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In-home, smart sleep monitoring system for cardiorespiratory estimation and sleep apnea detection: proof of concept

  • Apnea is a sleep disorder characterized by breathing interruptions during sleep, impacting cardiorespiratory function and overall health. Traditional diagnostic methods, like polysomnography (PSG), are unobtrusive, leading to noninvasive monitoring. This study aims to develop and validate a novel sleep monitoring system using noninvasive sensor technology to estimate cardiorespiratory parameters and detect sleep apnea. We designed a seamless monitoring system integrating noncontact force-sensitive resistor sensors to collect ballistocardiogram signals associated with cardiorespiratory activity. We enhanced the sensor’s sensitivity and reduced the noise by designing a new concept of edge-measuring sensor using a hemisphere dome and mechanical hanger to distribute the force and mechanically amplify the micromovement caused by cardiac and respiration activities. In total, we deployed three edgemeasuring sensors, two deployed under the thoracic and one under the abdominal regions. The system is supported with onboard signal preprocessing in multiple physical layers deployed under the mattress. We collected the data in four sleeping positions from 16 subjects and analyzed them using ensemble empirical mode decomposition (EMD) to avoid frequency mixing. We also developed an adaptive thresholding method to identify sleep apnea. The error was reduced to 3.98 and 1.43 beats/min (BPM) in heart rate (HR) and respiration estimation, respectively. The apnea was detected with an accuracy of 87%. We optimized the system such that only one edge-measuring sensor can measure the cardiorespiratory parameters. Such a reduction in the complexity and simplification of the instruction of use shows excellent potential for in-home and continuous monitoring.

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Metadaten
Author of HS ReutlingenMartínez Madrid, Natividad
DOI:https://doi.org/10.1109/jsen.2024.3370819
ISSN:1530-437X
eISSN:1558-1748
Erschienen in:IEEE sensors journal
Publisher:IEEE
Place of publication:New York
Document Type:Journal article
Language:English
Publication year:2024
Tag:apnea detection; cardiorespiratory estimation; in-home continuous measurement; noninvasive measurement; sleep monitoring
Volume:24
Issue:8
Page Number:14
First Page:13364
Last Page:13377
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt