Sleep Staging Based on Autonomic Signals: A Multi-Center Validation Study

Hedner, Jan; White, David P.; Malhotra, Atul; Herscovici, Sarah; Pittman, Stephen D.; Zou, Ding; Grote, Ludger; Pillar, Giora

doi:10.5664/jcsm.1078

Cited by 123 publications

(104 citation statements)

References 28 publications

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“…Note that for the purpose of reducing between-subject variability in respiration, all the features are normalized (Z-score) for each overnight recording. We simply adopt a linear discriminant (LD) classifier which has been widely used for the task of sleep staging (Redmond et al 2007, Devot et al 2010, Foussier et al 2013, Long et al 2014a. The data including 48 entire-night recordings is randomly divided to 10 data subsets where each fold consists of four or five recordings and then we execute the sleep staging iteratively using a ten-fold cross-validation (CV).…”

Section: Sleep Stagingmentioning

confidence: 99%

Measuring dissimilarity between respiratory effort signals based on uniform scaling for sleep staging

Long¹,

Yang²,

Weysen³

et al. 2014

Physiol. Meas.

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Polysomnography (PSG) has been extensively studied for sleep staging, where sleep stages are usually classified as wake, rapid-eye-movement (REM) sleep, or non-REM (NREM) sleep (including light and deep sleep). Respiratory information has been proven to correlate with autonomic nervous activity that is related to sleep stages. For example, it is known that the breathing rate and amplitude during NREM sleep, in particular during deep sleep, are steadier and more regular compared to periods of wakefulness that can be influenced by body movements, conscious control, or other external factors. However, the respiratory morphology has not been well investigated across sleep stages. We thus explore the dissimilarity of respiratory effort with respect to its signal waveform or morphology. The dissimilarity measure is computed between two respiratory effort signal segments with the same number of consecutive breaths using a uniform scaling distance. To capture the property of signal morphological dissimilarity, we propose a novel window-based feature in a framework of sleep staging. Experiments were conducted with a data set of 48 healthy subjects using a linear discriminant classifier and a ten-fold cross validation. It is revealed that this feature can help discriminate between sleep stages, but with an exception of separating wake and REM sleep. When combining the new feature with 26 existing respiratory features, we achieved a Cohen's Kappa coefficient of 0.48 for 3-stage classification (wake, REM sleep and NREM sleep) and of 0.41 for 4-stage classification (wake, REM sleep, light sleep and deep sleep), which outperform the results obtained without using this new feature.

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Section: Sleep Stagingmentioning

confidence: 99%

Measuring dissimilarity between respiratory effort signals based on uniform scaling for sleep staging

Long¹,

Yang²,

Weysen³

et al. 2014

Physiol. Meas.

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“…The WP system used in this study is a home sleep testing (HST) system based on a wrist-worn device and a finger probe which acquires Peripheral Arterial Tone (PAT) signals and arterial oxygen saturation levels, together with actigraphy data from a 3D accelerometer that is embedded in the wrist unit, and an optional snoring and body position (SBP) sensor that is positioned under the sternal notch. The WP algorithm detects offline apnea/hypopnea events, respiratory effort-related arousals, and sleep/wake status, and determines sleep stages [8,[21][22][23][24].…”

Section: Watchpat Systemmentioning

confidence: 99%

“…In addition to accurate sleep/wake discrimination, the WP has also been shown to provide accurate determinations of REM stage sleep [21,22], as well as non-REM categorization into deep and light stages [23,24], and further, with the addition of an optional detector located below the supra-sternal notch, provides accurate measurements of snoring and body position [25].…”

Section: Introductionmentioning

confidence: 99%

Detecting central sleep apnea in adult patients using WatchPAT—a multicenter validation study

