), 1.73-6.55; p<0.001; HR, 5.77; 95% CI, 3.24-10.29; p<0.001]. Only the PLMS>15 group had a higher mortality rate than that in the reference group after adjusting for age, gender, and sleep efficiency (HR, 1.99; 95% CI, 1.06-2.21; p=0.033). CONCLUSION: These results suggest that PLMS may be associated with increased mortality.

Exploration of changes in the brain response to sleep-related pictures after cognitive-behavioral therapy for psychophysiological insomnia.

Psychophysiological insomnia (PI) includes arousal to sleep-related stimuli (SS), which can be treated by cognitive behavioral therapy for insomnia (CBT-I). The present study was an exploratory, prospective intervention study that aimed to explore brain response to visual SS in PI before and after CBT-I. Blood oxygen level dependent (BOLD) signal differences in response to SS and neutral stimuli (NS) were compared between 14 drug-free PI patients and 18 good sleepers (GS) using functional magnetic resonance imaging (fMRI). BOLD changes after CBT-I in patients were also examined. PI patients showed higher BOLD activation to SS in the precentral, prefrontal, fusiform, and posterior cingulate cortices before CBT-I. The increased responses to SS were reduced after CBT-I. The increased response to SS in the precentral cortex was associated with longer wake time after sleep onset (WASO), and its reduction after CBT-I was associated with improvements in WASO. Clinical improvements after CBT-I were correlated with BOLD reduction in the right insula and left paracentral cortex in response to SS. PI showed hyper-responses to SS in the precentral cortex, prefrontal cortex, and default mode network and these brain hyper-responses were normalized after CBT-I. CBT-I may exert its treatment effects on PI by reducing hyper-responses to SS in the precentral cortex and insula.

Depressive symptoms are associated with poor sleep quality rather than apnea-hypopnea index or hypoxia during sleep in patients with obstructive sleep apnea.

PURPOSE: We examined factors, including the severity of obstructive sleep apnea (OSA), that were associated with depressive symptoms and excessive daytime sleepiness in OSA patients. METHODS: We retrospectively reviewed data of 1203 subjects (861 men, mean age 48.2 years) who completed self-reported questionnaires and underwent polysomnography between September 2005 and May 2016 at the Center for Sleep and Chronobiology at Seoul National University Hospital. We compared scores on the Beck Depression Inventory (BDI) and Epworth sleepiness scale (ESS) between OSA and control groups. Analyses for OSA subgroups were classified by severity, and partial correlations were performed. RESULTS: BDI scores were higher in the OSA than in the control group (12.3 ± 7.6 vs. 10.1 ± 6.2, p = 0.001). There were differences in ESS scores among the mild, moderate, and severe OSA subgroups (p < 0.001), but not in BDI scores (p = 0.236). After adjusting for age, gender, and BMI, BDI in the OSA group correlated positively with wake after sleep onset (WASO; p = 0.014) and sleep latency (SL; p < 0.001), negatively with total sleep time (TST; p < 0.001) and sleep efficiency (SE; p = 0.001), but not with the apnea-hypopnea index (AHI; p = 0.387) or average O2 (p = 0.542). ESS in the OSA group correlated positively with TST (p < 0.001), SE (p < 0.001), and AHI (p < 0.001) and negatively with WASO (p = 0.010), SL (p < 0.001), and average O2 (p < 0.001). CONCLUSIONS: In this study, patients with OSA had more depressive symptoms than those without OSA. Daytime sleepiness in OSA patients was related to the severity of OSA as well as sleep propensity. However, depressive symptoms in OSA patients were associated with poor sleep quality rather than OSA severity. Depressive symptoms in OSA may be treated by modulating the sleep architecture and sleep quality.

Transdermal Nicotine Patch Effects on EEG Power Spectra and Heart Rate Variability During Sleep of Healthy Male Adults.

OBJECTIVE: The effect of transdermal nicotine patch on sleep physiology is not well established. The current study aimed to examine the influence of nicotine patch on homeostatic sleep propensity and autonomic nervous system. METHODS: We studied 16 non-smoking young healthy volunteers with nocturnal polysomnography in a double blind crossover design between sleep with and without nicotine patch. We compared the sleep variables, sleep EEG power spectra, and heart rate variability. RESULTS: The night with nicotine patch showed significant increase in sleep latency, wake after sleep onset, and stage 1 sleep; and decrease in total sleep time, sleep efficiency, and percentage of REM sleep. Also, spectral analysis of the sleep EEG in the night with nicotine patch revealed decreased slow wave activity in stage 2 and REM sleep and increased alpha activity in the first NREM-REM sleep cycle. Heart rate variability showed no differences between the 2 nights, but the low to high ratio (a parameter indicative of sympathetic nervous system activity) positively correlated with wake after sleep onset in night with nicotine patch. CONCLUSION: Transdermal nicotine patch significantly disrupts sleep continuity, sleep architecture, and homeostatic sleep propensity. The overactivation of the sympathetic nervous system may be responsible for these changes.

