Application of multiscale entropy in arterial waveform contour analysis in healthy and diabetic subjects.
We applied multiscale entropy (MSE) to assess variation in crest time (CT), a parameter in arterial waveform analysis, in diagnosing patients with diabetes. Data on digital volume pulse were obtained from 93 individuals in three groups [Healthy young (Group 1, 20 < age ≤ 40, n = 30), healthy upper-m...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 53; no. 1; pp. 89 - 99 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jan2015
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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=109773472&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109773472 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2015 vid: 53 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109773472 NLM25351478 2012869320 10.1007/s11517-014-1220-4 NLM25351478 109773472 ppf: 89 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Application of multiscale entropy in arterial waveform contour analysis in healthy and diabetic subjects. aug: au: Liu, An-Bang Wu, Hsien-Tsai Liu, Chun-Wei Liu, Cyuan-Cin Tang, Chieh-Ju Tsai, I-Ting Sun, Cheuk-Kwan sug: subj: Arteries Physiopathology Diabetes Mellitus Physiopathology Physics Health Signal Processing, Computer Assisted Adult Case Control Studies Human Metabolic Syndrome X Physiopathology Middle Age Time Factors Young Adult Adult: 19-44 years Middle Aged: 45-64 years ab: We applied multiscale entropy (MSE) to assess variation in crest time (CT), a parameter in arterial waveform analysis, in diagnosing patients with diabetes. Data on digital volume pulse were obtained from 93 individuals in three groups [Healthy young (Group 1, 20 < age ≤ 40, n = 30), healthy upper-middle-aged (Group 2, age > 40, n = 30), and diabetic (Group 3, n = 33) subjects]. Crest time, normalized crest time, crest time ratio (CTR), small- and large-scale MSE on CT [MSESS(CT) and MSELS(CT), respectively] were computed and correlated with anthropometric (i.e., body weight/height, waist circumference), hemodynamic (i.e., blood pressure), and biochemical parameters (i.e., serum triglyceride, high-density lipoprotein, fasting blood sugar, and glycosylated hemoglobin). The results demonstrated higher variability in CT in healthy subjects (Groups 1 and 2) compared with that in diabetic patients (Group 3) as reflected in significantly elevated MSESS(CT) and MSELS(CT) in the former (p < 0.003 and p < 0.001, respectively). MSELS(CT) also showed significant association with waist circumference and fasting blood sugar (i.e., two diagnostic criteria of metabolic syndrome) as well as glycosylated hemoglobin concentration. In conclusion, using MSE analysis for assessing CT variation successfully distinguished diabetic patients from healthy subjects. MSESS(CT) and MSELS(CT) therefore may serve as noninvasive tools for identifying subjects with diabetes and those at risk. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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