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.

Moderating effect of APOE ε4 on the relationship between sleep-wake cycle and brain ß-amyloid.

OBJECTIVES: To clarify the relationships between sleep-wake cycle and cerebral ß-amyloid (Aß) deposition in cognitively normal (CN) older adults, focusing primarily on the moderating effects of the APOE ε4 allele. METHODS: The present study included 133 CN older adults who participated in the Korean Brain Aging Study for Early Diagnosis & Prediction of Alzheimer's Disease cohort. All participants underwent [11C] Pittsburgh compound B-PET imaging to quantify Aß deposition in the brain and blood sampling for APOE genotyping. Sleep and circadian parameters were measured using actigraphy for 8 consecutive days. RESULTS: The APOE ε4 allele had moderating effects on the associations of sleep latency (SL), mesor, and acrophase with cerebral Aß deposition, and the interactions between APOE ε4 status and SL and between APOE ε4 status and acrophase remained significant after adjusting for multiple comparisons. In APOE ε4 noncarriers, shorter SL, higher mesor, and advanced acrophase were associated with Aß positivity. In contrast, APOE ε4 carriers showed a relationship between delayed acrophase and Aß accumulation that approached but did not reach significance. After the Bonferroni correction, the associations of shorter SL and higher mesor with Aß positivity remained significant for APOE ε4 noncarriers. CONCLUSIONS: Our findings suggest that the APOE ε4 allele may act as a moderator in the relationship between the sleep-wake cycle and Aß accumulation in CN older adults. Thus, APOE ε4 status needs to be considered as a key factor when designing related research or interventions.

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.

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.

Comparison of the Effects of Quetiapine XR and Lithium Monotherapy on Actigraphy-Measured Circadian Parameters in Patients With Bipolar II Depression.

PURPOSE/BACKGROUND: The aim of this study was to evaluate the effects of quetiapine XR and lithium on actigraphy-measured circadian parameters in patients with bipolar II depression. METHODS/PROCEDURES: This was an 8-week, open-label, prospective, randomized comparative study. The assessments included the 17-item Hamilton Depression Rating Scale score and actigraphic measures concerning the previous 7 days, collected at each visit (weeks 0 [baseline], 1, 2, 4, 6, and 8); the actigraphic data were analyzed with a cosinor analysis. FINDINGS/RESULTS: Medication, time, and the interaction between medication and time were significantly associated with acrophase for the entire group (Ps = 0.003, 0.020, and 0.042, respectively). More specifically, acrophase was significantly delayed at weeks 1 and 6 (Ps = 0.004 and 0.039, respectively) in the quetiapine XR group. The F statistics significantly increased over time for the entire group (P < 0.001), and there was a significant increase in F statistics on weeks 4 and 6 in the quetiapine XR group (Ps = 0.016 and 0.020, respectively) and on weeks 4 and 8 in the lithium group (Ps = 0.001 and 0.016, respectively). In addition, scores on the 17-item Hamilton Depression Rating Scale were significantly associated with the F statistics during 8 weeks for the entire group (P = 0.008). IMPLICATIONS/CONCLUSIONS: Both quetiapine XR and lithium affected several circadian parameters, including peak activity time and robustness of circadian rhythm, but exerted different effects on acrophase in patients with bipolar II depression. In particular, clinical depressive symptoms were associated with robustness of circadian rhythm during the course of the 8-week treatment.

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.

Oxygen Desaturation Index Estimation through Unconstrained Cardiac Sympathetic Activity Assessment Using Three Ballistocardiographic Systems.

