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 sl...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
15 April 2024
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| In: |
IEEE sensors journal
Year: 2024, Jahrgang: 24, Heft: 8, Pages: 13364-13377 |
| ISSN: | 1558-1748 |
| DOI: | 10.1109/JSEN.2024.3370819 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/JSEN.2024.3370819 Verlag, lizenzpflichtig, Volltext: https://ieeexplore.ieee.org/document/10458913 
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| Verfasserangaben: | Mostafa Haghi, Natividad Martínez Madrid, Ralf Seepold |
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| 520 | |a 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 edge-measuring 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. | ||
| 650 | 4 | |a Apnea detection | |
| 650 | 4 | |a Biomedical monitoring | |
| 650 | 4 | |a cardiorespiratory estimation | |
| 650 | 4 | |a Heart rate | |
| 650 | 4 | |a in-home continuous measurement | |
| 650 | 4 | |a Mechanical sensors | |
| 650 | 4 | |a Monitoring | |
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| 650 | 4 | |a Sensor systems | |
| 650 | 4 | |a Sensors | |
| 650 | 4 | |a Sleep apnea | |
| 650 | 4 | |a sleep monitoring | |
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| 700 | 1 | |a Seepold, Ralf E. D. |e VerfasserIn |0 (DE-588)173207847 |0 (DE-627)698128117 |0 (DE-576)134057902 |4 aut | |
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