Piezoelectric Rubber Sheet Sensor: a Promising Tool for Home Sleep Apnea Testing

Overview

Journal Sleep Breath

Publisher Springer

Date 2024 Feb 15

PMID 38358413

Authors

Junichiro Hayano

Hiroaki Yamamoto

Haruhito Tanaka

Emi Yuda

Affiliations

Soon will be listed here.

Abstract

Purpose: This study aimed to develop an unobtrusive method for home sleep apnea testing (HSAT) utilizing micromotion signals obtained by a piezoelectric rubber sheet sensor.

Methods: Algorithms were designated to extract respiratory and ballistocardiogram components from micromotion signals and to detect respiratory events as the characteristic separation of the fast envelope of the respiration component from the slow envelope. In 78 adults with diagnosed or suspected sleep apnea, micromotion signal was recorded with a piezoelectric rubber sheet sensor placed beneath the bedsheet during polysomnography. In a half of the subjects, the algorithms were optimized to calculate respiratory event index (REI), estimating apnea-hypopnea index (AHI). In the other half of subjects, the performance of REI in classifying sleep apnea severity was evaluated. Additionally, the predictive value of the frequency of cyclic variation in heart rate (Fcv) obtained from the ballistocardiogram was assessed.

Results: In the training group, the optimized REI showed a strong correlation with the AHI (r = 0.93). Using the optimal cutoff of REI ≥ 14/h, subjects with an AHI ≥ 15 were identified with 77.8% sensitivity and 90.5% specificity. When applying this REI to the test group, it correlated closely with the AHI (r = 0.92) and identified subjects with an AHI ≥ 15 with 87.5% sensitivity and 91.3% specificity. While Fcv showed a modest correlation with AHI (r = 0.46 and 0.66 in the training and test groups), it lacked independent predictive power for AHI.

Conclusion: The analysis of respiratory component of micromotion using piezoelectric rubber sheet sensors presents a promising approach for HSAT, providing a practical and effective means of estimating sleep apnea severity.

Citing Articles

Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach.

Hayano J, Adachi M, Murakami Y, Sasaki F, Yuda E Sleep Breath. 2025; 29(1):91.

PMID: 39891814 PMC: 11787281. DOI: 10.1007/s11325-025-03255-w.

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