BACKGROUND: Nocturnal hypoxemia, characterized by abnormally low oxygen saturation levels in arterial blood during sleep, is a significant feature of various pathological conditions. The oxygen desaturation index, commonly used to evaluate the nocturnal hypoxemia severity, is acquired using nocturnal pulse oximetry that requires the overnight wear of a pulse oximeter probe. OBJECTIVES: This study aimed to suggest a method for the unconstrained estimation of the oxygen desaturation index. METHODS: We hypothesized that the severity of nocturnal hypoxemia would be positively associated with cardiac sympathetic activation during sleep. Unconstrained heart rate variability monitoring was conducted using three different ballistocardiographic systems to assess cardiac sympathetic activity. Overnight polysomnographic and ballistocardiographic recording pairs were collected from the 20 non-nocturnal hypoxemia (oxygen desaturation index <5 events/h) subjects and the 76 nocturnal hypoxemia patients. Among the 96 recording pairs, 48 were used as training data and the remaining 48 as test data. RESULTS: The regression analysis, performed using the low-frequency component of heart rate variability, exhibited a root mean square error of 3.33 events/h between the estimates and the reference values of the oxygen desaturation index. The nocturnal hypoxemia diagnostic performance produced by our method was presented with an average accuracy of 96.5% at oxygen desaturation index cutoffs of ≥5, 15, and 30 events/h. CONCLUSIONS: Our method has the potential to serve as a complementary measure against the accidental slip-out of a pulse oximeter probe during nocturnal pulse oximetry. The independent application of our method could facilitate home-based long-term oxygen desaturation index monitoring.

Apnea-hypopnea index estimation using quantitative analysis of sleep macrostructure.

Obstructive sleep apnea, characterized by recurrent cessation or substantial reduction in breathing during sleep, is a prevalent and serious medical condition. Although a significant relationship between obstructive sleep apnea and sleep macrostructure has been revealed in several studies, useful applications of this relationship have been limited. The aim of this study was to suggest a novel approach using quantitative analysis of sleep macrostructure to estimate the apnea-hypopnea index, which is commonly used to assess obstructive sleep apnea. Without being bound by conventional sleep macrostructure parameters, various new sleep macrostructure parameters were extracted from the polysomnographic recordings of 132 subjects. These recordings were split into training and validation sets, each with 66 recordings including 48 recordings with an apnea-hypopnea index greater than 5 events h(-1). The nonlinear regression analysis, performed using the percentage transition probability from non-rapid eye movement sleep stage 2 to stage 1, was most effective in estimating the apnea-hypopnea index. Between the apnea-hypopnea index estimates and the reference values reported from polysomnography, a root mean square error of 7.30 events h(-1) was obtained in the validation set. At an apnea-hypopnea index cut-off of ⩾30 events h(-1), the obstructive sleep apnea diagnostic performance was provided with a sensitivity of 90.0%, a specificity of 93.5%, and an accuracy of 92.4% by our method. The developed apnea-hypopnea index estimation model has the potential to be utilized in circumstances in which it is not possible to acquire or analyze respiration signal but it is possible to obtain information on sleep macrostructure.

Polyvinylidene fluoride sensor-based method for unconstrained snoring detection.

We established and tested a snoring detection method using a polyvinylidene fluoride (PVDF) sensor for accurate, fast, and motion-artifact-robust monitoring of snoring events during sleep. Twenty patients with obstructive sleep apnea participated in this study. The PVDF sensor was located between a mattress cover and mattress, and the patients' snoring signals were unconstrainedly measured with the sensor during polysomnography. The power ratio and peak frequency from the short-time Fourier transform were used to extract spectral features from the PVDF data. A support vector machine was applied to the spectral features to classify the data into either the snore or non-snore class. The performance of the method was assessed using manual labelling by three human observers as a reference. For event-by-event snoring detection, PVDF data that contained 'snoring' (SN), 'snoring with movement' (SM), and 'normal breathing' epochs were selected for each subject. As a result, the overall sensitivity and the positive predictive values were 94.6% and 97.5%, respectively, and there was no significant difference between the SN and SM results. The proposed method can be applied in both residential and ambulatory snoring monitoring systems.

Heart Rate Variability Monitoring during Sleep Based on Capacitively Coupled Textile Electrodes on a Bed.

