scholarly journals Gaussian Mixture Models for Detecting Sleep Apnea Events Using Single Oronasal Airflow Record

2020 ◽  
Vol 10 (21) ◽  
pp. 7889
Author(s):  
Hisham ElMoaqet ◽  
Jungyoon Kim ◽  
Dawn Tilbury ◽  
Satya Krishna Ramachandran ◽  
Mutaz Ryalat ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Probability Model ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Cerebrovascular Diseases ◽  
Apnea Hypopnea Index ◽  
Related Disorder ◽  
Machine Learning Classification ◽  
Obstructive Sleep ◽  
Rule Based Classifier

Sleep apnea is a common sleep-related disorder that significantly affects the population. It is characterized by repeated breathing interruption during sleep. Such events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score sleep-related events. To address these limitations, many previous studies have proposed and implemented automatic scoring processes based on fewer sensors and machine learning classification algorithms. However, alternative device technologies developed for both home and hospital still have limited diagnostic accuracy for detecting apnea events even though many of the previous investigational algorithms are based on multiple physiological channel inputs. In this paper, we propose a new probabilistic algorithm based on (only) oronasal respiration signal for automated detection of apnea events during sleep. The proposed model leverages AASM recommendations for characterizing apnea events with respect to dynamic changes in the local respiratory airflow baseline. Unlike classical threshold-based classification models, we use a Gaussian mixture probability model for detecting sleep apnea based on the posterior probabilities of the respective events. Our results show significant improvement in the ability to detect sleep apnea events compared to a rule-based classifier that uses the same classification features and also compared to two previously published studies for automated apnea detection using the same respiratory flow signal. We use 96 sleep patients with different apnea severity levels as reflected by their Apnea-Hypopnea Index (AHI) levels. The performance was not only analyzed over obstructive sleep apnea (OSA) but also over other types of sleep apnea events including central and mixed sleep apnea (CSA, MSA). Also the performance was comprehensively analyzed and evaluated over patients with varying disease severity conditions, where it achieved an overall performance of TPR=88.5%, TNR=82.5%, and AUC=86.7%. The proposed approach contributes a new probabilistic framework for detecting sleep apnea events using a single airflow record with an improved capability to generalize over different apnea severity conditions

scholarly journals Estimating a Sleep Apnea Hypopnea Index Based on the ERB Correlation Dimension of Snore Sounds

2021 ◽  
Vol 2 ◽  
Author(s):  
Limin Hou ◽  
Qiang Pan ◽  
Hongliang Yi ◽  
Dan Shi ◽  
Xiaoyu Shi ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Correlation Dimension ◽  
Gaussian Mixture Models ◽  
Training Group ◽  
Gaussian Mixture ◽  
Apnea Hypopnea Index ◽  
Effective Parameters ◽  
Snore Sounds ◽  
Severity Levels ◽  
New Perspective

This paper proposes a new perspective of analyzing non-linear acoustic characteristics of the snore sounds. According to the ERB (Equivalent Rectangular Bandwidth) scale used in psychoacoustics, the ERB correlation dimension (ECD) of the snore sound was computed to feature different severity levels of sleep apnea hypopnea syndrome (SAHS). For the training group of 93 subjects, snore episodes were manually segmented and the ECD parameters of the snores were extracted, which established the gaussian mixture models (GMM). The nocturnal snore sound of the testing group of another 120 subjects was tested to detect SAHS snores, thus estimating the apnea hypopnea index (AHI), which is called AHIECD. Compared to the AHIPSG value of the gold standard polysomnography (PSG) diagnosis, the estimated AHIECD achieved an accuracy of 87.5% in diagnosis the SAHS severity levels. The results suggest that the ECD vectors can be effective parameters for screening SAHS.

