###### Abstract  
Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%. biomedical signal processing; cardiotocograph; empirical mode decomposition; fetal heart rate; spectral analysis; time-varying autoregressive modeling +
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extracting hidden FHR characteristics that can improve the CTG interpretation. However, despite these efforts, the results obtained are not satisfactory enough for their use in clinical practice so far [5; 7; 8; 9]. On the one hand, current advances in medical research indicate that a correct assessment of fetal distress requires a good understanding of the compensatory mechanism of the fetus. Besides, its health depends on how it compensates itself under certain stimuli, such as UC events, over time. These compensatory mechanisms are controlled by the autonomic nervous system (ANS), which modulates the fetal cardiac activity after a perceived oxygen insufficiency [10; 11]. Indeed, the fetal compensation response, represented by variations in the beat-to-beat FHR, can involve important time-variant dynamics related to the fetal condition [12; 13]. Given that concept, an appropriate FHR signal analysis should consider the nonlinear and non-stationary characteristics of the compensatory mechanism. Therefore, conventional signal processing methods that do not integrate these characteristics could not be suited for a correct CTG analysis. On the other hand, according to the literature [14], the CTG interpretation problems are mainly associated with the assessment of FHR decelerations, which are certainly fetal cardiac responses modulated by the ANS [6]. Likewise, recent research in biomedical engineering indicates that the UC activity has a graded effect on the FHR response [15; 16]. In this context, different signal processing approaches have been proposed, which are mainly based on frequency-domain, nonlinear feature extraction, and time-variant analysis. However, considering the physiological phenomena explained above, they have certain limitations because most of the proposed approaches focus only on fetal reactivity as a response to a UC event, without considering the time-variant characteristics involved in the modulated FHR response over time. In previous works, we have shown that using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in combination with time-varying autoregressive (TV-AR) modeling, might be useful for the analysis of those physiological characteristics [17; 18]. Firstly, CEEMDAN, as an appropriate tool for decomposing nonlinear and non-stationary signals, can help to demodulate the FHR signal dynamics modulated by the ANS (dynamics of interest).