REM sleep estimation based on autonomic dynamics using R-R intervals.

OBJECTIVE: We developed an automatic algorithm to determine rapid eye movement (REM) sleep on the basis of the autonomic activities reflected in heart rate variations. APPROACH: The heart rate variability (HRV) parameters were calculated using the R-R intervals from an electrocardiogram (ECG). A major autonomic variation associated with the sleep cycle was extracted from a combination of the obtained parameters. REM sleep was determined with an adaptive threshold applied to the acquired feature. The algorithm was optimized with the data from 26 healthy subjects and obstructive sleep apnea (OSA) patients and was validated with data from a separate group of 25 healthy and OSA subjects. MAIN RESULTS: According to an epoch-by-epoch (30 s) analysis, the average of Cohen's kappa and the accuracy were respectively 0.63 and 87% for the training set and 0.61 and 87% for the validation set. In addition, the REM sleep-related information extracted from the results of the proposed method revealed a significant correlation with those from polysomnography (PSG). SIGNIFICANCE: The current algorithm only using R-R intervals can be applied to mobile and wearable devices that acquire heart-rate-related signals; therefore, it is appropriate for sleep monitoring in the home and ambulatory environments. Further, long-term sleep monitoring could provide useful information to clinicians and patients for the diagnosis and treatments of sleep-related disorders and individual health care.

Preliminary Study on Quantitative Sleep EEG Characteristics in Patients with Schizophrenia.

We used quantitative electroencephalography (EEG) spectral analysis to compare activity in the bilateral frontal, central, and occipital areas in nine patients with schizophrenia and ten healthy control subjects during standard nocturnal polysomnography. Patients with schizophrenia had longer sleep latency than controls. In N2 sleep, the patients had significantly lower 0.5-1 Hz power and higher theta power in the left frontal region, and higher beta power in the left occipital region than did control subjects. In N3 sleep, the patients with schizophrenia had significantly higher alpha power in the left occipital region than did controls. These findings show distinctive EEG sleep patterns in patients with schizophrenia, which may reflect brain dysfunction or medication effects.

Sleep Period Time Estimation Based on Electrodermal Activity.

We proposed and tested a method to estimate sleep period time (SPT) using electrodermal activity (EDA) signals. Eight healthy subjects and six obstructive sleep apnea patients participated in the experiments. Each subject's EDA signals were measured at the middle and ring fingers of the dominant hand during polysomnography (PSG). For nine of the 17 participants, wrist actigraphy was also measured for a quantitative comparison of EDA- and actigraphy-based methods. Based on the training data, we observed that sleep onset was accompanied by a gradual reduction of amplitude of the EDA signals, whereas sleep offset was accompanied by a rapid increase in amplitude of EDA signals. We developed a method based on these EDA fluctuations during sleep-wake transitions, and applied it to a test dataset. The performance of the method was assessed by comparing its results with those from a physician's sleep stage scores. The mean absolute errors in the obtained values for sleep onset, offset, and period time between the proposed method, and the results of the PSG were 4.1, 3.0, and 6.1 min, respectively. Furthermore, there were no significant differences in the corresponding values between the methods. We compared these results with those obtained by applying actigraphic methods, and found that our algorithm outperformed these in terms of each estimated parameter of interest in SPT estimation. Long awakening periods were also detected based on sympathetic responses reflected in the EDA signals. The proposed method can be applied to a daily sleep monitoring system.

Apnea-Hypopnea Index Prediction Using Electrocardiogram Acquired During the Sleep-Onset Period.

The most widely used methods for predicting obstructive sleep apnea are based on clinical or anatomico-functional features. To improve exactitude in obstructive sleep apnea screening, this study aimed to devise a new predictor of apnea-hypopnea index. We hypothesized that less irregular respiration cycles would be observed in the patients with more severe obstructive sleep apnea during the sleep-onset period. From each of the 156 and 70 single-lead electrocardiograms collected from the internal polysomnographic database and from the Physionet Apnea-ECG database, respectively, the 150-s sleep-onset period was determined and the respiration cycles during this period were detected. Using the coefficient of variation of the respiration cycles, obtained from the internal dataset, as a predictor, the apnea-hypopnea index predictive model was developed through regression analyses and k-fold cross-validations. The apnea-hypopnea index predictability of the regression model was tested with the Physionet Apnea-ECG database. The regression model trained and validated from the 143 and 13 data, respectively, produced an absolute error (mean ± SD) of 3.65 ±2.98 events/h and a Pearson's correlation coefficient of 0.97 (P < 0.01) between the apnea-hypopnea index predictive values and the reference values for the 70 test data. The new predictor of apnea-hypopnea index has the potential to be utilized in making more reasoned clinical decisions on the need for formal diagnosis and treatment of obstructive sleep apnea. Our study is the first study that presented the strategy for providing a reliable apnea-hypopnea index without overnight recording.

