Dataset Information

Validation of novel automatic ultra-wideband radar for sleep apnea detection.

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.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC7212156 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Validation of novel automatic ultra-wideband radar for sleep apnea detection.

Zhou Yong Y Shu Degui D Xu Hangdi H Qiu Yuanhua Y Zhou Pan P Ruan Wenjing W Qin Guangyue G Jin Joy J Zhu Hao H Ying Kejing K Zhang Wenxia W Chen Enguo E

Journal of thoracic disease 20200401 4

<h4>Background</h4>To validate the accuracy of ultra-wideband (UWB) wireless radar for the screening diagnosis of sleep apnea.<h4>Methods</h4>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.<h4>Results</h4>A UWB radar-based AHI algorithm was successfully ...[more]

PMID: 32395265

Similar Datasets

Project description:Study objectivesWe investigated the diagnostic accuracy for the identification of obstructive sleep apnea (OSA) and its severity of a noninvasive technology based on image processing (SleepWise).MethodsThis is an observational, prospective study to evaluate the degree of agreement between polysomnography (PSG) and SleepWise. We recruited 56 consecutive subjects with suspected OSA who were referred as outpatients to the Sleep Unit of the Hospital Universitari Germans Trias i Pujol (HUGTiP) from January 2013 to January 2014. All patients underwent laboratory PSG and image processing with SleepWise simultaneously the same night. Both PSG and SleepWise analyses were carried independently and blindly.ResultsWe analyzed 50 of the 56 patients recruited. OSA was diagnosed through PSG in a total of 44 patients (88%) with a median apnea-hypopnea index (AHI) of 25.35 (24.9). According to SleepWise, 45 patients (90%) met the criteria for a diagnosis of OSA, with a median AHI of 22.8 (22.03). An analysis of the ability of PSG and SleepWise to classify patients by severity on the basis of their AHI shows that the two diagnostic systems distribute the different groups similarly. According to PSG, 23 patients (46%) had a diagnosis of severe OSA, 11 patients (22%) moderate OSA, and 10 patients (20%) mild OSA. According to SleepWise, 20, 13, and 12 patients (40%, 26%, and 24%, respectively) had a diagnosis of severe, moderate, and mild OSA respectively. For OSA diagnosis, SleepWise was found to have sensitivity of 100% and specificity of 83% in relation to PSG. The positive predictive value was 97% and the negative predictive value was 100%. The Bland-Altman plot comparing the mean AHI values obtained through PSG and SleepWise shows very good agreement between the two diagnostic techniques, with a bias of -3.85, a standard error of 12.18, and a confidence interval of -0.39 to -7.31.ConclusionsSleepWise was reasonably accurate for noninvasive and automatic diagnosis of OSA in outpatients. SleepWise determined the severity of OSA with high reliability. The current study including simultaneous laboratory PSG and SleepWise processing image is proposed as a reasonable validation standard.

Dataset Information

Validation of novel automatic ultra-wideband radar for sleep apnea detection.

Background

Methods

Results

Conclusions

Publications

Validation of novel automatic ultra-wideband radar for sleep apnea detection.

Similar Datasets

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