Diagnosis of Sleep-Disordered Breathing in the Home Environment

Dzięciołowska-Baran, Edyta; Gawlikowska-Sroka, Aleksandra; Szczurowski, Jacek

doi:10.1007/5584_2020_497

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Although, whilst previously believed that patients with normal BMI were less likely to have OSAS, 20–40% of patients with OSAS are not obese, 30 and most patients with moderate OSAS have BMIs within the normal range. 19 Nevertheless, our study found a relationship among obesity, age, and sex. In terms of obesity, the mean BMI was 29.29 ± 5.01, 27.07 ± 4.25, 25.71 ± 5.67, and 23.39 ± 3.44 in the severe OSAS, moderate OSAS, mild OSAS, and healthy groups, respectively.…”

Section: Discussioncontrasting

confidence: 59%

“…The use of the HST to diagnose OSAS remains controversial. 19 The arguments in using HST are regarding whether a PM can be used as the final diagnostic tool and whether the sensitivity of a patient self-operated PM is sufficient. These arguments were especially prevalent in the past when the sensitivity observed in most studies using PMs to detect OSAS was insufficient.…”

Section: Discussionmentioning

confidence: 99%

“… 21 Using a PM has high clinical value, as the sensitivity is as high as 90%. 19 , 22 Type 3 PM is the most widely accepted among the PM types. 17 The sensitivity and specificity of Type 3 PM (ApneaLink Air) in identifying OSAS (AHI ≥ 5) were 85% and 50%, respectively.…”

Section: Discussionmentioning

confidence: 99%

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Prevalence of Obstructive Sleep Apnea Using Home Sleep Test in Taiwan During the Coronavirus Disease Pandemic

Chou,

Hsu,

Twu

et al. 2023

NSS

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Background Obstructive sleep apnea syndrome (OSAS) is a common disorder associated with serious sequelae. The current gold standard diagnostic method, polysomnography, is costly and time consuming and requires patients to stay overnight at a facility. Aim This study aimed to reveal the prevalence of OSAS in general adult population using a home sleep test (HST) during the coronavirus disease 2019 (COVID-19) pandemic. Methods This prospective cohort study was conducted by the Department of Otolaryngology, Taipei City Hospital, Taipei, Taiwan, between January 2020 and December 2021. A total of 1372 patients aged 30–70 years completed an HST using a Type 3 portable sleep monitor (PM). The apnea-hypopnea index (AHI) was analyzed to assess the association of OSAS with age, body mass index (BMI), sex, Epworth Sleepiness Scale (ESS) and the Sleep Apnea Risk Assessment questionnaire (STOP-Bang questionnaire) rating. Results The mean age of the patients (782 men, 57%; 590 women, 43%) was 49.24 ± 11.04 years. OSAS was detected in 954 (69.5%) patients with 399 (29.1%) mild OSAS; 246 (17.9%) moderate OSAS; and 309 (22.5%) severe OSAS. Among these, the prevalence of moderate-to-severe OSAS was 143 (10.4%) in women and 412 (30.0%) in men. The mean age was the highest (51.29 ± 11.29) in the mild OSAS group and lowest (47.08 ± 10.87) in the healthy group. OSAS severity was greater with increasing BMI, 23.39 ± 3.44 in the healthy group and 29.29 ± 5.01 in the severe OSAS group. A positive correlation was also noted between the ESS/STOP-Bang questionnaire rating and OSAS severity. Conclusion The prevalence of OSAS in Taiwan was 69.5% in our study. It showed strong evidence that OSAS has important public health consequences and PMs are simple, fast, feasible, and cost-effective tools for OSAS screening in the home environment, especially during the COVID-19 pandemic.

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Section: Discussioncontrasting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

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Prevalence of Obstructive Sleep Apnea Using Home Sleep Test in Taiwan During the Coronavirus Disease Pandemic

Chou,

Hsu,

Twu

et al. 2023

NSS

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“…This can be explained by the simplified attachment of the ALA with fewer channels. One prior study also investigated the convenience of the ALA, in which 98.5% of patients were positive about its use, 67.6% assessed the comfort very well and 32.4% only mentioned minor comments regarding the nasal cannula or oximeter ( 21 ).…”

Section: Discussionmentioning

confidence: 99%

Cardiorespiratory Polygraphy for Detection of Obstructive Sleep Apnea in Patients With Atrial Fibrillation

Delesie

Knaepen

Verbraecken

et al. 2021

Front. Cardiovasc. Med.

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Background: Obstructive sleep apnea (OSA) is a modifiable risk factor of atrial fibrillation (AF) but is underdiagnosed in these patients due to absence of good OSA screening pathways. Polysomnography (PSG) is the gold standard for diagnosing OSA but too resource-intensive as a screening tool. We explored whether cardiorespiratory polygraphy (PG) devices using an automated algorithm for Apnea-Hypopnea Index (AHI) determination can meet the requirements of a good screening tool in AF patients.Methods: This prospective study validated the performance of three PGs [ApneaLink Air (ALA), SOMNOtouch RESP (STR) and SpiderSAS (SpS)] in consecutive AF patients who were referred for PSG evaluation. Patients wore one of the three PGs simultaneously with PSG, and a different PG during each of three consecutive nights at home. Severity of OSA was classified according to the AHI during PSG (<5 = no OSA, 5–14 = mild, 15–30 = moderate, >30 = severe).Results: Of the 100 included AF patients, PSG diagnosed at least moderate in 69% and severe OSA in 33%. Successful PG execution at home was obtained in 79.1, 80.2 and 86.8% of patients with the ALA, STR and SpS, respectively. For the detection of clinically relevant OSA (AHI ≥ 15), an area under the curve of 0.802, 0.772 and 0.803 was calculated for the ALA, STR and SpS, respectively.Conclusions: This study indicates that home-worn PGs with an automated AHI algorithm can be used as OSA screening tools in AF patients. Based on an appropriate AHI cut-off value for each PG, the device can guide referral for definite PSG diagnosis.

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“…Unlike PSG, these approaches are mostly based on measuring physiological signals, such as airflow [1], [2], thoracic signal [3], abdominal signal [4], [5] or oxygen saturation [6]- [8]. However, they either induce discomfort caused by electrodes, enforce limited movements due to gauges and cables, or may have their results affected by the potential psychological consequences due to being "monitored", possibly in an institutional environment or in home monitoring [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Azimi

Bouchard

et al. 2020

IEEE Access

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Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the bestperforming model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA. INDEX TERMS Biomedical measurement, data analysis, deep learning, machine learning, patient monitoring, pressure measurement, central sleep apnea detection.

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Diagnosis of Sleep-Disordered Breathing in the Home Environment

Cited by 8 publications

References 12 publications

Prevalence of Obstructive Sleep Apnea Using Home Sleep Test in Taiwan During the Coronavirus Disease Pandemic

Prevalence of Obstructive Sleep Apnea Using Home Sleep Test in Taiwan During the Coronavirus Disease Pandemic

Cardiorespiratory Polygraphy for Detection of Obstructive Sleep Apnea in Patients With Atrial Fibrillation

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

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