Abstract WP153: Severe Obstructive Sleep Apnea is Associated With Presence of Intracranial Cerebral Atherosclerosis

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Tae-Jin Song ◽  
Yoonkyung Chang ◽  
Yong-Jae Kim ◽  
Hyang Woon Lee
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Cerebrovascular Diseases ◽  
Apnea Hypopnea Index ◽  
Regression Analyses ◽  
Arousal Index ◽  
Asian Populations ◽  
Cerebral Atherosclerosis ◽  
Obstructive Sleep ◽  
The Mean

Background and significance: Intracranial cerebral atherosclerosis (ICAS) is closely associated with risk of cerebrovascular diseases, especially in Asian populations. Obstructive sleep apnea (OSA) is associated with systemic atherosclerosis, but it is not clear whether OSA is an independent risk factor of ICAS. We aimed to investigate the association between ICAS and apnea-hypopnea index (AHI), representing severity of OSA, in suspected OSA patients. Methods: We included 142 patients who suspected OSA from Ewha Sleep Center who underwent brain MR angiography (MRA) and polysomnography (PSG). We also investigated the presence and burden of ICAS on MRA and AHI on PSG. Results: The mean patient age was 59.7 ± 13.1 years, and 54.2% (77/142) were male. The mean AHI was 19.8 ± 20.9, and 17 patients (12.0%) had ICAS. Higher AHI was noted in patients with ICAS compared to those without ICAS (38.2 ± 27.1 vs. 17.3 ± 18.7, p = 0.023). The burden of ICAS was positively correlated with severity of OSA (p for trend = 0.013)(Figure 1). In multivariable binary logistic analyses, after adjusting for age, sex, hypertension, hyperlipidemia, smoking, arousal index and minimum SaO 2 , severe OSA was independently related with presence of ICAS (odds ratio (OR): 14.05, 95% confidence interval (CI): 1.28 - 153.64, p = 0.030). Likewise, in multivariable ordinal logistic regression analyses, severe OSA was marginally associated with burden of ICAS (OR: 8.47, 95% CI: 0.74 - 95.77, p = 0.084). Conclusion: Our findings suggest that severe OSA is associated with presence of ICAS in patients with suspected OSA.

Improvement in Obstructive Sleep Apnea (OSA) in Super Morbidly Obese Patients After Bariatric Surgery

2020 ◽  
Vol 103 (8) ◽  
pp. 725-728
Keyword(s):  
Bariatric Surgery ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Lifestyle Modification ◽  
Apnea Hypopnea Index ◽  
Morbidly Obese ◽  
Obese Patients ◽  
Obstructive Sleep ◽  
Objective Evidence

Background: Lifestyle modification is the mainstay therapy for obese patients with obstructive sleep apnea (OSA). However, most of these patients are unable to lose the necessary weight, and bariatric surgery (BS) has been proven to be an effective modality in selected cases. Objective: To provide objective evidence that BS can improve OSA severity. Materials and Methods: A prospective study was conducted in super morbidly obese patients (body mass index [BMI] greater than 40 kg/m² or BMI greater than 35 kg/m² with uncontrolled comorbidities) scheduled for BS. Polysomnography (PSG) was performed for preoperative assessment and OSA was treated accordingly. After successful surgery, patients were invited to perform follow-up PSG at 3, 6, and 12 months. Results: Twenty-four patients with a mean age of 35.0±14.0 years were enrolled. After a mean follow-up period of 7.8±3.4 months, the mean BMI, Epworth sleepiness scale (ESS), and apnea-hypopnea index (AHI) significantly decreased from 51.6±8.7 to 38.2±6.8 kg/m² (p<0.001), from 8.7±5.9 to 4.7±3.5 (p=0.003), and from 87.6±38.9 to 28.5±21.5 events/hour (p<0.001), respectively. Conclusion: BS was shown to dramatically improve clinical and sleep parameters in super morbidly obese patients. Keywords: Morbid obesity, Bariatric surgery, Obstructive sleep apnea (OSA)

Aerodynamics Analysis of the Impact of Nasal Surgery on Patients with Obstructive Sleep Apnea and Nasal Obstruction

