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Cardiotocograph Data Classification Improvement by Using Empirical Mode Decomposition.

Patricio Fuentealba, Alfredo Illanes, Frank Ortmeier

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
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    This study improves fetal heart rate (FHR) analysis by incorporating novel features derived from the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique. Incorporating CEEMDAN features enhanced classification accuracy in FHR signals.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Cardiology

    Background:

    • Fetal heart rate (FHR) signals reflect autonomic nervous system modulation.
    • Accurate FHR analysis is crucial for fetal well-being assessment.
    • Conventional time-domain features have limitations in capturing complex FHR dynamics.

    Purpose of the Study:

    • To investigate the utility of CEEMDAN-based features for FHR signal analysis.
    • To compare the classification performance of conventional features versus CEEMDAN-enhanced features.
    • To enhance the accuracy of FHR signal classification using advanced signal processing techniques.

    Main Methods:

    • Fetal heart rate (FHR) signals were analyzed using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique.
    • A combination of CEEMDAN-derived features and conventional time-domain features were extracted.
    • Support vector machine (SVM) was employed as the classification algorithm.

    Main Results:

    • Classification performance improved from 67.6% using only conventional features to 71.7% when CEEMDAN-based features were included.
    • The inclusion of CEEMDAN features demonstrated a statistically significant improvement in classification accuracy.
    • The study validated the hypothesis that CEEMDAN enhances FHR signal classification.

    Conclusions:

    • CEEMDAN-based features significantly improve the classification performance of FHR signals.
    • This approach offers a promising method for more accurate fetal monitoring.
    • The findings suggest broader applications of CEEMDAN in analyzing complex biological signals.