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

Yoon, Heenam; Hwang, Sang Hyun; Choi, Jae-Won; Lee, Yu Jin; Jeong, Do-Un; Park, Kwang Suk

doi:10.1109/jbhi.2017.2712861

Cited by 31 publications

(20 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, specific HRV characteristics in SWS have been observed, with stationary heart rate and uncorrelated consecutive R-R intervals (Herzig et al, 2016 ). Recent studies have attempted to classify sleep stages using ECG parameters only (Ebrahimi et al, 2013 ; Long et al, 2014 , 2017 ; Fonseca et al, 2015 ; Yoon H. et al, 2017 ; Yoon H. N. et al, 2017 ). Moreover, other studies have used a simple method to identify a segment within SWS and analyze HRV (Al Haddad et al, 2009 ; Herzig et al, 2016 , 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

et al. 2018

View full text Add to dashboard Cite

Objective: Measurements of heart rate variability (HRV) during sleep have become increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep).Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed within the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance, and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive R-R intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm.Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS.Conclusions: SWS, a stable state that, in contrast to waking, is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on R-R intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.

show abstract

Section: Introductionmentioning

confidence: 99%

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The gold standard of sleep-disordered diagnosis including conditions such as OSA is polysomnography (PSG). It is used to determine the frequency and severity of normal respiratory disorder events per hour and reports as the Apnea-Hypopnea Index (AHI) which can be used to classify the OSA as normal (AHI<5), mild (AHI is in [5][6][7][8][9][10][11][12][13][14], moderate (AHI is in [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and severe (AHI>30), respectively [10]. However, this method is a form of clinical practice which has to be done overnight in a laboratory or hospital [13] using numerous sensors to acquire the necessary data, such as electroencephalogram (EEG), electrooculogram (EOG), chin electromyography (EMG), leg movement, airflow, cannula flow, respiratory effort, oximetry, body position, electrocardiogram (ECG), and so forth [6].…”

Section: Introductionmentioning

confidence: 99%

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Banluesombatkul

Rakthanmanon

Wilaiprasitporn

2018

TENCON 2018 - 2018 IEEE Region 10 Conference

View full text Add to dashboard Cite

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI < 5) and severe OSA patients (AHI > 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of one-dimensional Convolutional Neural Networks (1-D CNN) for automatic feature extraction, deep recurrent neural networks with Long Short-Term Memory (LSTM) for temporal information extraction, and fully-connected neural networks (DNN) for feature encoding from a large number of features until it closed to two classes. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the kfold cross-validation technique. The accuracy of 79.45% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near realtime alerting systems such as before SCD occurs.

show abstract

“…According to the statistic investigation in [6], approximately 3∼7 % adult men and 2∼5 % adult women in the general population around the world suffer from OSA. This leads to a serious deterioration of health conditions, such as daytime sleepiness, excessive fatigue, morning headache, and even high blood pressure and depression mood in a long-term case [6]- [9]. To treat OSA, doctors need to determine the obstructive position of the respiratory tract in the very beginning.…”

Section: Introductionmentioning

confidence: 99%

“…To treat OSA, doctors need to determine the obstructive position of the respiratory tract in the very beginning. A standard determination approach often associates with a Drug-Induced Sleep Endoscopy (DISE) procedure, in which a flexible nasopharyngoscope is introduced into the upper airway while the patient is in a state of artificial sleep [9], [10]. Vibration mechanisms and locations can be observed while video and audio signals are recorded.…”

Section: Introductionmentioning

confidence: 99%

Snore-GANs: Improving Automatic Snore Sound Classification With Synthesized Data

Zhang

Han

Qian

et al. 2020

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cope with this problem, we propose a novel data augmentation approach based on semi-supervised conditional Generative Adversarial Networks (scGANs), which aims to automatically learn a mapping strategy from a random noise space to original data distribution. The proposed approach has the capability of well synthesizing 'realistic' high-dimensional data, while requiring no additional annotation process. To handle the mode collapse problem of GANs, we further introduce an ensemble strategy to enhance the diversity of the generated data. The systematic experiments conducted on a widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based systems can remarkably outperform other classic data augmentation systems, and are also competitive to other recently reported systems for ASSC.

show abstract

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

Cited by 31 publications

References 61 publications

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Snore-GANs: Improving Automatic Snore Sound Classification With Synthesized Data

Contact Info

Product

Resources

About