To sleep, or not to sleep – that is the question, for polysomnography

Corlăteanu, Alexandru; Covanţev, Serghei; Botnaru, Victor; Sircu, Victoria; Nenna, Raffaella

doi:10.1183/20734735.007717

Cited by 14 publications

(17 citation statements)

References 28 publications

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“…The introduction of miniaturised devices means that CRPG is now feasible in the home environment15 16 and the scope to evaluate more ‘efficient ways of diagnosing sleep-disordered breathing’17 18 is paramount. We report lessons learnt from research and clinical experience of home CRPG in 255 children with diverse comorbidities.…”

Section: Introductionmentioning

confidence: 99%

Cardiorespiratory sleep studies at home: experience in research and clinical cohorts

et al. 2018

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ObjectiveTo evaluate the success rates of home cardiorespiratory polygraphy in children under investigation for sleep-disordered breathing and parent perspectives on equipment use at home.DesignProspective observational study.SettingSheffield, Evelina London and Southampton Children’s Hospitals.PatientsData are reported for 194 research participants with Down syndrome, aged 0.5–5.9 years across the three centres and 61 clinical patients aged 0.4–19.5 years from one centre, all of whom had home cardiorespiratory polygraphy including respiratory movements, nasal pressure flow, pulse oximetry, body position and motion.Main outcome measuresPercentage of home cardiorespiratory studies successfully acquiring ≥4 hours of artefact-free data at the first attempt. Parental report of ease of use of equipment and preparedness to repeat home diagnostics in the future.Results143/194 (74%; 95% CI 67% to 79%) of research participants and 50/61 (82%; 95% CI 71% to 90%) of clinical patients had successful home cardiorespiratory polygraphy at the first attempt. Some children required multiple attempts to achieve a successful study. Overall, this equated to 1.3 studies per research participant and 1.2 studies per clinical child. The median artefact-free sleep time for successful research studies was 515 min (range 261–673) and for clinical studies 442 min (range 291–583). 84% of research and 87% of clinical parents expressed willingness to repeat home cardiorespiratory polygraphy in the future. 67% of research parents found the equipment ‘easy or okay’ to use, while 64% of clinical parents reported it as ‘easy’ or ‘very easy’.ConclusionsHome cardiorespiratory polygraphy offers an acceptable approach to the assessment of sleep-disordered breathing in children.

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Section: Introductionmentioning

confidence: 99%

Cardiorespiratory sleep studies at home: experience in research and clinical cohorts

et al. 2018

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“…This was also not done in this study. Indeed, nevertheless polysomnography is costly and now obstructive sleep apnoea is often diagnosed with cheaper methods [18]. Questionnaires are becoming more widely used [19].…”

Section: Discussionmentioning

confidence: 99%

Correlation Between Predictors of Obstructive Sleep Apnoea

Otoghile

Oboite

Otoru

2019

JAMMR

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Background: Obstructive sleep apnoea is becoming a respiratory disorder of public health concern. Different obesity indices such as Mallampati score, Body Mass Index, neck circumference have been identified in the literature as predictors of obstructive sleep apnoea. Studies on the relationship between these variables is however limited particularly in Nigeria. The aim of this study was to evaluate the correlation between Mallampati score, Body Mass Index and neck circumference which are predictors of obstructive sleep apnoea. Methods: The study was done among commercial drivers. The risk of obstructive sleep apnoea was assessed with Berlin questionnaire. The participants were assessed for Mallampati score, Body Mass Index; their neck circumference was measured and the correlation between these variables was done. Data collated was analyzed with statistical package for social sciences (SPSS) Version 20.0 software. Results: There were 105 drivers recruited into this study, they were all males with a mean age of 44.8±12.03 years and 50.5% of the drivers had high risk of sleep apnoea. There was a positive Otoghile et al.; JAMMR, 28(9): 1-5, 2018; Article no.JAMMR.46651 2 correlation between Mallampati score, Body Mass Index and neck circumference. Conclusion: Mallampati score increase with increase in Body Mass Index and neck circumference which are all predictors of obstructive sleep apnoea. Original Research Article

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“…Sleep-related disorders such as sleep deprivation [6] and sleep apnea can be studied and diagnosed, and interventions can occur using ubiquitous sensing technologies [1]. Sleep clinics typically use Polysomnography (PSG), a test conducted to study sleep and to diagnose different forms of sleep disorders [4]. To date, PSG is considered as the most accurate method for diagnosing sleep-related problems and considered the gold standard in clinical sleep medicine.…”

Section: Introductionmentioning

confidence: 99%

“…To date, PSG is considered as the most accurate method for diagnosing sleep-related problems and considered the gold standard in clinical sleep medicine. However, it suffers from the fact that it is expensive, complex, time-consuming, and uncomfortable for the users [4]. In this early work, we aim to model a sleep classification system using an unobtrusive Ballistocardiographic (BCG)-based heart sensor signal collected from Dozee 1 (Fig.…”

Section: Introductionmentioning

confidence: 99%

DeepSleep

Rao

Ali

César

2019

Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Current techniques for tracking sleep are either obtrusive (Polysomnography) or low in accuracy (wearables). In this early work, we model a sleep classification system using an unobtrusive Ballistocardiographic (BCG)-based heart sensor signal collected from a commercially available pressuresensitive sensor sheet. We present DeepSleep, a hybrid deep neural network architecture comprising of CNN and LSTM layers. We further employed a 2-phase training strategy to build a pre-trained model and to tackle the limited dataset size. Our model results in a classification accuracy of 74%, 82%, 77% and 63% using Dozee BCG, MIT-BIH's ECG, Dozee's ECG and Fitbit's PPG datasets, respectively. Furthermore, our model shows a positive correlation (r = 0.43) with the SATED perceived sleep quality scores. We show that BCG signals are effective for long-term sleep monitoring, but currently not suitable for medical diagnostic purposes. CCS CONCEPTS • Human-centered computing → Ubiquitous computing.

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To sleep, or not to sleep – that is the question, for polysomnography

Cited by 14 publications

References 28 publications

Cardiorespiratory sleep studies at home: experience in research and clinical cohorts

Cardiorespiratory sleep studies at home: experience in research and clinical cohorts

Correlation Between Predictors of Obstructive Sleep Apnoea

DeepSleep

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