Comput Math Methods Med - Validation in principal components analysis applied to EEG data.

Tópicos

{ analysi(2126) use(1163) compon(1037) }
{ sampl(1606) size(1419) use(1276) }
{ spatial(1525) area(1432) region(1030) }
{ age(1611) year(1155) adult(843) }
{ bind(1733) structur(1185) ligand(1036) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ signal(2180) analysi(812) frequenc(800) }
{ studi(1410) differ(1259) use(1210) }
{ method(1969) cluster(1462) data(1082) }
{ method(1219) similar(1157) match(930) }
{ treatment(1704) effect(941) patient(846) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ first(2504) two(1366) second(1323) }
{ use(1733) differ(960) four(931) }
{ imag(1057) registr(996) error(939) }
{ studi(2440) review(1878) systemat(933) }
{ chang(1828) time(1643) increas(1301) }
{ extract(1171) text(1153) clinic(932) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ studi(1119) effect(1106) posit(819) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ result(1111) use(1088) new(759) }
{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
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{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
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{ framework(1458) process(801) describ(734) }
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{ care(1570) inform(1187) nurs(1089) }
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{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The well-known multivariate technique Principal Components Analysis (PCA) is usually applied to a sample, and so component scores are subjected to sampling variability. However, few studies address their stability, an important topic when the sample size is small. This work presents three validation procedures applied to PCA, based on confidence regions generated by a variant of a nonparametric bootstrap called the partial bootstrap: (i) the assessment of PC scores variability by the spread and overlapping of "confidence regions" plotted around these scores; (ii) the use of the confidence regions centroids as a validation set; and (iii) the definition of the number of nontrivial axes to be retained for analysis. The methods were applied to EEG data collected during a postural control protocol with twenty-four volunteers. Two axes were retained for analysis, with 91.6% of explained variance. Results showed that the area of the confidence regions provided useful insights on the variability of scores and suggested that some subjects were not distinguishable from others, which was not evident from the principal planes. In addition, potential outliers, initially suggested by an analysis of the first principal plane, could not be confirmed by the confidence regions.

Resumo Limpo

wellknown multivari techniqu princip compon analysi pca usual appli sampl compon score subject sampl variabl howev studi address stabil import topic sampl size small work present three valid procedur appli pca base confid region generat variant nonparametr bootstrap call partial bootstrap assess pc score variabl spread overlap confid region plot around score ii use confid region centroid valid set iii definit number nontrivi axe retain analysi method appli eeg data collect postur control protocol twentyfour volunt two axe retain analysi explain varianc result show area confid region provid use insight variabl score suggest subject distinguish other evid princip plane addit potenti outlier initi suggest analysi first princip plane confirm confid region

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