This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2019.2950798, IEEE Access Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2017.DOI Cardiotocographic Signal Feature Extraction through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment PATRICIO FUENTEALBA 1,3 , (Student Member, IEEE), ALFREDO ILLANES 2 , AND FRANK ORTMEIER 1 1 Faculty of Computer Science, Otto-von-Guericke University, 39106 Magdeburg, Germany 2 Chair of Intelligent Catheter, Otto-von-Guericke University, 39104 Magdeburg, Germany 3 Electrical and Electronics Department, Universidad Austral de Chile, 5111187 Valdivia, Chile Corresponding author: P. Fuentealba (e-mail: patricio.fuentealba@ovgu.de). This work was supported in part by the National Commission for Scientific and Technological Research CONICYT, Chilean National Scholarship Program for Graduate Studies. ABSTRACT 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%. INDEX TERMS Biomedical signal processing, cardiotocograph, empirical mode decomposition, fetal heart rate, spectral analysis, time-varying autoregressive modeling. I. INTRODUCTION T HE main aim of fetal surveillance during labor is to timely identify potential acidotic fetuses without un- necessary interventions. During this process, a fetus can repeatedly suffer from decreased oxygen insufficiency, which is a natural phenomenon, but fetuses with weakened defense mechanisms could develop metabolic acidosis. As a conse- quence, it can lead to neuro-development disability, cerebral palsy, or in some cases, even death [1]. For this reason, fetal monitoring during labor is essential, which is commonly per- formed by using a cardiotocograph (CTG), which provides the joint recording of fetal heart rate (FHR) and uterine contraction (UC) signals. The CTG assessment is currently performed by a visual analysis of several morphological FHR VOLUME 4, 2016 1