Pattern recognition in airflow recordings to assist in the sleep apnoea-hypopnoea syndrome diagnosis.
This paper aims at detecting sleep apnoea-hypopnoea syndrome (SAHS) from single-channel airflow (AF) recordings. The study involves 148 subjects. Our proposal is based on estimating the apnoea-hypopnoea index (AHI) after global analysis of AF, including the investigation of respiratory rate variabil...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 12; pp. 1367 - 1381 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Dec2013
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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=104111853&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104111853 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2013 vid: 51 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104111853 104111853 NLM24057145 2012362866 10.1007/s11517-013-1109-7 NLM24057145 104111853 ppf: 1367 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Pattern recognition in airflow recordings to assist in the sleep apnoea-hypopnoea syndrome diagnosis. aug: au: Gutiérrez-Tobal, Gonzalo C Alvarez, Daniel Marcos, J Víctor Del Campo, Félix Hornero, Roberto Álvarez, Daniel affil: Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, Paseo Belén 15, 47011, Valladolid, Spain, gonzalo.gutierrez@gib.tel.uva.es. sug: subj: Information Science Methods Polysomnography Methods Signal Processing, Computer Assisted Sleep Apnea, Obstructive Diagnosis Adult Female Human Linear Regression Male Middle Age Sleep Apnea, Obstructive Physiopathology Nonparametric Statistics Adult: 19-44 years Middle Aged: 45-64 years Female Male ab: This paper aims at detecting sleep apnoea-hypopnoea syndrome (SAHS) from single-channel airflow (AF) recordings. The study involves 148 subjects. Our proposal is based on estimating the apnoea-hypopnoea index (AHI) after global analysis of AF, including the investigation of respiratory rate variability (RRV). We exhaustively characterize both AF and RRV by extracting spectral, nonlinear, and statistical features. Then, the fast correlation-based filter is used to select those relevant and non-redundant. Multiple linear regression, multi-layer perceptron (MLP), and radial basis functions are fed with the features to estimate AHI. A conventional approach, based on scoring apnoeas and hypopnoeas, is also assessed for comparison purposes. An MLP model trained with AF and RRV selected features achieved the highest agreement with the true AHI (intra-class correlation coefficient = 0.849). It also showed the highest diagnostic ability, reaching 92.5 % sensitivity, 89.5 % specificity and 91.5 % accuracy. This suggests that AF and RRV can complement each other to estimate AHI and help in SAHS diagnosis. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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