Apnea-hypopnea index prediction through an assessment of autonomic influence on heart rate in wakefulness.

With the high prevalence of obstructive sleep apnea, the issue of developing a practical tool for obstructive sleep apnea screening has been raised. Conventional obstructive sleep apnea screening tools are limited in their ability to help clinicians make rational decisions due to their inability to predict the apnea-hypopnea index. Our study aimed to develop a new prediction model that can provide a reliable apnea-hypopnea index value during wakefulness. We hypothesized that patients with more severe obstructive sleep apnea would exhibit more attenuated waking vagal tone, which may result in lower effectiveness in decreasing heart rate as a response to deep inspiration breath-holding. Prior to conducting nocturnal in-laboratory polysomnography, 30 non-obstructive sleep apnea (apnea-hypopnea index<5events/h) subjects and 246 patients with obstructive sleep apnea participated in a 75-second experiment that consisted of a 60-second baseline measurement and consecutive 15-second deep inspiration breath-hold sessions. Two apnea-hypopnea index predictors were devised by considering the vagal activities reflected in the electrocardiographic recordings acquired during the experiment. Using the predictors obtained from 184 individuals, regression analyses and k-fold cross-validation tests were performed to develop an apnea-hypopnea index prediction model. For the remaining 92 individuals, the developed model provided an absolute error (mean±SD) of 3.53±2.67events/h and a Pearson's correlation coefficient of 0.99 (P<0.01) between the apnea-hypopnea index predictive values and the reference values reported by polysomnography. Our study is the first to achieve reliable and time-efficient prediction of the apnea-hypopnea index during wakefulness.

New predictors of sleep efficiency.

Sleep efficiency is a commonly and widely used measure to objectively evaluate sleep quality. Monitoring sleep efficiency can provide significant information about health conditions. As an attempt to facilitate less cumbersome monitoring of sleep efficiency, our study aimed to suggest new predictors of sleep efficiency that enable reliable and unconstrained estimation of sleep efficiency during awake resting period. We hypothesized that the autonomic nervous system activity observed before falling asleep might be associated with sleep efficiency. To assess autonomic activity, heart rate variability and breathing parameters were analyzed for 5 min. Using the extracted parameters as explanatory variables, stepwise multiple linear regression analyses and k-fold cross-validation tests were performed with 240 electrocardiographic and thoracic volume change signal recordings to develop the sleep efficiency prediction model. The developed model's sleep efficiency predictability was evaluated using 60 piezoelectric sensor signal recordings. The regression model, established using the ratio of the power of the low- and high-frequency bands of the heart rate variability signal and the average peak inspiratory flow value, provided an absolute error (mean ± SD) of 2.18% ± 1.61% and a Pearson's correlation coefficient of 0.94 (p < 0.01) between the sleep efficiency predictive values and the reference values. Our study is the first to achieve reliable and unconstrained prediction of sleep efficiency without overnight recording. This method has the potential to be utilized for home-based, long-term monitoring of sleep efficiency and to support reasonable decision-making regarding the execution of sleep efficiency improvement strategies.

Increased Mortality in Relation to Insomnia and Obstructive Sleep Apnea in Korean Patients Studied with Nocturnal Polysomnography.

STUDY OBJECTIVES: To elucidate the links between the two most prevalent sleep disorders, insomnia and obstructive sleep apnea (OSA), and mortality. METHODS: We studied 4,225 subjects who were referred to the Center for Sleep and Chronobiology, Seoul National University Hospital, from January 1994 to December 2008. We divided the subjects into five groups: mild OSA (5 ≤ AHI < 15), moderate OSA (15 ≤ AHI < 30), severe OSA (AHI ≥ 30), insomnia, and a no-sleep-disorder group consisting of subjects without sleep disorders. Standardized mortality ratio (SMR), hazard ratio, and the survival rates of the five groups were calculated and evaluated. RESULTS: The SMR of all-cause mortality was significantly higher in the severe OSA group than in the general population (1.52, 95% CI 1.23-1.85, p < 0.05). The SMR of cardiovascular mortality increased progressively with the severity of OSA (no-sleep-disorder: 0.09, mild: 0.40, moderate: 0.52, severe: 1.79, p < 0.05). Statistical analyses of the hazard ratios indicated that severe OSA is a risk factor for all-cause mortality (HR 3.50, 95% CI 1.03-11.91, p = 0.045) and cardiovascular mortality (HR 17.16, 95% CI 2.29-128.83, p = 0.006). Cardiovascular mortality was also significantly elevated in the insomnia group (HR 8.11, 95% CI 1.03-63.58, p = 0.046). CONCLUSIONS: Severe OSA was associated with increased all-cause mortality and cardiovascular mortality compared to the no-sleep-disorder group. Insomnia was associated with increased cardiovascular mortality compared to the no-sleep-disorder group.