Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition.
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 a...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 7; pp. 1293 - 1304 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130320749&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130320749 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2018 vid: 56 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 130320749 130320749 NLM29280093 130320749 10.1007/s11517-017-1766-z NLM29280093 130320749 ppf: 1293 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition. aug: au: Chen, Ya-Chen Hsiao, Tzu-Chien affil: Institute of Computer Science and Engineering, National Chiao Tung University, Hsinchu, Taiwan, Republic of China sug: subj: Respiration Respiratory Muscles Physiology Algorithms Plethysmography Signal Processing, Computer Assisted Young Adult Male Female Human Male Female ab: 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. Graphical abstract ᅟ. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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