Data-Driven Multimodal Sleep Apnea Events Detection.
A novel multimodal and bio-inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data-driven biomedical signal processing and classification. Th...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 7; pp. 1 - 8 |
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| Autor principal: | |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2016
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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=115925381&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925381 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2016 vid: 40 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925381 115925381 115925381 10.1007/s10916-016-0520-7 115925381 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Data-Driven Multimodal Sleep Apnea Events Detection. aug: au: Rutkowski, Tomasz affil: Life Science Center of TARA University of Tsukuba, Tennodai 1-1-1 Tsukuba-shi Japan 305-8577 sug: subj: Sleep Apnea Syndromes Diagnosis Signal Processing, Computer Assisted Electroencephalography Methods Human Brain-Computer Interfaces Brain Waves Japan Polysomnography Sleep Apnea, Obstructive Diagnosis Sleep Apnea, Central Diagnosis Adult Female Male Middle Age Sleep Apnea Syndromes Classification Algorithms Descriptive Statistics Nonparametric Statistics Wilcoxon Rank Sum Test Discriminant Analysis Adult: 19-44 years Middle Aged: 45-64 years Female Male ab: A novel multimodal and bio-inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data-driven biomedical signal processing and classification. The presented signal processing and classification methods have been already successfully applied to real-time unimodal brainwaves (EEG only) decoding in brain-computer interfaces developed by the author. In the current project the very encouraging results are obtained using multimodal biomedical (brainwaves and peripheral physiological) signals in a unified processing approach allowing for the automatic semantic data description. The results thus support a hypothesis of the data-driven and bio-inspired signal processing approach validity for medical data semantic interpretation based on the sleep apnea events machine-learning-related classification. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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