Detection rate of fetal distress using contraction-dependent fetal heart rate variability analysis

Warmerdam, G.J.J.; Vullings, Rik; Laar, Judith O E H van; Jagt, M.B. van der Hout-van der; Bergmans, J.W.M.; Schmitt, L.; Oei, S.G.

doi:10.1088/1361-6579/aaa925

Cited by 28 publications

(29 citation statements)

References 42 publications

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“…Then, by including features extracted from resting periods, the classification performance increased to 83.2%. These results coincide with recent, similar research, which has shown that considering features extracted from the entire FHR segment and contraction-dependent features, the classification performance improved to 79% [19].…”

Section: Discussionsupporting

confidence: 92%

“…The results coincide with a recent approach [19], which employs another database, but similar class formation criteria and evaluation. That research shows that, considering features extracted from the entire FHR segment in combination with contraction-dependent features, might improve the detection of fetal distress, whose classification performance, based on the geometric mean, improved from 70% to 79%.…”

Section: Features Evaluation and Discussionsupporting

confidence: 84%

“…Based on this feature extraction methodology, this work focuses on studying the CTG classification performance by considering the complete FHR signal, deceleration episodes, and resting periods (between contractions) separately. Considering that recent research has shown that a separate analysis of these FHR signal segments can improve the detection of fetal distress [19], we propose to extract information from those segments independently and determine their contribution in CTG analysis evaluated by a support vector machine (SVM) based classification. The main hypothesis is that the feature extraction approach based on CEEMDAN and TV-AR modeling, applied separately to the complete signal, deceleration episodes, and resting periods, improves the CTG classification performance compared with the traditional analysis based only on the complete signal.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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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Section: Discussionsupporting

confidence: 92%

Section: Features Evaluation and Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…We found the computerized identification of FHR characteristics for the afECG technique was in most instances more faithful to results from the scalp electrode than US-based FHR detection in the identification of baseline heart rate, and heart rate variability, ac- pattern interpretation, [28][29][30] This is the first study to address whether afECG monitoring produces FHR patterns that would be interpreted in the same manner as those produced by US-based cardiotachometry. Of concern in designing this study was the fact that there is considerable inter-and intra-observer variation in the visual interpretation of FHR patterns.…”

Section: Discussionmentioning

confidence: 81%

“…Current recommendations for the clinical assessment of variability in the interpretation of FHR patterns require making fine distinctions in the degree of variability observed. Because such distinctions have a potentially substantial influence on decision‐making, and because they are a component of all extant systems for FHR pattern interpretation, it is critically important that the technique used to assess rate and variability provide the most accurate available information. It has been assumed since the introduction of autocorrelation techniques to cardiotachometer software that US‐derived FHR patterns can be assumed to display true FHR variability.…”

Section: Discussionmentioning

confidence: 99%

Relative accuracy of computerized intrapartum fetal heart rate pattern recognition by ultrasound and abdominal electrocardiogram detection

Hayes‐Gill

Martin²,

Liu

et al. 2019

Acta Obstet Gynecol Scand

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Introduction Noninvasive fetal heart rate monitoring using transabdominal fetal electrocardiographic detection is now commercially available and has been demonstrated to be an effective alternative to traditional Doppler ultrasonographic techniques. Our objective in this study was to compare the results of computerized identification of fetal heart rate patterns generated by ultrasound‐based and transabdominal fetal electrocardiogram‐based techniques with simultaneously obtained fetal scalp electrode‐derived heart rate information. Material and methods We applied an objective computer‐based analysis for recognition of fetal heart rate patterns (Monica Decision Support) to data obtained simultaneously from a direct fetal scalp electrode, Doppler ultrasound, and the abdominal‐fetal electrocardiogram techniques. This allowed us to compare over 145 hours of fetal heart rate patterns generated by the external devices with those derived from the scalp electrode in 30 term singleton uncomplicated pregnancies during labor. The direct fetal scalp electrode is considered to be the most accurate and reliable technique used in current clinical practice, and was, therefore, used as the standard for comparison. The program quantified the baseline heart rate, long‐ and short‐term variability. It indicated when an acceleration or deceleration was present and whether it was large or small. Results Ultrasound was associated with significantly greater deviations from the fetal scalp electrode results than the abdominal fetal electrocardiogram technique in recognizing the correct baseline heart rate, its variability, and the presence of small and large accelerations and small decelerations. For large decelerations the two external methods were each not significantly different from the scalp electrode results. Conclusions Noninvasive fetal heart rate monitoring using maternal abdominal wall electrodes to detect fetal cardiac activity more reliably reproduced the computerized analysis of heart rate patterns derived from a direct fetal scalp electrode than did traditional ultrasound‐based monitoring. Abdominal‐fetal electrocardiogram should, therefore, be considered a primary option for externally monitored patients.

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Improvement of accuracy and resilience in FHR classification via double trend accumulation encoding and attention mechanism

Zhou

Zhao

Zhang

et al. 2023

Biomedical Signal Processing and Control

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Detection rate of fetal distress using contraction-dependent fetal heart rate variability analysis

Abstract: Our results show that combining contraction-dependent HRV features with HRV features calculated over the entire fetal heart rate signal improves the detection rate of fetal distress.

Cited by 28 publications

References 42 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

Relative accuracy of computerized intrapartum fetal heart rate pattern recognition by ultrasound and abdominal electrocardiogram detection

Improvement of accuracy and resilience in FHR classification via double trend accumulation encoding and attention mechanism

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