Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses
The presented collection of data comprises of a set of 12 linear and nonlinear indices computed at different time scales and extracted from Fetal Heart Rate (FHR) traces acquired through Hewlett Packard CTG fetal monitors (series 1351A), connected to a PC. The sampling frequency of the recorded FHR...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-04-01
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| Series: | Data in Brief |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340920300585 |
| _version_ | 1828319249171480576 |
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| author | Maria G. Signorini Nicolò Pini Alberto Malovini Riccardo Bellazzi Giovanni Magenes |
| author_facet | Maria G. Signorini Nicolò Pini Alberto Malovini Riccardo Bellazzi Giovanni Magenes |
| author_sort | Maria G. Signorini |
| collection | DOAJ |
| description | The presented collection of data comprises of a set of 12 linear and nonlinear indices computed at different time scales and extracted from Fetal Heart Rate (FHR) traces acquired through Hewlett Packard CTG fetal monitors (series 1351A), connected to a PC. The sampling frequency of the recorded FHR signal is equal 2 Hz. The recorded populations consist of two groups of fetuses: 60 healthy and 60 Intra Uterine Growth Restricted (IUGR) fetuses. IUGR condition is a fetal condition defined as the abnormal rate of fetal growth. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. The pathology is a documented cause of fetal and neonatal morbidity and mortality. The described database was employed in a set of machine learning approaches for the early detection of the IUGR condition: “Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring” [1]. The added value of the proposed indices is their interpretability and close connection to physiological and pathological aspect of FHR regulation. Additional information on data acquisition, feature extraction and potential relevance in clinical practice are discussed in [1]. Keywords: Fetal heart rate monitoring, Cardiotocography, Intra uterine growth restricted, Physiology-based features, Multivariate analysis, Predictive analytics |
| first_indexed | 2024-04-13T17:53:45Z |
| format | Article |
| id | doaj.art-de7276d35b364407969f98b2cf12661a |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-13T17:53:45Z |
| publishDate | 2020-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-de7276d35b364407969f98b2cf12661a2022-12-22T02:36:35ZengElsevierData in Brief2352-34092020-04-0129Dataset on linear and non-linear indices for discriminating healthy and IUGR fetusesMaria G. Signorini0Nicolò Pini1Alberto Malovini2Riccardo Bellazzi3Giovanni Magenes4Department of Electronics, Information and Bioengineering (DEIB), Politecnico Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; Corresponding author.Department of Electronics, Information and Bioengineering (DEIB), Politecnico Milano, Piazza Leonardo da Vinci 32, 20133 Milano, ItalyIRCCS Fondazione S. Maugeri, Via Maugeri 10, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, ItalyThe presented collection of data comprises of a set of 12 linear and nonlinear indices computed at different time scales and extracted from Fetal Heart Rate (FHR) traces acquired through Hewlett Packard CTG fetal monitors (series 1351A), connected to a PC. The sampling frequency of the recorded FHR signal is equal 2 Hz. The recorded populations consist of two groups of fetuses: 60 healthy and 60 Intra Uterine Growth Restricted (IUGR) fetuses. IUGR condition is a fetal condition defined as the abnormal rate of fetal growth. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. The pathology is a documented cause of fetal and neonatal morbidity and mortality. The described database was employed in a set of machine learning approaches for the early detection of the IUGR condition: “Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring” [1]. The added value of the proposed indices is their interpretability and close connection to physiological and pathological aspect of FHR regulation. Additional information on data acquisition, feature extraction and potential relevance in clinical practice are discussed in [1]. Keywords: Fetal heart rate monitoring, Cardiotocography, Intra uterine growth restricted, Physiology-based features, Multivariate analysis, Predictive analyticshttp://www.sciencedirect.com/science/article/pii/S2352340920300585 |
| spellingShingle | Maria G. Signorini Nicolò Pini Alberto Malovini Riccardo Bellazzi Giovanni Magenes Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses Data in Brief |
| title | Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses |
| title_full | Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses |
| title_fullStr | Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses |
| title_full_unstemmed | Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses |
| title_short | Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses |
| title_sort | dataset on linear and non linear indices for discriminating healthy and iugr fetuses |
| url | http://www.sciencedirect.com/science/article/pii/S2352340920300585 |
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