In this study, we developed and tested a capacitively coupled electrocardiogram (ECG) measurement system using conductive textiles on a bed, for long-term healthcare monitoring. The system, which was designed to measure ECG in a bed with no constraints of sleep position and posture, included a foam layer to increase the contact region with the curvature of the body and a cover to ensure durability and easy installation. Nine healthy subjects participated in the experiment during polysomnography (PSG), and the heart rate (HR) coverage and heart rate variability (HRV) parameters were analyzed to evaluate the system. The experimental results showed that the mean of R-peak coverage was 98.0% (95.5%-99.7%), and the normalized errors of HRV time and spectral measures between the Ag/AgCl system and our system ranged from 0.15% to 4.20%. The root mean square errors for inter-beat (RR) intervals and HR were 1.36 ms and 0.09 bpm, respectively. We also showed the potential of our developed system for rapid eye movement (REM) sleep and wake detection as well as for recording of abnormal states.

Unconstrained sleep apnea monitoring using polyvinylidene fluoride film-based sensor.

We established and tested an unconstrained sleep apnea monitoring method using a polyvinylidene (PVDF) film-based sensor for continuous and accurate monitoring of apneic events occurred during sleep. Twenty-six sleep apnea patients and six normal subjects participated in this study. Subjects' respiratory signals were measured using the PVDF-based sensor during polysomnography. The PVDF sensor comprised a 4 × 1 array, and a thin silicon pad was placed over the sensor to prevent damage. Total thickness of the merged system was approximately 1.1 mm which was thin enough to prevent the subject from being consciously aware of its presence. It was designed to be placed under subjects' backs and installed between a bed cover and mattress. The proposed method was based on the standard deviation of the PVDF signals, and it was applied to a test set for detecting apneic events. The method's performance was assessed by comparing the results with a sleep physician's manual scoring. The correlation coefficient for the apnea-hypopnea index (AHI) values between the methods was 0.94 (p < 0.001). The areas under the receiver operating curves at three AHI threshold levels (>5, >15, and >20) for sleep apnea diagnosis were 0.98, 0.99, and 0.98, respectively. For min-by-min apnea detection, the method classified sleep apnea with an average sensitivity of 72.9%, specificity of 90.6%, accuracy of 85.5%, and kappa statistic of 0.60. The developed system and method can be applied to sleep apnea detection in home or ambulatory monitoring.

Ballistocardiography for nonintrusive sleep structure estimation.

Based on the its nonintrusive characteristics, ballistocardiography(BCG) has applied in the estimation of sleep structure without attaching any sensors to the subject's body. Loadcell or polyvinylidenefluoride (PVDF) film sensors are installed on the mattress for the monitoring of BCG. BCG peak was detected and heart rate variability parameters are derived. Parameters representing sleep structure and quality are estimated using these parameters. Sleep efficiency, four stages of sleep structure and sleep onset latency are estimated and results are compared with the results derived from polysomnographic recording.

Estimation of body postures on bed using unconstrained ECG measurements.

We developed and tested a system for estimating body postures on a bed using unconstrained measurements of electrocardiogram (ECG) signals using 12 capacitively coupled electrodes and a conductive textile sheet. Thirteen healthy subjects participated in the experiment. After detecting the channels in contact with the body among the 12 electrodes, the features were extracted on the basis of the morphology of the QRS (Q wave, R wave, and S wave of ECG) complex using three main steps. The features were applied to linear discriminant analysis, support vector machines with linear and radial basis function (RBF) kernels, and artificial neural networks (one and two layers), respectively. SVM with RBF kernel had the highest performance with an accuracy of 98.4% for estimation of four body postures on the bed: supine, right lateral, prone, and left lateral. Overall, although ECG data were obtained from few sensors in an unconstrained manner, the performance was better than the results that have been reported to date. The developed system and algorithm can be applied to the obstructive apnea detection and analyses of sleep quality or sleep stages, as well as body posture detection for the management of bedsores.

Estimation of sleep onset latency based on the blood pressure regulatory reflex mechanism.

