Screening of OSA in Hospitalized Stroke Patients Using BSP

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.

Conditions

• Sleep-Disordered Breathing
• Sleep Architecture
• Stroke, Acute

Interventions

DEVICE

Belun 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 & Collaborators

• Case Western Reserve University

  collaborator OTHER

• Taipei Medical University Shuang Ho Hospital

  lead OTHER

Principal Investigators

• Wen-Te Liu, MD. PhD · Taipei Medical University Shuang Ho Hospital

Study Design

Allocation

NA

Purpose

DIAGNOSTIC

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Max Age

80 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2022-05-04

Primary Completion

2023-05-03

Completion

2024-05-03

Countries

• Taiwan

Study Locations