Validation of Novel Automatic Ultra-wideband Radar for Sleep Apnea Detection

Overview

Journal J Thorac Dis

Publisher AME Publishing Company

Specialty Pulmonary Medicine

Date 2020 May 13

PMID 32395265

Citations 9

Authors

Yong Zhou

Degui Shu

Hangdi Xu

Yuanhua Qiu

Pan Zhou

Wenjing Ruan

Guangyue Qin

Joy Jin

Hao Zhu

Kejing Ying

Wenxia Zhang

Enguo Chen

Affiliations

Soon will be listed here.

Abstract

Background: To validate the accuracy of ultra-wideband (UWB) wireless radar for the screening diagnosis of sleep apnea.

Methods: One hundred and seventy-six qualified participants were successfully recruited. Apnea-hypopnea index (AHI) results from polysomnography (PSG) were reviewed by physicians, while the radar device automatically calculated AHI values with an embedded chip. All results were statistically analyzed.

Results: A UWB radar-based AHI algorithm was successfully developed according to respiratory movement and body motion signals. Of all 176 participants, 63 exhibited normal results (AHI <5/hr) and the remaining 113 were diagnosed with obstructive sleep apnea. Significant correlation was detected between radar AHI and PSG AHI (Intraclass correlation coefficient 0.98, P<0.001). Receiver operating characteristic curve (ROC) analysis revealed high sensitivity and specificity. High concordance in participants with varying gender, age, BMI, and PSG AHI was reached.

Conclusions: The UWB radar may be a portable, convenient, and reliable device for obstructive sleep apnea screening.

Citing Articles

Noncontact Longitudinal Respiratory Rate Measurements in Healthy Adults Using Radar-Based Sleep Monitor (Somnofy): Validation Study.

Toften S, Kjellstadli J, Thu O, Ellingsen O JMIR Biomed Eng. 2024; 7(2):e36618.

PMID: 38875674 PMC: 11041471. DOI: 10.2196/36618.

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

Lin S, Tsai C, Majumdar A, Ho Y, Huang Y, Kao C J Clin Sleep Med. 2024; 20(8):1267-1277.

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Mattress-Based Non-Influencing Sleep Apnea Monitoring System.

Qi P, Gong S, Jiang N, Dai Y, Yang J, Jiang L Sensors (Basel). 2023; 23(7).

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Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study.

Choi J, Kim D, Koo D, Park Y, Nam H, Lee J Sensors (Basel). 2022; 22(19).

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Sleep Position Detection with a Wireless Audio-Motion Sensor-A Validation Study.

Kukwa W, Lis T, Laba J, Mitchell R, Mlynczak M Diagnostics (Basel). 2022; 12(5).

PMID: 35626350 PMC: 9139663. DOI: 10.3390/diagnostics12051195.

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