Analysis of the foetal heart rate in cardiotocographic recordings through a progressive characterization of decelerations

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%.

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“…for the floating-line computation but with a different sliding window. Following [65], the sliding window size was set to 400 s length. 2.…”

Section: A Virtual Baseline (Vbl) Is Estimated By Filtering the Fhr Smentioning

confidence: 99%

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

2019

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“…This first category usually consists of statistical indicators computed from short and long term analysis in order to characterize the standard morphological CTG patterns described on guidelines [17]- [22], or statistical models to reproduce the clinician's interpretation of such patterns [23].…”

Section: ) Time-domain Featuresmentioning

confidence: 99%

“…For this purpose, first, a virtual BL (VBL) is extracted by filtering the FHR signal using a nonlinear median filter [59]. Based on [22], this filter is computed over a sliding window of 400s length. Then, this VBL is used to define the low (L) and high (H ) traces (see Fig.…”

Section: ) Progressive Fhr Baseline Estimationmentioning

confidence: 99%

Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment

2019

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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.

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“…The first step for decelerations identification is performed following the method proposed in [11], which allows the detection of deceleration episodes through the use of a floating-line. The detected episodes of more than 15 bpm in amplitude and more than 15 seconds length [2] (evident decelerations) are then parameterized according to their location, length and depth.…”

Section: Identification Of Decelerationsmentioning

confidence: 99%

Progressive Fetal Distress Estimation by Characterization of Fetal Heart Rate Decelerations Response Based on Signal Variability in Cardiotocographic Recordings.

Fuentealba

Illanes

Ortmeier

2017

Computing in Cardiology Conference (CinC)

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In the current clinical practice, Cardiotocograph (CTG) is a standard and widely used tool for fetal surveillance. However, CTG interpretation is difficult since it involves human diagnosis of different patterns in highly complex signals. Fetal heart rate (FHR) decelerations and variability are known to be the most significant and difficult patterns to assess by the clinical staff. The main goal of this work is to analyze the fetal distress by tracking the evolution of the dynamical changes occurring in the CTG recording. The idea is to consider the direct input/output relationship between uterine contraction (UC) and FHR signals by the characterization of FHR decelerations in terms of their signal variability as a sign of the fetal response corresponding to a UC event. Results show that the progression of the decelerations response over time can help the observer to track fetal distress.

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Analysis of the foetal heart rate in cardiotocographic recordings through a progressive characterization of decelerations

Cited by 8 publications

References 8 publications

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment

Progressive Fetal Distress Estimation by Characterization of Fetal Heart Rate Decelerations Response Based on Signal Variability in Cardiotocographic Recordings.

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