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Titel: Cardiotocographic signal feature extraction through CEEMDAN and time-varying autoregressive spectral-based analysis for fetal welfare assessment
Autor(en): Fuentealba, Patricio
Illanes, Alfredo
Ortmeier, Frank
Erscheinungsdatum: 2019
Art: Artikel
Sprache: Englisch
URN: urn:nbn:de:gbv:ma9:1-1981185920-367215
Schlagwörter: Biomedical signal processing
Cardiotocograph
Empirical mode decomposition
Fetal heart rate
Time-varying autoregressive modeling
Spectral analysis
Zusammenfassung: 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 pro-cessing 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%.
URI: https://opendata.uni-halle.de//handle/1981185920/36721
http://dx.doi.org/10.25673/36487
Open-Access: Open-Access-Publikation
Nutzungslizenz: (CC BY 4.0) Creative Commons Namensnennung 4.0 International(CC BY 4.0) Creative Commons Namensnennung 4.0 International
Sponsor/Geldgeber: DFG-Publikationsfonds 2019
Journal Titel: IEEE access
Verlag: IEEE
Verlagsort: New York, NY
Band: 7
Heft: 2019
Originalveröffentlichung: 10.1109/access.2019.2950798
Seitenanfang: 159754
Seitenende: 159772
Enthalten in den Sammlungen:Fakultät für Informatik (OA)

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