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Sleep Engineering
Nonintrusive Measurement
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Biomedical Signal Processing & Analysis
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Biomedical Engineering

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    Sleep monitoring (updated 2019)

Human spend almost one third of our lifetime asleep, and sleep is indispensable to life. Good sleep is a key factor of health and well-being for human

Polysomnography (PSG), which comprehensively records physiological changes that occur during sleep, are the gold standard methods in sleep medicine for sleep monitoring. However, PSG recording during sleep brings inconvenience to the sleeper because numerous sensors are attached to the body and face of the sleeper. In addition, manual scoring of sleep stages and sleep-related events from PSG data is a very time-consuming and laborious process 

Our research group developed sleep stage estimation algorithms using a minimum biomedical signal and proposed sleep monitoring method using an unconstrained manner

1. Sleep stage  estimation 

l  Developed an automatic slow-wave sleep detection algorithm from the heart rate variations of a single sensor [Ref 1]

l  Developed an algorithm for distinguishing wakefullness from sleep using a patch-type device [Ref 2]

l  Developed an automatic algorithm to determine rapid eye movement (REM) sleep on the basis of the autonomic activities reflected in heart rate variations [Ref 3]

l  Proposed algorithms could be an appropriate solution for long-term objective sleep monitoring in both healthy individuals and patients with OSA



[Ref 1] H.N. Yoon et al., Slow-Wave Sleep Estimation for Healthy Subjects and OSA Patients Using R–R Intervals. IEEE journal of biomedical and health informatics, 2018, 22.1: 119-128.

[Ref 2] H.N. Yoon et al., Wakefulness evaluation during sleep for healthy subjects and OSA patients using a patch-type device. Computer methods and programs in biomedicine, 2018, 155: 127-138.

[Ref 3] H.N. Yoon et al., REM sleep estimation based on autonomic dynamics using R–R intervals. Physiological measurement, 2017, 38.4: 631.

2. Unconstrained sleep monitoring

l  Proposed air-mattress with balancing tube (AMBT) method to noninvasively monitor the cardiopulmonary activity and sleep event during sleep [Ref 1]

l  Proposed a new sleep stage estimation method that uses a PVDF sensor that can unconstrainedly measure the respiration and body movement signals from subjects [Ref 2]

l  Developed a slow-wave sleep detection algorithm using movement and cardiac activity which was measured unobtrusively by a load-cell-installed bed [Ref 3]

[Ref 1] J.H. Shin et al., Nonconstrained sleep monitoring system and algorithms using air-mattress with balancing tube method. IEEE transactions on information technology in biomedicine, 2010, 14.1: 147-156.

[Ref 2] S.H. Hwang et al., Unconstrained sleep stage estimation based on respiratory dynamics and body movement. Methods of information in medicine, 2016, 55.06: 545-555.

[Ref 3] B.H. Choi et al., Slow-wave sleep estimation on a load-cell-installed bed: a non-constrained method. Physiological measurement, 2009, 30.11: 1163.

    Sleep disorders detection (updated 2019)

The most common sleep disorders are OSA (Obstructive Sleep Apnea) which is the periodic reduction or cessation of airflow due to narrowing of the upper airway during sleep, and insomnia due to depression or stress

In this research group, event detection and diagnosis algorithms for sleep disorders were developed using a minimum biomedical signal. Those methods reduce user’s discomfort and cost and enable continuous sleep disorder monitoring

1. Sleep Apnea   

l  Developed an automated apnea-hypopnea event detection model, based on Convolutional Neural Network

l  Overlapping nasal pressure signal segments are used to precisely detect AH events

l  The proposed model exhibited a kappa of 0.82 in event detection and an average accuracy of 94.9% in SAHS severity diagnosis 

l  The proposed method could be used to reduce AH events scoring time in sleep laboratories, and it can be applied to screen SAHS severity before PSG tests


[Ref] S.H. Choi et al., Real-time apnea-hypopnea event detection during sleep by convolutional neural networks, Computers in Biology and Medicine, Vol. 100, pp. 123-131, 2018.

l  A new strategy for near real-time automatic detection of apneic events using nocturnal pulse oximetry 

l  Apneic event detection based on the morphometric characteristics in the fluctuation of blood oxygen saturation values

l  The minute-by-minute apneic segment detection exhibited an average accuracy of 91.0%

l  The proposed method could be potentially useful in home-based multinight apneic event monitoring


[Ref] D.W. Jung et al., Real-Time Automatic Apneic Event Detection Using Nocturnal Pulse Oximetry, IEEE Transactions on Biomedical Engineering, Vol.65, No.3, pp. 706-712, 2018.

2. Snoring  

l  A new snoring monitoring method using a PVDF sensor

l  Accurate, fast, and motion-artifact-robust snore event detection

l  The overall sensitivity and the positive predictive values were 94.6% and 97.5%, respectively

l  The proposed method can be applied in both residential and ambulatory


[Ref] S.H. Hwang et al, Polyvinylidene fluoride sensor-based method for unconstrained snoring detection, Physiological Measurement, Vol. 36, pp. 1399-1414, 2015.

3. Depression 

l  Hypothesis that autonomic dysfunction links depression and cardiac disease

l  Polysomnography was performed in patients with depression and healthy subjects

l  Fractal (alpha-1) heart rate variability correlated with beck depression inventory score

l  Heart rate variability may be used as an indicator of depressive disorder


[Ref] H.B. Kwon et al, Heart rate variability changes in major depressive disorder during sleep: fractal index correlates with BDI score during REM sleep, Psychiatry Research, Vol. 271, pp. 291-298, 2019.


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Department of Biomedical Engineering, College of Medicine, Seoul National University,
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