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Medical & Biological Engineering & Computing

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26.12.2017 | Original Article

Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition

verfasst von: Ya-Chen Chen, Tzu-Chien Hsiao

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 7/2018

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Abstract

Respiratory inductance plethysmography (RIP) sensor is an inexpensive, non-invasive, easy-to-use transducer for collecting respiratory movement data. Studies have reported that the RIP signal’s amplitude and frequency can be used to discriminate respiratory diseases. However, with the conventional approach of RIP data analysis, respiratory muscle effort cannot be estimated. In this paper, the estimation of the respiratory muscle effort through RIP signal was proposed. A complementary ensemble empirical mode decomposition method was used, to extract hidden signals from the RIP signals based on the frequency bands of the activities of different respiratory muscles. To validate the proposed method, an experiment to collect subjects’ RIP signal under thoracic breathing (TB) and abdominal breathing (AB) was conducted. The experimental results for both the TB and AB indicate that the proposed method can be used to loosely estimate the activities of thoracic muscles, abdominal muscles, and diaphragm.

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Titel: Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition
verfasst von: Ya-Chen Chen
Tzu-Chien Hsiao
Publikationsdatum: 26.12.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 7/2018
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-017-1766-z

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