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

The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.

Lin SY ，Tsai CY ，Majumdar A ，Ho YH ，Huang YW ，Kao CK ，Yeh SM ，Hsu WH ，Kuan YC ，Lee KY ，Feng PH ，Tseng CH ，Chen KY ，Kang JH ，Lee HC ，Wu CJ ，Liu WT ... -  《-》

Polysomnography in patients with obstructive sleep apnea: an evidence-based analysis.

Medical Advisory Secretariat 《-》

[Diagnosis of obstructive sleep apnea by a new radar device: a parallel controlled study evaluating agreement with polysomnographic monitoring].

Li CY ，Wang W ，Huang WJ ，Xu HH ，Yi HL ，Guan J ，Li G ，Yin SK ... -  《-》

Continuous positive airway pressure device-based automated detection of obstructive sleep apnea compared to standard laboratory polysomnography.

Prasad B ，Carley DW ，Herdegen JJ 《-》

Overnight polysomnography versus respiratory polygraphy in the diagnosis of pediatric obstructive sleep apnea.

Tan HL ，Gozal D ，Ramirez HM ，Bandla HP ，Kheirandish-Gozal L ... -  《-》