Sleep Architecture in Patients With Primary Snoring and Obstructive Sleep Apnea

Shahveisi, Kaveh; Jalali, Amir; Moloudi, Mohammad Raman; Moradi, Shima; Maroufi, Azad; Khazaie, Habibolah

doi:10.29252/nirp.bcn.9.2.147

Cited by 37 publications

(27 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To fully understand why the accuracy and the kappa coefficient of High AHI group are lower than Low AHI group, we go through the sleep architectures of high AHI subjects. A high percentage of the stage N1 is observed in the High AHI group, which is in accordance with the observation in [40] and [41]. In the clinical observation study, the sleep architecture changes in sleep apnea patients in comparison with normal subjects.…”

Section: B Performance Of the Personalized Hybrid Expert Systemsupporting

confidence: 87%

Towards a Hybrid Expert System Based on Sleep Event’s Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm

Chen

Ugon²,

Sun

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Identification of sleep stages is a fundamental step in clinical sleep analysis. Existing automatic sleep staging systems ignore two major issues: 1) Most of existing automatic sleep staging systems are using numerical classification methods without involving medical knowledge. These kinds of systems are not yet understood and accepted by physicians. 2) Individual variability sources are ignored. However, individual variability is observed in many aspects of sleep research (such as polysomnography recordings, sleep patterns, and sleep architecture). In this paper, a hybrid expert system is proposed to mimic the decisionmaking process of clinical sleep staging in accordance with the medical knowledge by using symbolic fusion. To formalize the medical guideline and knowledge, thresholds are used for translating the sleep events into symbols and the sleep event's threshold dependencies are analyzed for fully understanding the thresholds dependencies among different sleep stages and subjects. Meanwhile, the differential evolution algorithm is adopted to automate the setting-up of thresholds that are used in the symbolic fusion model and to provide personalized thresholds, which allows taking the individual variability into consideration. The robustness and clinical applicability of the proposed system are evaluated and demonstrated on a clinical dataset. The dataset is composed of 16 patients (nine males and seven females) and scored by physicians. Only 5% of the dataset is used for the training process to obtain the personalized thresholds. Then, these personalized thresholds are passed to the classification process, and the overall accuracy on the identification of five sleep stages reaches 80.09%. Using a small dataset for the training process, the proposed system not only drastically reduces the training set but also achieves favorable results compared with most of the existing works.

show abstract

Section: B Performance Of the Personalized Hybrid Expert Systemsupporting

confidence: 87%

Towards a Hybrid Expert System Based on Sleep Event’s Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm

Chen

Ugon²,

Sun

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Obstructive Sleep Apnea is one of the most common types of sleep-disordered breathing (SDB) (1)(2)(3). It is characterized by recurrent upper airway collapse during sleep, often leading to repeated awakening, sleep fragmentation, and reduced slowwave sleep (4,5). Increasingly, OSA is regarded as a systemic disease that impacts multiple organs of the metabolic, cardio-and cerebrovascular systems, elevating all-cause mortality (6,7).…”

Section: Introductionmentioning

confidence: 99%

EEG Power Spectral Analysis of Abnormal Cortical Activations During REM/NREM Sleep in Obstructive Sleep Apnea

Liu

Shen

et al. 2021

Front. Neurol.