et al. 2019

Self Cite

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Study objectives To assess the accuracy of WatchPAT (WP-Itamar-Medical, Caesarea, Israel) enhanced with a novel systolic upstroke analysis coupled with respiratory movement analysis derived from a dedicated snoring and body position (SBP) sensor, to enable automated algorithmic differentiation between central sleep apnea (CSA) and obstructive sleep apnea (OSA) compared with simultaneous in-lab sleep studies with polysomnography (PSG). Methods Eighty-four patients with suspected sleep-disordered breathing (SDB) underwent simultaneous WP and PSG studies in 11 sleep centers. PSG scoring was blinded to the automatically analyzed WP data. Results Overall WP apnea-hypopnea index (AHI; mean ± SD) was 25.2 ± 21.3 (range 0.2-101) versus PSG AHI 24.4 ± 21.2 (range 0-110) (p = 0.514), and correlation was 0.87 (p < 0.001). Using a threshold of AHI ≥ 15, the sensitivity and specificity of WP versus PSG for diagnosing sleep apnea were 85% and 70% respectively and agreement was 79% (kappa = 0.867). WP central AHI (AHIc) was 4.2 ± 7.7 (range 0-38) versus PSG AHIc 5.9 ± 11.8 (range 0-63) (p = 0.034), while correlation was 0.90 (p < 0.001). Using a threshold of AHI ≥ 15, the sensitivity and specificity of WP versus PSG for diagnosing CSA were 67% and 100% respectively with agreement of 95% (kappa = 0.774), and receiver operator characteristic (ROC) area under the curve of 0.866, (p < 0.01). Using a threshold of AHI ≥ 10 showed comparable overall sleep apnea and CSA diagnostic accuracies. Conclusions These findings show that WP can accurately detect overall AHI and effectively differentiate between CSA and OSA. Brief summary Current knowledge/study rationale So far, the WatchPAT, an ambulatory sleep testing device based on peripheral arterial tone, could not separate central sleep apnea (CSA) from Obstructive Sleep Apnea (OSA). Following improvement of the algorithm, the aim of the present study was to validate the capability of the WP to distinguish CSA from OSA in a multicenter study. Study impact This study found that the WP can accurately detect overall AHI, and reasonably differentiate between CSA and OSA. This may further improve home-based diagnosis of sleep apnea.

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“…Sleep disorders research and treatment can also employ continuous monitoring of blood oxidation, which can assist in the detection of sleep staging and sleep architecture. Understanding of sleep pathology is important, for example, for treatment of patients with obstructive sleep apnea (OSA) [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

The Feasibility of Flat, Portable and Wireless Device for Non-Invasive Peripheral Oxygenation Measurement over the Entire Body

Ovadia‐Blechman¹,

Gino²,

Dandeker³

et al. 2016

JBiSE

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Peripheral oxygenation level (SpO2) can provide vital information on body functions. Continuous monitoring facilitates effective diagnosis and treatment and can even be lifesaving. Clinical device monitor SpO2 using a clip, and measure light transmission through the tissue. This method limits the body locations of the clip's placement and is sensitive to body movement, which hampers continuous SpO2 monitoring during wakefulness or sleep, thus decreasing its usability in clinics and its accessibility in homecare usage. We developed a portable, wireless, flat and low cost prototype for continuous monitoring of SpO2 that overcomes those limitations. The prototype enables convenient measurement in larger variety of body locations by spectrophotometric measurements of changes in the optical reflectance unlike other device that measure absorption through the tissue. The original design and signal processing enable reliable signal acquisition, synchronization and control. An Android's application was developed to provide a user friendly interface for results display on smartphones. The prototype's measurements were compared to commercial device that simultaneously measured heart rate frequency, transcutaneous oxygen tension (tcPO2) and SpO2. The prototype's measurements accurately reflected changes caused by blood pulses, were correlated to the heart rate, and were sensitive to changes in oxygen saturation. Excellent real time behavior and synchronization were demonstrated between the hardware and smartphone software. Our prototype thus enables convenient SpO2 measurement over the entire body, while maintaining accuracy comparable to commercial device. Its smartphone application enables * Corresponding author.Z. Ovadia-Blechman et al. 148accessible and understandable results display to patients, caregivers and healthcare professionals. The application's display and alert calibration flexibility facilitates the prototype's usage in changing medical requirements and for various disease and conditions. A device based on this prototype can monitor continuously and accurately patients' SpO2 without limiting their everyday activities or disturbing their sleep and can thus significantly improve their medical care in both clinics and home.

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Sleep Staging Based on Autonomic Signals: A Multi-Center Validation Study

Cited by 123 publications

References 28 publications

Measuring dissimilarity between respiratory effort signals based on uniform scaling for sleep staging

Measuring dissimilarity between respiratory effort signals based on uniform scaling for sleep staging

Detecting central sleep apnea in adult patients using WatchPAT—a multicenter validation study

The Feasibility of Flat, Portable and Wireless Device for Non-Invasive Peripheral Oxygenation Measurement over the Entire Body

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