Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization
| Autor: | Lenka Lhotska, Chrysostomos D. Stylios, Georgios Georgoulas, Jiří Spilka, Petros Karvelis, Vaclav Chudacek |
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| Rok vydání: | 2015 |
| Předmět: |
Engineering
Cardiotocography Physiology Biomedical Engineering Biophysics Machine learning computer.software_genre Field (computer science) Machine Learning Physiology (medical) medicine Representation (mathematics) Observer Variation medicine.diagnostic_test business.industry Signal Processing Computer-Assisted Heart Rate Fetal Class (biology) Latent class model Fetal heart rate ROC Curve Categorization Classification methods Artificial intelligence business computer Algorithms |
| Zdroj: | Physiological Measurement. 36:1001-1024 |
| ISSN: | 1361-6579 0967-3334 |
| DOI: | 10.1088/0967-3334/36/5/1001 |
| Popis: | The most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractions-the cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natural ordering, and representation of increased severity, for obtaining the final results. The results are promising suggesting that more effort should be put into this proposed approach. |
| Databáze: | OpenAIRE |
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