Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment
Cardiotocograph (CTG) is a widely used tool for fetal surveillance during labor, which provides the joint recording of fetal heart rate (FHR) and uterine contraction data. Unfortunately, the CTG interpretation is difficult because it involves a visual analysis of highly complex signals. Recent clini...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8888167/ |
| _version_ | 1818910144159809536 |
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| author | Patricio Fuentealba Alfredo Illanes Frank Ortmeier |
| author_facet | Patricio Fuentealba Alfredo Illanes Frank Ortmeier |
| author_sort | Patricio Fuentealba |
| collection | DOAJ |
| description | Cardiotocograph (CTG) is a widely used tool for fetal surveillance during labor, which provides the joint recording of fetal heart rate (FHR) and uterine contraction data. Unfortunately, the CTG interpretation is difficult because it involves a visual analysis of highly complex signals. Recent clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms modulated by the autonomic nervous system. Certainly, this modulation reflects variations in the FHR, whose characteristics can involve significant information about the fetal condition. The main contribution of this work is to investigate these characteristics by a new approach combining two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modeling. The idea is to study the CEEMDAN intrinsic mode functions (IMFs) in both the time-domain and the spectral-domain in order to extract information that can help to assess the fetal condition. For this purpose, first, the FHR signal is decomposed, and then for each IMF, the TV-AR spectrum is computed in order to study their spectral dynamics over time. In this paper, we first explain the foundations of our proposed features. Then, we evaluate their performance in CTG classification by using three machine learning classifiers. The proposed approach has been evaluated on real CTG data extracted from the CTU-UHB database. Results show that by using only conventional FHR features, the classification performance achieved 78, 0%. Then, by including the proposed CEEMDAN spectral-based features, it increased to 81, 7%. |
| first_indexed | 2024-12-19T22:38:08Z |
| format | Article |
| id | doaj.art-d585cb09f320471ab119edbf8b7357fb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T22:38:08Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d585cb09f320471ab119edbf8b7357fb2022-12-21T20:03:08ZengIEEEIEEE Access2169-35362019-01-01715975415977210.1109/ACCESS.2019.29507988888167Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare AssessmentPatricio Fuentealba0https://orcid.org/0000-0002-7119-0580Alfredo Illanes1Frank Ortmeier2Faculty of Computer Science, Institute for Intelligent Cooperating Systems, Otto-von-Guericke University Magdeburg, Magdeburg, GermanyFaculty of Electrical Engineering and Information Technology, Institute of Medical Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, GermanyFaculty of Computer Science, Institute for Intelligent Cooperating Systems, Otto-von-Guericke University Magdeburg, Magdeburg, GermanyCardiotocograph (CTG) is a widely used tool for fetal surveillance during labor, which provides the joint recording of fetal heart rate (FHR) and uterine contraction data. Unfortunately, the CTG interpretation is difficult because it involves a visual analysis of highly complex signals. Recent clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms modulated by the autonomic nervous system. Certainly, this modulation reflects variations in the FHR, whose characteristics can involve significant information about the fetal condition. The main contribution of this work is to investigate these characteristics by a new approach combining two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modeling. The idea is to study the CEEMDAN intrinsic mode functions (IMFs) in both the time-domain and the spectral-domain in order to extract information that can help to assess the fetal condition. For this purpose, first, the FHR signal is decomposed, and then for each IMF, the TV-AR spectrum is computed in order to study their spectral dynamics over time. In this paper, we first explain the foundations of our proposed features. Then, we evaluate their performance in CTG classification by using three machine learning classifiers. The proposed approach has been evaluated on real CTG data extracted from the CTU-UHB database. Results show that by using only conventional FHR features, the classification performance achieved 78, 0%. Then, by including the proposed CEEMDAN spectral-based features, it increased to 81, 7%.https://ieeexplore.ieee.org/document/8888167/Biomedical signal processingcardiotocographempirical mode decompositionfetal heart ratespectral analysistime-varying autoregressive modeling |
| spellingShingle | Patricio Fuentealba Alfredo Illanes Frank Ortmeier Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment IEEE Access Biomedical signal processing cardiotocograph empirical mode decomposition fetal heart rate spectral analysis time-varying autoregressive modeling |
| title | Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment |
| title_full | Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment |
| title_fullStr | Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment |
| title_full_unstemmed | Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment |
| title_short | Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment |
| title_sort | cardiotocographic signal feature extraction through ceemdan and time varying autoregressive spectral based analysis for fetal welfare assessment |
| topic | Biomedical signal processing cardiotocograph empirical mode decomposition fetal heart rate spectral analysis time-varying autoregressive modeling |
| url | https://ieeexplore.ieee.org/document/8888167/ |
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