The Accuracy and Uncertainty of a Sheet-type Portable Monitor as a Screening Device to Identify Obstructive Sleep Apnea-hypopnea Syndrome

Abstract

Objective Laboratory-based polysomnography (PSG) is the gold standard for diagnosing obstructive sleep apnea-hypopnea syndrome (OSAHS), but it is expensive and requires overnight hospitalization. Recently, a sheet-shaped breath detection monitor, the SD-101, has been developed, and several reports have so far demonstrated the screening accuracy of this device. The aim of this study was to assess the accuracy and the uncertainty of this device. Methods A total of 101 suspected OSAHS patients underwent simultaneous examinations with PSG and the SD-101. Results There was a statistically significant relationship between the respiratory disturbance index (RDI) by the SD-101 and the apnea-hypopnea index (AHI) by PSG. At an RDI cutoff of 14 episodes per hour, the sensitivity and specificity to detect an AHI ! 20 episodes per hour were 90.2% and 90.0%, respectively. To reduce the influence of sleep efficiency, the time in bed (TIB) obtained from PSG, instead of the total seep time (TST), was used to calculate the AHI from the PSG data. There was also a statistically significant relationship between the RDI and AHI for the TIB. Moreover, it was suggested that arousal index and TIB were likely associated with false-negative and/or false-positive results.Conclusion Although the present study demonstrated a close relationship between the RDI and the AHI, use of the SD-101 to examine symptomatic OSAHS patients should be performed with a full understanding of its incapability to detect the sleep state, including arousal reaction and the existence of false respiratory events caused by body movements.

Validation of a sheet-shaped body vibrometer for screening of obstructive sleep apnea

“…Therefore, we compared the screening accuracy of REI_eTST with that of REI_TIB according to the OSA severity cut-off levels. In particular, the sensitivity and specificity of non-wear PM devices for predicting severe OSA (AHI ≥ 30 events/h) have not been reported previously (13)(14)(15)(16)(17). However, in the present study, both REI_eTST and REI_TIB measured with the SBV showed relatively high sensitivity and specificity at optimal cutoff values for predicting OSA with all three criteria (AHI ≥ 30, AHI ≥ 15, AHI ≥ 5), Moreover, data loss in PM recording using wearable sensors such as oronasal and respiratory effort sensors is considered an important problem (11).…”

“…These issues can be resolved by PM recording without the need for attaching sensors, i.e., non-wear PM devices. Previous studies on the validity of OSA screening with non-wear PM devices, such as a static charge sensitive bed (SCSB) (13,14) and a sheet-shaped device placed on a mattress (15)(16)(17), have been conducted. However, due to the insufficient number of validation studies, these devices have been classified as type 4 PM for the screening of OSA, and have hence not been generally accepted (9,11).…”

Validation of a novel sleep-monitoring system for diagnosing obstructive sleep apnea: A comparison with polysomnography

“…In the study by Agatsuma et al (14), ROC curve analysis of aa respiratory disturbance index (measured by SD-101, Kentzmedico Co. Ltd, Saitama, Japan) of 14 events/h revealed 89.5% sensitivity and 85.8% specificity for identifying OSA. Tsukahara et al (15) calculated AHI using the time in bed, and at a cut-off of 14 events/h, the sensitivity and specificity of detecting an AHI of ≥20 events/h were 90.2 and 90.0%, respectively. Kobayashi et al (16) used the SD-101 with percutaneous oxygen saturation detecting an AHI of >15 events/h on PSG with a sensitivity (specificity) of 96.9% (90.5 %) compared with 87.5% (85.7 %), respectively.…”

Digital Health and Sleep-Disordered Breathing: A Systematic Review and Meta-Analysis

“…Three assessed the SD-101 sensor (Tsukahara et al, Agatsuma et al, and Takasaki et al). [23][24][25] All six studies, including Beattie et al, evaluated devices that used, at a minimum, several pressure sensors that measured respiratory and body movement to estimate AHI. 26 Norman et al evaluated a device that also measured acoustic features, 27 and Tenhunem et al additionally measured heart rate.…”

“…Of the six studies, one (Tsukahara et al 23 ) was not included in the quantitative analysis because we were not able to obtain the true positive, true negative, false positive, and false negative values. A forest plot of the five remaining studies is shown in Figure 2.…”