View full text Add to dashboard Cite

Objective: To characterize electroencephalogram (EEG) power in different frequency bands during rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with obstructive sleep apnea (OSA).Methods: Retrospective data on 151 patients were collected and divided into three groups: primary snoring group (AHI < 5/h), mild-moderate OSA group (6 ≤ AHI < 30/h), and severe OSA group (AHI ≥ 30/h). EEG recordings in the frontal, central, and occipital regions were extracted from both REM and NREM sleep, to compute the normalized spectral power densities in the delta, theta, alpha, sigma, beta, and gamma frequency bands, using Fast Fourier Transform. Correlations between the computed EEG power and PSG parameters were analyzed.Results: In NREM sleep, elevated normalized power spectral density (PSD) in the delta band was observed in the severe OSA group compared to the other two groups. In contrast, the PSD of the other frequency bands showed a corresponding decrease in the severe OSA group. In REM sleep, similar changes were observed in the frontal region. Delta band PSD was positively correlated with Apnea Hypopnea Index (AHI) (r = 0.33), longest time of apnea, oxygen desaturation index (ODI) (r = 0.34), percent sleep time below 90% SaO2 (T90%) (r = 0.30), Arousal Index (ArI) (r = 0.29), and negatively correlated with N3%, minimum oxygen saturation (minSaO2).Conclusion: Our findings provide neurophysiological evidence for pathological cortical activation during REM/NREM sleep, which may be associated with the arousals and cognitive impairments in OSA. The technique of power spectral analysis could prove a potentially useful tool in complementing traditional PSG parameters in assessing disease burden to guide therapeutic decisions.

show abstract

“…While blocking the nose clearly increased the AHI, it may have also independently disturbed sleep because the spontaneous arousal index and wake after sleep onset time were also increased on the blocked nose night while total sleep time was reduced in contrast to what is typically observed in at least chronic OSA. 32 In addition, the OSA that was induced was associated with Figure 2 Motor sequence task performance in men and women with adequate data (n=12). The number of correct sequences typed during the motor sequence task over the twelve 30s trials conducted in the evening (E1-E12) and morning (M1-M12) on control (black square) and blocked nose (grey triangle) nights.…”

Section: Discussionmentioning

confidence: 99%

<p>Does Nasal Obstruction Induce Obstructive Sleep Apnea in Healthy Women?</p>

Pittaway

Ishkova

Bean

et al. 2020

NSS

View full text Add to dashboard Cite

Purpose: Obstructive sleep apnea (OSA) is less prevalent among women and is associated with different symptoms and consequences to OSA in men. The reasons for these differences are unknown and difficult to tease apart in clinical populations. If OSA could be temporarily induced in healthy men and women, the causes of some of these differences could be investigated. Nasal blocking has been used to induce OSA in healthy men but its effect in women has not been reported. Patients and Methods: A total of 14 healthy individuals (10 women) underwent inlaboratory diagnostic sleep studies on two occasions separated by a week. On one occasion, the nasal passages were blocked, whereas on the other occasion, participants slept naturally. In both conditions, a full-face mask was used to monitor respiratory events. Participants' self-reported sleepiness, mood and performance on a motor learning task were assessed in the evening and morning of both sleep studies. Furthermore, endothelial function and selfreported sleep quality were assessed in the morning following each study. Results: Nasal blockage induced OSA in healthy young (age=22±3 years) and slim (BMI=22.2 ±3.2 kg/m 2) women (control AHI=2.0±2.6, blocked AHI=33.1±36.7 events/hr, p=0.02). One night of OSAwas associated with poorer self-reported sleep quality (p<0.001) and increased self-reported snoring (p<0.04), choking and gasping during sleep (p<0.001) but was not associated with alterations in mood, neurocognitive or endothelial function on the following morning. Conclusion: Nasal blockage induces OSA in healthy, young, and normal weight women. However, whether the induced OSA is representative of naturally occurring OSA and the technique useful for future studies is unclear.

show abstract

Sleep Architecture in Patients With Primary Snoring and Obstructive Sleep Apnea

Cited by 37 publications

References 38 publications

Towards a Hybrid Expert System Based on Sleep Event’s Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm

Towards a Hybrid Expert System Based on Sleep Event’s Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm

EEG Power Spectral Analysis of Abnormal Cortical Activations During REM/NREM Sleep in Obstructive Sleep Apnea

<p>Does Nasal Obstruction Induce Obstructive Sleep Apnea in Healthy Women?</p>

Contact Info

Product

Resources

About