Su Hwan Hwang

Nocturnal Awakening and Sleep Efficiency Estimation Using Unobtrusively Measured Ballistocardiogram

Fragmented sleep due to frequent awakenings represents a major cause of impaired daytime performance and adverse health outcomes. Currently, the gold standard for studying and assessing sleep fragmentation is polysomnography (PSG). Here, we propose an alternative method for real-time detection of nocturnal awakening via ballistocardiography using an unobtrusive polyvinylidene fluoride (PVDF) film sensor on a bed mattress. From ballistocardiogram, heart rate and body movement information were extracted to develop an algorithm for classifying sleeping and awakening epochs. In total, ten normal subjects (mean age 38.7 ± 14.6 years) and ten patients with obstructive sleep apnea (OSA) (mean age 44.2 ± 16.5 years) of varying symptom severity participated in this study. Our study detected awakening epochs with an average sensitivity of 85.3% and 85.2%, specificity of 98.4% and 97.7%, accuracy of 97.4% and 96.5%, and Cohens kappa coefficient of 0.83 and 0.81 for normal subjects and OSA patients, respectively. Also, sleep efficiency was estimated using detected awakening epochs and then compared with PSG results. Mean absolute errors in sleep efficiency were 1.08% and 1.44% for normal subjects and OSA patients, respectively. The results presented here indicate that our suggested method could be reliably applied to real-time nocturnal awakening detection and sleep efficiency estimation. Furthermore, our method may ultimately be an effective tool for long-term, home monitoring of sleep-wake behavior.

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.

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.

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.

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 Cohens 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.

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 subjects 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.

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

Objectives To clarify the relationships between sleep-wake cycle and cerebral β-amyloid (Aβ) 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 Alzheimers Disease cohort. All participants underwent [11C] Pittsburgh compound B-PET imaging to quantify Aβ 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 Aβ 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 Aβ positivity. In contrast, APOE ε4 carriers showed a relationship between delayed acrophase and Aβ accumulation that approached but did not reach significance. After the Bonferroni correction, the associations of shorter SL and higher mesor with Aβ 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 Aβ accumulation in CN older adults. Thus, APOE ε4 status needs to be considered as a key factor when designing related research or interventions.

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.

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.

Automatic Detection of Slow-Wave Sleep Based on Electrocardiogram

Abstract: 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 whichsimultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were thenapplied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. OverallCohen’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 REMand wake estimation technique on unattended home-based sleep monitoring. Key words: Slow-wave sleep, Electrocardiogram, Heart rate I. 서 론 서파 수면(Slow-wave sleep, SWS)은 수면에서 나타나는 다양한 수면 단계 중 하나로 생체신호의 특성 변화를 기반으로 수면 단계를 구분한 RKNREM)수면이다[1]. 전체 수면의 15-25%를 차지하는 서파수면은 그 이름에서도 알 수 있듯이 수면 중 1-2 Hz 이하의 뇌전도(EEG)의 크기가 우세하게 나타나는 특징을 가지며, 다양한 생리학적 기능을 수행한다고 알려져 있다. 기본적으로 인간은 서파 수면을 통해 일상 생활에 의한 뇌의 피로를 회복한다고 알려져 있으며[2], 다른 수면 상태보다 서파 수면에서 성장 호르몬의 분비가 증가한다고 밝혀졌다[3].또한, 최근 연구에서 서파 수면은 기억 응고화(Memoryconsolidation)의 기능을 한다고 밝힌바 있다[4,5]. 연구에의하면 서파 수면의 비율과 기억력 사이의 유의미한 양의상관 관계가 있으며, 이는 서파 수면에서 주로 나타나는 뇌파의 느린 진동(Slow oscillation)에 의한 것이라 입증하였다. 나아가 서파 수면의 양(Quantity)은 폐쇄성 수면 무호흡증 환자를 치료를 위한 중요한 특징으로 활용 될 수Corresponding Author : Park Kwang Suk 있다고 밝힌 연구 또한 존재한다[6]. 이처럼 서파 수면은 기능적으로 중요한 역할을 수행할 뿐만 아니라, 수면 관련 질환자들의 처치에도 유용하게 활용할 수 있기 때문에, 전체 수면에서 서파 수면을 검출하고, 이해하는 것은 큰 의미를 갖는다고 말할 수 있다.Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, KoreaTEL: +82-2-2072-3135 / FAX: +82-2-3676-2821E-mail: pks@bmsil.snu.ac.kr