Prediction of Preterm Delivery from Unbalanced EHG Database
Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The p...
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| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/4/1507 |
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| author | Somayeh Mohammadi Far Matin Beiramvand Mohammad Shahbakhti Piotr Augustyniak |
| author_facet | Somayeh Mohammadi Far Matin Beiramvand Mohammad Shahbakhti Piotr Augustyniak |
| author_sort | Somayeh Mohammadi Far |
| collection | DOAJ |
| description | Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager–Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms. |
| first_indexed | 2024-03-09T21:06:35Z |
| format | Article |
| id | doaj.art-d959f126ce314904a43aae40fe01aae7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:06:35Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d959f126ce314904a43aae40fe01aae72023-11-23T22:00:42ZengMDPI AGSensors1424-82202022-02-01224150710.3390/s22041507Prediction of Preterm Delivery from Unbalanced EHG DatabaseSomayeh Mohammadi Far0Matin Beiramvand1Mohammad Shahbakhti2Piotr Augustyniak3AGH University of Science and Technology, 30059 Krakow, PolandDepartment of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful 313, IranBiomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, LithuaniaAGH University of Science and Technology, 30059 Krakow, PolandObjective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager–Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.https://www.mdpi.com/1424-8220/22/4/1507preterm laborpredictionelectrohysterogramempirical mode decompositionsupport vector machine |
| spellingShingle | Somayeh Mohammadi Far Matin Beiramvand Mohammad Shahbakhti Piotr Augustyniak Prediction of Preterm Delivery from Unbalanced EHG Database Sensors preterm labor prediction electrohysterogram empirical mode decomposition support vector machine |
| title | Prediction of Preterm Delivery from Unbalanced EHG Database |
| title_full | Prediction of Preterm Delivery from Unbalanced EHG Database |
| title_fullStr | Prediction of Preterm Delivery from Unbalanced EHG Database |
| title_full_unstemmed | Prediction of Preterm Delivery from Unbalanced EHG Database |
| title_short | Prediction of Preterm Delivery from Unbalanced EHG Database |
| title_sort | prediction of preterm delivery from unbalanced ehg database |
| topic | preterm labor prediction electrohysterogram empirical mode decomposition support vector machine |
| url | https://www.mdpi.com/1424-8220/22/4/1507 |
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