All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome.
Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 4; pp. 373 - 382 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2012
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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=104545223&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104545223 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2012 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104545223 NLM22407477 2011504888 10.1007/s11517-012-0885-9 NLM22407477 PMC3314810 104545223 ppf: 373 ppct: 9 formats: fmt: @attributes: type: P tig: atl: All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome. aug: au: Mesquita J Solà-Soler J Fiz JA Morera J Jané R Mesquita, J Solà-Soler, J Fiz, J A Morera, J Jané, R affil: Department ESAII, Universitat Politècnica de Catalunya, Barcelona, Spain sug: subj: Signal Processing, Computer Assisted Sleep Apnea Syndromes Complications Sleep Apnea Syndromes Diagnosis Snoring Etiology Adult Aged Clinical Assessment Tools Female Human Male Middle Age Polysomnography Methods Severity of Illness Indices Sound Spectrography Methods Time Factors Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h(-1)) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h(-1)) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h(-1)) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h(-1), respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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