Sleep onset latency (SOL) is an objective indicator of sleepiness and is being used clinically as a diagnostic tool for sleep deprivation. This study proposes a new and less intrusive approach to estimate SOL based on the blood pressure (BP) regulatory reflex mechanism.We hypothesized that the arterial baroreflex, one kind of reflex mechanism for BP regulation, maintains the toning-down effect sleep has on BP. The arterial baroreflex is strongly activated after the time of sleep onset in order to maintain the lowered BP by leading to an increase in heart beat interval (HBI). This observation suggests that the arterial baroreflex has a marked influence on the HBI control with the onset of sleep. As a result, a positive correlation is expected between fluctuations of BP and those of subsequent HBI after sleep onset. To investigate our hypothesis, we determined the relationship between BP and HBI using the R-J and R-R intervals measured from an electrocardiogram and a ballistocardiogram.We estimated SOL using the correlation coefficients corresponding to the relationship between fluctuations of the R-J interval and those of the subsequent R-R interval. The SOLs of ten healthy subjects [age (mean ± SD): 28.72 ± 3.21 years] were estimated using our proposed method and compared with the polysomnography data. The mean absolute error was 0.25 ± 0.35 min, corresponding to a half epoch (15 s) on average. We expect our method will be applicable as a nonintrusive and automatic SOL estimation system that does not require the use of electroencephalogram sensors.

Heart rate variability analysis using a ballistocardiogram during Valsalva manoeuvre and post exercise.

We introduced a novel non-constrained technique for estimating heart rate variability (HRV) using a ballistocardiogram (BCG). To assess whether the BCG signal can be used to analyse the cardiac autonomic modulation, HRV parameters derived from the BCG signal (ballistocardiographic HRV, B-HRV) were statistically compared with the HRV parameters from the ECG signal during rest and under two different experimental conditions that induce cardiac autonomic rhythm changes: the Valsalva manoeuvre and static exercise. Time domain, frequency domain and nonlinear analyses were individually performed on 15 healthy subjects to assess whether the BCG can be used to analyse the cardiac autonomic modulation under each condition. For all subjects, the proposed method had averages of relative errors of 5.01 ± 4.72, 5.64 ± 4.83 and 5.98 ± 5.80% for resting, Valsalva and post-exercise sessions, respectively, and the correlation coefficients between the reference (ECG) and proposed (BCG) methods are 0.97, 0.98 and 0.98, for resting, Valsalva and post-exercise sessions, respectively. During cardiac autonomic changes, the B-HRV parameters changed in a pattern that is very similar to the variations in the HRV parameters based on Student's t-test results. In addition, some of the B-HRV parameters changed according to cardiac autonomic rhythms controlled by sympathetic and parasympathetic activities during the experiments. These findings indicate that BCG can provide an accurate and reliable means to evaluate autonomic system activation by HRV in its unconstrained way.

Wakefulness estimation only using ballistocardiogram: nonintrusive method for sleep monitoring.

To evaluate sleep quality or autonomic nervous system, many annoying electrodes have be attached to subjects' body. It can disturb comfortable sleep and, moreover, since it is very expensive experiment, continuous sleep monitoring is difficult. Since heart rate reflects the autonomic nervous system, it is highly synchronized with the sympathetic activation during transition from non-REM sleep to wakefulness. When the transition occurred the heart rate abruptly increased clearly distinguished with other changes. By using this physiology, we tried to classify the wakefulness during the whole night sleep. Our final goal is adopting this method to the continuous monitoring in our daily life. electrocardiogram (ECG) is not the suitable. Subjects have to attach the electrodes by themselves in their housing to obtain ECG. In that point of view, we used the ballistocardiogram (BCG) that is the representative method to obtain heart beat nonintrusively. For ten normal subjects, the wakefulness classifications by using the heart rate dynamics were executed. Nine subjects showed substantial agreement with the visually-scored method, polysomnography (PSG), and only one subject showed moderate agreement in Cohen's kappa value.