ORL ◽  
2021 ◽  
pp. 1-8
Author(s):  
Lifeng Li ◽  
Demin Han ◽  
Hongrui Zang ◽  
Nyall R. London
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Nasal Obstruction ◽  
Upper Airway ◽  
Apnea Hypopnea Index ◽  
Nasal Surgery ◽  
Subjective Sleep Quality ◽  
Obstructive Sleep ◽  
Significant Difference ◽  
The Impact

<b><i>Objective:</i></b> The purpose of this study was to evaluate the effects of nasal surgery on airflow characteristics in patients with obstructive sleep apnea (OSA) by comparing the alterations of airflow characteristics within the nasal and palatopharyngeal cavities. <b><i>Methods:</i></b> Thirty patients with OSA and nasal obstruction who underwent nasal surgery were enrolled. A pre- and postoperative 3-dimensional model was constructed, and alterations of airflow characteristics were assessed using the method of computational fluid dynamics. The other subjective and objective clinical indices were also assessed. <b><i>Results:</i></b> By comparison with the preoperative value, all postoperative subjective symptoms statistically improved (<i>p</i> &#x3c; 0.05), while the Apnea-Hypopnea Index (AHI) changed little (<i>p</i> = 0.492); the postoperative airflow velocity and pressure in both nasal and palatopharyngeal cavities, nasal and palatopharyngeal pressure differences, and total upper airway resistance statistically decreased (all <i>p</i> &#x3c; 0.01). A significant difference was derived for correlation between the alteration of simulation metrics with subjective improvements (<i>p</i> &#x3c; 0.05), except with the AHI (<i>p</i> &#x3e; 0.05). <b><i>Conclusion:</i></b> Nasal surgery can decrease the total resistance of the upper airway and increase the nasal airflow volume and subjective sleep quality in patients with OSA and nasal obstruction. The altered airflow characteristics might contribute to the postoperative reduction of pharyngeal collapse in a subset of OSA patients.

scholarly journals High Plasma Cystine Levels Are Associated with Blood Pressure and Reversed by CPAP in Patients with Obstructive Sleep Apnea

2021 ◽  
Vol 10 (7) ◽  
pp. 1387
Author(s):  
Raphael Boneberg ◽  
Anita Pardun ◽  
Lena Hannemann ◽  
Olaf Hildebrandt ◽  
Ulrich Koehler ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Mononuclear Cells ◽  
Vascular Risk Factor ◽  
High Plasma ◽  
Apnea Hypopnea Index ◽  
Hypoxic Stress ◽  
Peripheral Blood Mononuclear ◽  
Obstructive Sleep

Obstructive sleep apnea (OSA) independent of obesity (OBS) imposes severe cardiovascular risk. To what extent plasma cystine concentration (CySS), a novel pro-oxidative vascular risk factor, is increased in OSA with or without OBS is presently unknown. We therefore studied CySS together with the redox state and precursor amino acids of glutathione (GSH) in peripheral blood mononuclear cells (PBMC) in untreated male patients with OSA (apnea-hypopnea-index (AHI) > 15 h−1, n = 28) compared to healthy male controls (n = 25) stratifying for BMI ≥ or < 30 kg m−2. Fifteen OSA patients were reassessed after 3–5-months CPAP. CySS correlated with cumulative time at an O2-saturation <90% (Tu90%) (r = 0.34, p < 0.05) beside BMI (r = 0.58, p < 0.001) and was higher in subjects with “hypoxic stress” (59.4 ± 2.0 vs. 50.1 ± 2.7 µM, p < 0.01) defined as Tu90% ≥ 15.2 min (corresponding to AHI ≥ 15 h−1). Moreover, CySS significantly correlated with systolic (r = 0.32, p < 0.05) and diastolic (r = 0.31, p < 0.05) blood pressure. CPAP significantly lowered CySS along with blood pressure at unchanged BMI. Unexpectedly, GSH antioxidant capacity in PBMC was increased with OSA and reversed with CPAP. Plasma CySS levels are increased with OSA-related hypoxic stress and associated with higher blood pressure. CPAP decreases both CySS and blood pressure. The role of CySS in OSA-related vascular endpoints and their prevention by CPAP warrants further studies.

