Validation of a Handy Sleep Monitoring Device: UMindSleep in Patients With Obstructive Sleep Apnea

The Chinese University of Hong Kong

Status: Unknown

Conditions: Validation

Treatments: Device: UMindSleep

Study type: Interventional

Funder types: Other

Identifiers

NCT04208672

CREC2018.501

Details and patient eligibility

About

This is a validation study recruiting subjects with and without obstructive sleep apnea. All subjects will undergo a nocturnal standard polysomnography and UMindSleep assessment. Sleep parameters, such as sleep stages and apnea UMindSleep software. Correlation in each parameter between PSG and events in polysomnography (PSG) will be scored according to the AASM criteria while the sleep parameters will be automatically scored by the UMindSleep will be analyzed to determine the magnitude of agreement between UMindSleep and PSG.

Full description

Obstructive sleep apnea (OSA) is a common sleep disorder in the general population with a prevalence ranging from 4-30%. OSA has been shown to be a significant risk factor of many cardiovascular diseases and mental disorders. However, OSA is a neglected problem in the general population. In addition, there is a significant unmet need for the treatment of OSA. One of the major reason for the under-diagnosis and under-treatment of OSA in the general population is a lack of reliable screening tool in detecting OSA. In this regard, several devices, such as ApneaLink, have been developed for the screening of OSA. However, as this kind of device only employs time in bed rather than actual sleep time to calculate the apnea index or desaturation index, they tend to underestimate the severity of sleep apnea. In this regard, it is timely need to develop and validate new device that can integrate the actual sleep time and apnea or desaturation events for the precise calculation of sleep apnea index or desaturation index.

The UMindSleep is a handy and wearable device to acquire EEG, heart rate, saturation, snoring and temperature. The signal will be uploaded to a smartphone through Bluetooth. By integrating these signals, the system can automatically analyze several key parameters by the algorithms, such as sleep stages and desaturation events.

Enrollment: 100 estimated patients

Sex: All

Ages: 18 to 65 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• complain of habitual snoring;
• AHI as measured by standard PSG > 5/hour

Exclusion criteria

• Aged 17 years old or below
• patients with narcolepsy and REM sleep behavior disorder

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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100 participants in 1 patient group

Patient attending for PSG in Sleep Assessment Unit

Experimental group

Description:

Subjects referred to the SAU will be invited to participate into this study

Treatment:

Device: UMindSleep