Classification and Comparison of Cardiotocography Signals with
Cardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this...
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ftinonuuniv:oai:abakus.inonu.edu.tr:11616/26393 2023-05-15T18:12:23+02:00 Classification and Comparison of Cardiotocography Signals with Artificial Neural Network and Extreme Learning Machine Comert, Z Kocamaz, AF Gungor, S 2016 http://hdl.handle.net/11616/26393 unknown http://hdl.handle.net/11616/26393 2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU) 2016 ftinonuuniv 2022-03-28T19:48:09Z Cardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine Learning Repository. The prominent features, such as baseline, the number of acceleration and deceleration patterns, and variability recommended by International Federation of Gynecology and Obstetrics (FIGO) have also taken into account during CTG analysis. The features were applied as the input to feedforward neural network (ANN) and Extreme Learning Machine (ELM) to classify FHR patterns in this study. FHR is recently divided into three classes as normal, suspicious and pathological. According to the results of this study, the accuracy of classification of ANN and ELM were obtained as 91.84% and 93.42%, respectively. C1 [Comert, Zafer] Bitlis Eren Univ, Dept Comp Engn, Bitlis, Turkey. [Kocamaz, Adnan Fatih] Inonu Univ, Dept Comp Engn, Malatya, Turkey. [Gungor, Sami] Med Pk Hosp, Obstet & Gynecol Clin, Elazig, Turkey. Other/Unknown Material sami Unknown |
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Cardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine Learning Repository. The prominent features, such as baseline, the number of acceleration and deceleration patterns, and variability recommended by International Federation of Gynecology and Obstetrics (FIGO) have also taken into account during CTG analysis. The features were applied as the input to feedforward neural network (ANN) and Extreme Learning Machine (ELM) to classify FHR patterns in this study. FHR is recently divided into three classes as normal, suspicious and pathological. According to the results of this study, the accuracy of classification of ANN and ELM were obtained as 91.84% and 93.42%, respectively. C1 [Comert, Zafer] Bitlis Eren Univ, Dept Comp Engn, Bitlis, Turkey. [Kocamaz, Adnan Fatih] Inonu Univ, Dept Comp Engn, Malatya, Turkey. [Gungor, Sami] Med Pk Hosp, Obstet & Gynecol Clin, Elazig, Turkey. |
author |
Comert, Z Kocamaz, AF Gungor, S |
spellingShingle |
Comert, Z Kocamaz, AF Gungor, S Classification and Comparison of Cardiotocography Signals with |
author_facet |
Comert, Z Kocamaz, AF Gungor, S |
author_sort |
Comert, Z |
title |
Classification and Comparison of Cardiotocography Signals with |
title_short |
Classification and Comparison of Cardiotocography Signals with |
title_full |
Classification and Comparison of Cardiotocography Signals with |
title_fullStr |
Classification and Comparison of Cardiotocography Signals with |
title_full_unstemmed |
Classification and Comparison of Cardiotocography Signals with |
title_sort |
classification and comparison of cardiotocography signals with |
publishDate |
2016 |
url |
http://hdl.handle.net/11616/26393 |
genre |
sami |
genre_facet |
sami |
op_source |
2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU) |
op_relation |
http://hdl.handle.net/11616/26393 |
_version_ |
1766184927937691648 |