scholarly journals Analysis of nocturnal desaturation waveforms using algorithms in patients with idiopathic pulmonary fibrosis

2021 ◽  
Author(s):  
Yuichiro Yasuda ◽  
Tatsuya Nagano ◽  
Shintaro Izumi ◽  
Mina Yasuda ◽  
Kosuke Tsuruno ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Diagnostic Algorithm ◽  
Sleep Time ◽  
Periodic Pattern ◽  
Apnea Hypopnea Index ◽  
Apnea Test ◽  
Eligibility Criteria ◽  
Obstructive Sleep

Abstract Purpose Sleep-disordered breathing is recognized as a comorbidity in patients with idiopathic pulmonary fibrosis (IPF). Among them, nocturnal hypoxemia has been reported to be associated with poor prognosis and disease progression. We developed a diagnostic algorithm to classify nocturnal desaturation from percutaneous oxygen saturation (SpO2) waveform patterns: sustained pattern, periodic pattern, and intermittent pattern. We then investigated the prevalence of nocturnal desaturation and the association between the waveform patterns of nocturnal desaturation and clinical findings of patients with IPF. Methods We prospectively enrolled patients with IPF from seven general hospitals between April 2017 and March 2020 and measured nocturnal SpO2 and nasal airflow by using a home sleep apnea test. An algorithm was used to classify the types of nocturnal desaturation. We evaluated the association between sleep or clinical parameters and each waveform pattern of nocturnal desaturation. Results Among 60 patients (47 men) who met the eligibility criteria, there were 3 cases with the sustained pattern, 49 cases with the periodic pattern, and 41 cases with the intermittent pattern. Lowest SpO2 during sleep and total sleep time spent with SpO2 < 90% were associated with the sustained pattern, and apnea–hypopnea index was associated with the intermittent pattern. Conclusion We demonstrated the prevalence of each waveform and association between each waveform and sleep parameters in patients with IPF. This classification algorithm may be useful to predict the degree of hypoxemia or the complication of obstructive sleep apnea.

scholarly journals Altered K-complex morphology during sustained inspiratory airflow limitation is associated with next-day lapses in vigilance in obstructive sleep apnea

SLEEP ◽  
2021 ◽  
Author(s):  
Ankit Parekh ◽  
Korey Kam ◽  
Anna E Mullins ◽  
Bresne Castillo ◽  
Asem Berkalieva ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Spectral Characteristics ◽  
Sleep Time ◽  
Airflow Limitation ◽  
Apnea Hypopnea Index ◽  
Complex Morphology ◽  
Obstructive Sleep ◽  
Inspiratory Airflow Limitation ◽  
Psychomotor Vigilance

Abstract Study Objectives Determine if changes in K-complexes associated with sustained inspiratory airflow limitation (SIFL) during N2 sleep are associated with next-day vigilance and objective sleepiness. Methods Data from thirty subjects with moderate-to-severe obstructive sleep apnea who completed three in-lab polysomnograms: diagnostic, on therapeutic continuous positive airway pressure (CPAP), and on suboptimal CPAP (4 cmH2O below optimal titrated CPAP level) were analyzed. Four 20-min psychomotor vigilance tests (PVT) were performed after each PSG, every 2 h. Changes in the proportion of spontaneous K-complexes and spectral characteristics surrounding K-complexes were evaluated for K-complexes associated with both delta (∆SWAK), alpha (∆αK) frequencies. Results Suboptimal CPAP induced SIFL (14.7 (20.9) vs 2.9 (9.2); %total sleep time, p &lt; 0.001) with a small increase in apnea–hypopnea index (AHI3A: 6.5 (7.7) vs 1.9 (2.3); p &lt; 0.01) versus optimal CPAP. K-complex density (num./min of stage N2) was higher on suboptimal CPAP (0.97 ± 0.7 vs 0.65±0.5, #/min, mean ± SD, p &lt; 0.01) above and beyond the effect of age, sex, AHI3A, and duration of SIFL. A decrease in ∆SWAK with suboptimal CPAP was associated with increased PVT lapses and explained 17% of additional variance in PVT lapses. Within-night during suboptimal CPAP K-complexes appeared to alternate between promoting sleep and as arousal surrogates. Electroencephalographic changes were not associated with objective sleepiness. Conclusions Sustained inspiratory airflow limitation is associated with altered K-complex morphology including the increased occurrence of K-complexes with bursts of alpha as arousal surrogates. These findings suggest that sustained inspiratory flow limitation may be associated with nonvisible sleep fragmentation and contribute to increased lapses in vigilance.

