Sleep Architecture, Sleep-Disordered Breathing, Stroke, Acute
Conditions
Keywords
Obstructive Sleep Apnea, Central Sleep Apnea, Stroke, Sleep Position
Brief summary
Obstructive sleep apnea (OSA) is prevalent in patients with stroke and has a negative effect on outcomes by predisposing them to recurrent stroke, increasing mortality, and so forth. Therefore, it is extremely important to identify OSA in patients with stroke. Wearable devices can greatly reduce the manpower and material requirements of traditional laboratory-based polysomnography (PSG). With Photoplethysmography (PPG) technology and neural network algorithms, the Belun ring and the sleeping platform not only can detect blood oxygen, and heart rate but also can identify sleep stage and estimate the severity of sleep apnea. In this study, inpatients with acute ischemic stroke in the hospital will proceed with three nights test for recording the parameters of the autonomic nervous system in the acute phase, evaluate whether sleep apnea and the feasibility of the Belun sleep platform. It is important that early recognition of OSA and prompt treatment, which can potentially improve OSA-associated adverse outcomes, as well as understanding the degree of autonomic nervous function impairment for patients with acute ischemic stroke. After smoothing this process, it can help clinicians more accurately comprehend the condition, timing of admission, and discharge.
Interventions
DEVICEBelun Sleep platform
BSP(Belun Technology Company Limited) is a novel wearable system using a neural network algorithm that consists of a patented wearable device (Belun Ring), a charging cradle, and cloud-based software. The Belun Ring, an FDA-cleared pulse oximeter, acquires signals from the radialis indicis artery of the proximal index finger. The Belun Ring total sleep time can be derived from features extracted from accelerometer, SpO2, and PPG signals, whereas Belun Ring AHI can be derived from Ring-TST and features extracted from HRV and SpO2 changes. The BSP proprietary OSA detection algorithm was using neural network. BSP performance can be improved by training the algorithm through continual data collection. To our knowledge, BSP is the only validated sleep platform using a medical-grade wearable pulse oximeter, actigraphy, and artificial intelligence algorithm that has the capability to identify sleep stages, detect OSA events, and monitor autonomic nervous system activity changes during sleep.
Sponsors
Case Western Reserve University
Taipei Medical University Shuang Ho Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* Hospitalized patients with confirmed acute ischemic stroke * Age 18-80 * Able to consent
Exclusion criteria
* History of atrial fibrillation, LVEF \< 45%, pacemaker/defibrillator, left ventricular assist device (LVAD), or status post-cardiac transplantation, devastating strokes ( mRS \>= 4). * Aphasia, severe bulbar palsy, unable to comprehend, consent, or answer questionnaires. * Unstable cardiopulmonary status. * Recent surgery including tracheotomy in 30 days. * On narcotics. * On O2, PAP device, ventilator, diaphragmatic pacing, or any form of nerve stimulator * unable to understand instructions or to accurately use BRP during the instruction session. * Patients with technically valid recording time under 4 hours will be excluded.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AHI, duration with SpO2 < 90%, and SpO2 nadir, as well as sleep stage parameters (total sleep time [TST], wake time, REM time, and NREM time) | 1 year | To specifically assess the accuracy of BSP bAHI in predicting OSA by comparing to the concurrent in-lab PSG-AHI and to determine the accuracy of BSP sleep stage parameters by comparing to the concurrent PSG. BSP bAHI, BSP time with SpO2 \< 90%, and BSP-SpO2 nadir will be extracted from BSP and compared to PSGAHI (4% hypopnea criteria), PSG time with SpO2 \< 90% (PSG-T90), PSG-SpO2 nadir extracted from the concurrent PSG. BSP sleep stage parameters (total sleep time \[TST\], wake time, REM time, and NREM time) will be extracted from BSP and compared to the same parameters of the concurrent PSG. Epoch-by-epoch comparison will be performed. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Duration of BSP use and technically valid recording time | 1 year | Duration of BSP use and technically valid recording time will be extracted from BSP for assessment feasibility of BSP testing in hospitalized patients. |
| Score of STOP-Bang | 1 year | Stop-Bang cutoff of 3, 4, and 5 will be combined with PSG-cutoff of 5 events/h, 15 events/h, and 30 events/h and compared for accuracy of OSA prediction. |
| HRV parameters (including both frequency and time domain) ,the length of hospital stay, NIH Stroke Scale (NIHSS) score, and modified Rankin score (mRS) | 1 year | ANS parameters including HRV frequency domain parameters (low frequency \[LF\], high frequency \[HF\], and LF/HF ratio) as well as time domain parameters (standard deviation of normal to normal R-R intervals \[SDNN\] and root mean square of successive heartbeat interval difference \[RMSSD\]) will be extracted from BSP and test the correlations with the length of hospital stay, NIH Stroke Scale (NIHSS) score, and modified Rankin score (mRS) will be analyzed. |
Countries
Taiwan
Contacts
Primary ContactWen-Te Liu, MD. PhD
Outcome results
None listed