Comput Methods Programs Biomed - Characterisation of linear predictability and non-stationarity of subcutaneous glucose profiles.

Tópicos

{ chang(1828) time(1643) increas(1301) }
{ measur(2081) correl(1212) valu(896) }
{ patient(2315) diseas(1263) diabet(1191) }
{ blood(1257) pressur(1144) flow(957) }
{ patient(2837) hospit(1953) medic(668) }
{ intervent(3218) particip(2042) group(1664) }
{ model(2656) set(1616) predict(1553) }
{ signal(2180) analysi(812) frequenc(800) }
{ analysi(2126) use(1163) compon(1037) }
{ network(2748) neural(1063) input(814) }
{ gene(2352) biolog(1181) express(1162) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ system(1976) rule(880) can(841) }
{ method(1219) similar(1157) match(930) }
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{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
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{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ patient(1821) servic(1111) care(1106) }
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{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Continuous glucose monitoring is increasingly used in the management of diabetes. Subcutaneous glucose profiles are characterised by a strong non-stationarity, which limits the application of correlation-spectral analysis. We derived an index of linear predictability by calculating the autocorrelation function of time series increments and applied detrended fluctuation analysis to assess the non-stationarity of the profiles. Time series from volunteers with both type 1 and type 2 diabetes and from control subjects were analysed. The results suggest that in control subjects, blood glucose variation is relatively uncorrelated, and this variation could be modelled as a random walk with no retention of 'memory' of previous values. In diabetes, variation is both greater and smoother, with retention of inter-dependence between neighbouring values. Essential components for adequate longer term prediction were identified via a decomposition of time series into a slow trend and responses to external stimuli. Implications for diabetes management are discussed.

Resumo Limpo

continu glucos monitor increas use manag diabet subcutan glucos profil characteris strong nonstationar limit applic correlationspectr analysi deriv index linear predict calcul autocorrel function time seri increment appli detrend fluctuat analysi assess nonstationar profil time seri volunt type type diabet control subject analys result suggest control subject blood glucos variat relat uncorrel variat model random walk retent memori previous valu diabet variat greater smoother retent interdepend neighbour valu essenti compon adequ longer term predict identifi via decomposit time seri slow trend respons extern stimuli implic diabet manag discuss

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