Devices for home detection of obstructive sleep apnea: A review

Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Ravelo-García, Antonio G.; Morgado‐Dias, Fernando; Penzel, Thomas

doi:10.1016/j.smrv.2018.02.004

Cited by 103 publications

(72 citation statements)

References 86 publications

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“…However, the developed algorithm uses a simple classifier (LR) and easy to implement features. Both are relevant characteristics for a small and non-invasive HMD [13]. The other analyzed methods reported a lower global accuracy.…”

Section: Discussionmentioning

confidence: 88%

“…Such devices can be categorized as a Type IV according to the American Academy of Sleep Medicine categorization [12]. The hypothesis is supported by the conclusions of two reviews, one that examined HMD for OSA examination [13] and the other that examined proposed methods for the most common source sensors employed for OSA diagnosis [14]. It was verified that combining source sensors did not contribute to a substantial increase of the classification capability, suggesting that one of the sensors is dominating the examination.…”

mentioning

confidence: 99%

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An Oximetry Based Wireless Device for Sleep Apnea Detection

Mendonça

Mostafa

Morgado‐Dias

et al. 2020

Sensors

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Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.

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Section: Discussionmentioning

confidence: 88%

mentioning

confidence: 99%

An Oximetry Based Wireless Device for Sleep Apnea Detection

Mendonça

Mostafa

Morgado‐Dias

et al. 2020

Sensors

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“…Following the high prevalence, for clinical purposes, recording devices for sleep disordered breathing were reduced to the essentials [ 9 , 10 ▪ ]. The agreed essentials are airflow, respiratory effort, oxygen saturation, HR (or ECG or pulse rate), body position, some surrogate for sleep time (could be just self reported), sometimes snoring [ 10 ▪ ]. Devices based on these six channels/signals had been validated against the gold standard PSG and have proven to deliver accurate diagnoses when compared between centres [ 11 ].…”

Section: Home Sleep Apnoea Testingmentioning

confidence: 99%

Alternative algorithms and devices in sleep apnoea diagnosis: what we know and what we expect

Penzel

Fietze

Glos

2020

Current Opinion in Pulmonary Medicine

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Purpose of review Diagnosis of sleep apnoea was performed in sleep laboratories with polysomnography. This requires a room with supervision and presence of technologists and trained sleep experts. Today, clinical guidelines in most countries recommend home sleep apnoea testing with simple systems using six signals only. If criteria for signal quality, recording conditions, and patient selection are considered, then this is a reliable test with high accuracy. Recent findings Recently diagnostic tools for sleep apnoea diagnosis become even more simple: smartwatches and wearables with smart apps claim to diagnose sleep apnoea when these devices are tracking sleep and sleep quality as part of new consumer health checking. Alternative and new devices range from excellent diagnostic tools with high accuracy and full validation studies down to very low-quality tools which only result in random diagnostic reports. Due to the high prevalence of sleep apnoea, even a random diagnosis may match a real disorder sometimes. Summary Until now, there are no metrics established how to evaluate these alternative algorithms and simple devices. Proposals for evaluating smartwatches, smartphones, single-use sensors, and new algorithms are presented. New assessments may help to overcome current limitations in sleep apnoea severity metrics. Video abstract .

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“…For the 48 patients studied, a good correlation (r 2 = 0.723) and mean difference of 3.1 between the AHI from the two systems was found. The system described in this paper is now one of many nonlaboratory diagnostic systems for apnea on the market [9] with the majority intended for home based sleep testing (i.e. Type 3 and 4 sleep tests [10]).…”

Section: Ventilation and Respiratory Rate Monitoringmentioning

confidence: 99%

Journal of Clinical Monitoring and Computing 2018–2019 end of year summary: respiration

Karbing

Perchiazzi

Rees

et al. 2020

J Clin Monit Comput

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This paper reviews 28 papers or commentaries published in Journal of Clinical Monitoring and Computing in 2018 and 2019, within the field of respiration. Papers were published covering endotracheal tube cuff pressure monitoring, ventilation and respiratory rate monitoring, lung mechanics monitoring, gas exchange monitoring, CO 2 monitoring, lung imaging, and technologies and strategies for ventilation management.

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Devices for home detection of obstructive sleep apnea: A review

Cited by 103 publications

References 86 publications

An Oximetry Based Wireless Device for Sleep Apnea Detection

An Oximetry Based Wireless Device for Sleep Apnea Detection

Alternative algorithms and devices in sleep apnoea diagnosis: what we know and what we expect

Journal of Clinical Monitoring and Computing 2018–2019 end of year summary: respiration

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