Novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction
Preterm births are one of the key causes of death in children under the age of five: they have financial implications associated with care and cause great psychological distress for the families involved. In this work, we applied a novel signal decomposition approach termed Linear Series Decompositi...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305322000618 |
| _version_ | 1828377410152693760 |
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| author | Ejay Nsugbe |
| author_facet | Ejay Nsugbe |
| author_sort | Ejay Nsugbe |
| collection | DOAJ |
| description | Preterm births are one of the key causes of death in children under the age of five: they have financial implications associated with care and cause great psychological distress for the families involved. In this work, we applied a novel signal decomposition approach termed Linear Series Decomposition Learner (LSDL) from electrohysterogram (EHG) and tocodynamometer (Toco) signals to the prediction of a preterm delivery, alongside an associated delivery imminency timeline, using the logistic regression and support vector machine classifiers. The results from the classification exercise showed an equivalent performance for the EHG and Toco signals for the preterm prediction, while in the case of the imminency prediction, the Toco signals provided better results in predicting the delivery imminency period. These results have made apparent that a LSDL decomposed Toco/mechanical signal carries useful information which can be used to predict delivery imminency and supersedes that of an electrophysiological/EHG signal. |
| first_indexed | 2024-04-14T08:13:14Z |
| format | Article |
| id | doaj.art-cbca98eb48b448879506a02cc40786fd |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-04-14T08:13:14Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-cbca98eb48b448879506a02cc40786fd2022-12-22T02:04:30ZengElsevierIntelligent Systems with Applications2667-30532022-11-0116200123Novel uterine contraction signals decomposition for enhanced preterm and birth imminency predictionEjay Nsugbe0Nsugbe Research Labs, Swindon SN1 3LG, United KingdomPreterm births are one of the key causes of death in children under the age of five: they have financial implications associated with care and cause great psychological distress for the families involved. In this work, we applied a novel signal decomposition approach termed Linear Series Decomposition Learner (LSDL) from electrohysterogram (EHG) and tocodynamometer (Toco) signals to the prediction of a preterm delivery, alongside an associated delivery imminency timeline, using the logistic regression and support vector machine classifiers. The results from the classification exercise showed an equivalent performance for the EHG and Toco signals for the preterm prediction, while in the case of the imminency prediction, the Toco signals provided better results in predicting the delivery imminency period. These results have made apparent that a LSDL decomposed Toco/mechanical signal carries useful information which can be used to predict delivery imminency and supersedes that of an electrophysiological/EHG signal.http://www.sciencedirect.com/science/article/pii/S2667305322000618Preterm predictionMachine learningSignal processingDecision supportIntelligent systemsCybernetics |
| spellingShingle | Ejay Nsugbe Novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction Intelligent Systems with Applications Preterm prediction Machine learning Signal processing Decision support Intelligent systems Cybernetics |
| title | Novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction |
| title_full | Novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction |
| title_fullStr | Novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction |
| title_full_unstemmed | Novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction |
| title_short | Novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction |
| title_sort | novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction |
| topic | Preterm prediction Machine learning Signal processing Decision support Intelligent systems Cybernetics |
| url | http://www.sciencedirect.com/science/article/pii/S2667305322000618 |
| work_keys_str_mv | AT ejaynsugbe noveluterinecontractionsignalsdecompositionforenhancedpretermandbirthimminencyprediction |