Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest.
Biocybern Biomed Eng
; 40(1): 352-362, 2020.
Article
en En
| MEDLINE
| ID: mdl-32308250
ACC, accuracy; ADASYN, adaptive synthetic sampling approach; ANN, artificial neural network; AR, auto-regressive model; AUC, the area under the curve; CorrDim, correlation dimension; DT, decision tree; EHG, electrohysterogram; Electrohysterogram (EHG); Feature extraction; Gestational week; IUPC, intrauterine pressure catheter; K-NN, K-nearest; LDA, linear discriminant analysis; LE, Lyapunov exponent; MDF, median frequency; MNF, mean frequency; PE, preterm delivery before the 26th week of gestation; PF, peak frequency; PL, preterm delivery after the 26th week of gestation; Preterm delivery; QDA, quadratic discriminant analysis; RF, random forest; RMS, root mean square; ROC, the receiver operating characteristic curve; Random forest (RF).; SD, standard deviation; SE, energy values in signal; SM, maximum values in signal; SS, singular values in signal; SV, variance values in signal; SVM, support vector machine; SampEn, sample entropy; TE, term delivery before the 26th week of gestation; TL, term delivery after the 26th week of gestation; TOCO, tocodynamometer; TPEHG, term-preterm electrohysterogram; Tr, time reversibility; τz, zero-crossing
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
Idioma:
En
Revista:
Biocybern Biomed Eng
Año:
2020
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Polonia