Temat: kardiotokografia - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: The influence of cardiotocogram signal feature selection method on fetal state assessment efficacy
Autorzy:: Jeżewski, M.
Czabański, R.
Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333440.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: cardiotocography
classification
feature selection
kardiotokografia
klasyfikacja
selekcja cech
Opis:: Cardiotocographic (CTG) monitoring is a method of assessing fetal state. Since visual analysis of CTG signal is difficult, methods of automated qualitative fetal state evaluation on the basis of the quantitative description of the signal are applied. The appropriate selection of learning data influences the quality of the fetal state assessment with computational intelligence methods. In the presented work we examined three different feature selection procedures based on: principal components analysis, receiver operating characteristics and guidelines of International Federation of Gynecology and Obstetrics. To investigate their influence on the fetal state assessment quality the benchmark SisPorto® dataset and the Lagrangian support vector machine were used.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 51-58
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Influence of gestational age on neural networks interpretation of fetal monitoring signals
Autorzy:: Jeżewski, M.
Czabański, R.
Horoba, K.
Wróbel, J.
Łęski, J.
Jeżewski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333505.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: monitoring płodu
kardiotokografia
klasyfikacja
sieci neuronowe
fetal monitoring
cardiotocography
classification
neural networks (NN)
Opis:: Cardiotocographic monitoring (CTG) is a primary biophysical monitoring method for assessment of the fetal state and is based on analysis of fetal heart rate, uterine contraction activity and fetal movement signals. Visual analysis of CTG traces is very difficult so computer-aided fetal monitoring systems have become a standard in clinical centres. We proposed the application of neural networks for the prediction of fetal outcome using the parameters of quantitative description of acquired signals as inputs. We focused on the influence of the gestational age (during trace recording) on the fetal outcome classification quality. We designed MLP and RBF neural networks with changing the number of neurons in the hidden layer to find the best structure. Networks were trained and tested fifty times, with random cases assignment to training, validating and testing subset. We obtained the value of sensitivity index above 0.7, what may be regarded as good result. However additional trace grouping within similar gestational age, increased classification quality in the case of MLP networks.
Źródło:: Journal of Medical Informatics & Technologies; 2008, 12; 137-142
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 3.

Tytuł:: Unsupervised clustering for fetal state assessment based on selected features of the cardiotocographic signals
Autorzy:: Przybyła, T.
Jeżewski, J.
Roj, D.
Powiązania:: https://bibliotekanauki.pl/articles/333112.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: klasyfikacja algorytmów bez nadzoru
grupowanie danych
monitoring płodu
kardiotokografia
unsupervised classification
fuzzy clustering
principal component analysis
fetal monitoring
Opis:: In modern obstetrics the cardiotocography is a routine method of fetal condition assessment based mainly on analysis of the fetal heart rate signals. The correct interpretation of recorded traces from a bedside monitor is very difficult even for experienced clinicians. Therefore, computerized fetal monitoring systems are used to yield the quantitative description of the signal. However, the effective techniques enabling automated conclusion generation based on cardiotocograms are still being searched. The paper presents an attempt to diagnose the fetal state basing on seventeen features describing the cardiotocographic records. The proposed method applies the unsupervised classification of signals. During our research we tried to classify the fetal state using the fuzzy c-means (FCM) clustering. We also tested how the efficiency of classification could be influenced by application of principal component analysis (PCA) algorithm. The obtained results showed that unsupervised classification cannot be considered as a support to fetal state assessment.
Źródło:: Journal of Medical Informatics & Technologies; 2009, 13; 157-162
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "kardiotokografia" wg kryterium: Temat

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język