scholarly journals Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

2021 ◽  
Author(s):  
Satoru Tsuiki ◽  
Takuya Nagaoka ◽  
Tatsuya Fukuda ◽  
Yuki Sakamoto ◽  
Fernanda R. Almeida ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Apnea Hypopnea Index ◽  
Cephalometric Analysis ◽  
Obstructive Sleep ◽  
Main Region

Abstract Purpose In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. Methods A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison. Results The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75. Conclusions A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

scholarly journals Snoring: a source of noise pollution and sleep apnea predictor

SLEEP ◽  
2019 ◽  
Vol 43 (6) ◽  
Author(s):  
Mudiaga Sowho ◽  
Francis Sgambati ◽  
Michelle Guzman ◽  
Hartmut Schneider ◽  
Alan Schwartz
Keyword(s):  
Sleep Apnea ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Noise Pollution ◽  
Apnea Hypopnea Index ◽  
Sleep Study ◽  
Sound Levels ◽  
Obstructive Sleep ◽  
Single Night ◽  
The Relationship

Abstract Snoring is a highly prevalent condition associated with obstructive sleep apnea (OSA) and sleep disturbance in bed partners. Objective measurements of snoring in the community, however, are limited. The present study was designed to measure sound levels produced by self-reported habitual snorers in a single night. Snorers were excluded if they reported nocturnal gasping or had severe obesity (BMI &gt; 35 kg/m2). Sound was measured by a monitor mounted 65 cm over the head of the bed on an overnight sleep study. Snoring was defined as sound ≥40 dB(A) during flow limited inspirations. The apnea hypopnea index (AHI) and breath-by-breath peak decibel levels were measured. Snore breaths were tallied to determine the frequency and intensity of snoring. Regression models were used to determine the relationship between objective measures of snoring and OSA (AHI ≥ 5 events/h). The area under the curve (AUC) for the receiver operating characteristic (ROC) was used to predict OSA. Snoring intensity exceeded 45 dB(A) in 66% of the 162 participants studied, with 14% surpassing the 53 dB(A) threshold for noise pollution. Snoring intensity and frequency were independent predictors of OSA. AUCs for snoring intensity and frequency were 77% and 81%, respectively, and increased to 87% and 89%, respectively, with the addition of age and sex as predictors. Snoring represents a source of noise pollution in the bedroom and constitutes an important target for mitigating sound and its adverse effects on bed partners. Precise breath-by-breath identification and quantification of snoring also offers a way to risk stratify otherwise healthy snorers for OSA.

scholarly journals Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 267
Author(s):  
Duan Liang ◽  
Shan Wu ◽  
Lan Tang ◽  
Kaicheng Feng ◽  
Guanzheng Liu
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sample Entropy ◽  
Biomedical Signal Processing ◽  
Apnea Hypopnea Index ◽  
Short Term ◽  
Autonomic Nervous System Dysfunction ◽  
Obstructive Sleep ◽  
Hrv Analysis ◽  
Frequency Power

Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (p < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (|r| = 0.602, p < 0.05), NPSampEn (|r| = 0.756, p < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal.

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