Self-Applied Home Sleep Recordings

Korkalainen, Henri; Nikkonen, Sami; Kainulainen, Samu; Dwivedi, Amit Krishna; Myllymaa, Sami; Leppänen, Timo; Töyräs, Juha

doi:10.1016/j.jsmc.2021.07.003

Cited by 13 publications

(6 citation statements)

References 56 publications

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“…The use of CST for OSA screening has already been reviewed and subjected to comprehensive theoretical analyses (Baptista et al, 2022; Baron et al, 2018; Fino & Mazzetti, 2019; Korkalainen et al, 2021; Uddin et al, 2018). At least two meta‐analyses regarding CST for the screening of OSA have already been published (Kim et al, 2022; Rosa et al, 2018).…”

Section: Discussion and Expected Resultsmentioning

confidence: 99%

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta‐analysis of diagnostic test accuracy

Pires

Arnardóttir

Íslind

et al. 2023

Journal of Sleep Research

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Summary There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep‐disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta‐analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full‐text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea–hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch‐by‐epoch and event‐by‐event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta‐analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta‐analysis will be performed for continuous outcomes using the DerSimonian and Laird random‐effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

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Section: Discussion and Expected Resultsmentioning

confidence: 99%

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta‐analysis of diagnostic test accuracy

Pires

Arnardóttir

Íslind

et al. 2023

Journal of Sleep Research

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“…The authors concluded that the usage of alternative measures to the standard AHI, such as the HB might better represent treatment effects on the ventilatory deficit associated with upper airway obstruction. In a recent review, Korkalainen et al (2021b) discussed self-applied home sleep recordings including wearable sensing solutions and AI-based scoring for screening and longterm monitoring of sleep disorders. Besides the obvious advantages in clinical practice, the larger scale and higher throughput of AIenabled HSATs may also facilitate larger population-wide research studies that help us understand the link between SDB and other health conditions and outcomes.…”

Section: Discussionmentioning

confidence: 99%

Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Anderer¹,

Ross²,

Cerny³

et al. 2023

Front. Sleep

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Human experts scoring sleep according to the American Academy of Sleep Medicine (AASM) rules are forced to select, for every 30-second epoch, one out of five stages, even if the characteristics of the neurological signals are ambiguous, a very common occurrence in clinical studies. Moreover, experts cannot score sleep in studies where these signals have not been recorded, such as in home sleep apnea testing (HSAT). In this topic review we describe how artificial intelligence can provide consistent and reliable scoring of sleep stages based on neurological signals recorded in polysomnography (PSG) and on cardiorespiratory signals recorded in HSAT. We also show how estimates of sleep stage probabilities, usually displayed as hypnodensity graph, can be used to quantify sleep stage ambiguity and stability. As an example of the application of hypnodensity in the characterization of sleep disordered breathing (SDB), we compared 49 patients with sleep apnea to healthy controls and revealed a severity-depending increase in ambiguity and decrease in stability during non-rapid eye movement (NREM) sleep. Moreover, using autoscoring of cardiorespiratory signals, we show how HSAT-derived apnea-hypopnea index and hypoxic burden are well correlated with the PSG indices in 80 patients, showing how using this technology can truly enable HSATs as alternatives to PSG to diagnose SDB.

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“…One of the main objectives of adopting these new practices is to bring sleep studies from hospitals into patients’ homes (Arnardottir et al, 2022; Korkalainen et al, 2021). Currently, attended polysomnography (PSG) in a sleep laboratory is the ‘gold standard’ measurement for diagnostics of SDB (AAST, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, attended polysomnography (PSG) in a sleep laboratory is the ‘gold standard’ measurement for diagnostics of SDB (AAST, 2012). Although a valid method for differential diagnostics of sleep disorders, PSG comes with several disadvantages (Hirshkowitz, 2016; Korkalainen et al, 2021). The limited availability, high costs, and in‐person overnight supervision of PSG have made portable monitoring and home sleep apnea testing (HSAT) a more practical option for screening and diagnosing SDB (Berry et al, 2020; Bruyneel & Ninane, 2014; Ghegan et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, a substantial amount of research has been conducted to develop sensors that could be self‐applied by subjects at home (Korkalainen et al, 2021). In addition, many other novel sleep monitoring technologies, such as in‐ear EEG, smart rings, and watches have been developed (Altini & Kinnunen, 2021; Cay et al, 2022; da Silva Souto et al, 2021; De Zambotti et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Self‐applied somnography: technical feasibility of electroencephalography and electro‐oculography signal characteristics in sleep staging of suspected sleep‐disordered adults

Rusanen,

Korkalainen,

Gretarsdottir

et al. 2023

Journal of Sleep Research

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SummarySleep recordings are increasingly being conducted in patients’ homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self‐application. To overcome this, self‐applied forehead montages with electroencephalography and electro‐oculography sensors have been developed. We evaluated the technical feasibility of a self‐applied electrode set from Nox Medical (Reykjavik, Iceland) through home sleep recordings of healthy and suspected sleep‐disordered adults (n = 174) in the context of sleep staging. Subjects slept with a double setup of conventional type II polysomnography sensors and self‐applied forehead sensors. We found that the self‐applied electroencephalography and electro‐oculography electrodes had acceptable impedance levels but were more prone to losing proper skin–electrode contact than the conventional cup electrodes. Moreover, the forehead electroencephalography signals recorded using the self‐applied electrodes expressed lower amplitudes (difference 25.3%–43.9%, p < 0.001) and less absolute power (at 1–40 Hz, p < 0.001) than the polysomnography electroencephalography signals in all sleep stages. However, the signals recorded with the self‐applied electroencephalography electrodes expressed more relative power (p < 0.001) at very low frequencies (0.3–1.0 Hz) in all sleep stages. The electro‐oculography signals recorded with the self‐applied electrodes expressed comparable characteristics with standard electro‐oculography. In conclusion, the results support the technical feasibility of the self‐applied electroencephalography and electro‐oculography for sleep staging in home sleep recordings, after adjustment for amplitude differences, especially for scoring Stage N3 sleep.

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Self-Applied Home Sleep Recordings

Cited by 13 publications

References 56 publications

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta‐analysis of diagnostic test accuracy

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta‐analysis of diagnostic test accuracy

Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Self‐applied somnography: technical feasibility of electroencephalography and electro‐oculography signal characteristics in sleep staging of suspected sleep‐disordered adults

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