Specific change in spectral power of fetal heart rate variability related to fetal acidemia during labor: Comparison between preterm and term fetuses

Kwon, Ji Young; Park, In Yang; Shin, Jong Chul; Song, Juhee; Tafreshi, Reza; Lim, Jongil

doi:10.1016/j.earlhumdev.2011.08.007

Cited by 23 publications

(31 citation statements)

References 25 publications

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“…With computerized analysis of tracings now available, we can rigorously test hypotheses about EFM patterns, such as the optimal defi nition of reduced variability. Furthermore, it is also possible to separate the different frequency components of heart rate variability corresponding to the clinical concepts of short-and long-term variability [22,38,50,64,69] . Considerable debate remains regarding the best method to measure the various components of variability and what components or ratio of components are the most discriminating of metabolic acidemia in general [22,50,52,64,68] .…”

Section: Three Basic Steps In Clinical Monitoringmentioning

confidence: 99%

New perspectives in electronic fetal surveillance

Hamilton¹,

Warrick²

2012

Journal of Perinatal Medicine

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Despite its recognized limitations, fetal heart rate monitoring is a mainstay of intrapartum care. Although the basic technology in standard electronic fetal monitors has changed little in recent decades, clinical behavior in response to heart rate monitoring has changed considerably. In addition to clearly defi ned nomenclature and clinical guidelines, there is an increased awareness that environmental and human factors can impair clinical judgment, resulting in delayed intervention and, consequently, birth-related injury. This review examines three essential steps that affect clinical outcome: (1) signal acquisition, (2) associations with physiological outcome, and (3) clinical intervention. Only the third step is directly responsible for changing clinical outcome. However, timely initiation of interventions is dependent upon the second step, which is dependent upon the fi rst step. Thus, defi ciencies at each step tend to accumulate and contribute to the worsening of overall clinical outcome. This review article summarizes advances occurring at each step. The synergy and convergence of innovations in engineering, mathematics, and behavioral science shows considerable promise in intrapartum fetal surveillance.

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Section: Three Basic Steps In Clinical Monitoringmentioning

confidence: 99%

New perspectives in electronic fetal surveillance

Hamilton¹,

Warrick²

2012

Journal of Perinatal Medicine

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“…According to several studies different frequency contributions can be identified in FHR [29,32] The frequency-based features are usually computed by using FFT-based techniques over the energy of the different spectral components described above [33,34,35,36,37,32]. As in adults HRV, it is assumed that differences in spectral bands can be correlated with foetal acidosis (through the umbilical cord pH) [38].…”

Section: A Time Invariant Spectral Based Featuresmentioning

confidence: 99%

“…Several epochs' lengths have been proposed depending on the type of the analysis (i.e. time invariant, time variant, short term or long term dynamic analysis [29,30,31,25,26,32,27]). The main principle is that spectral estimation depends on data length.…”

Section: B Stationary Segments Extractionmentioning

confidence: 99%

“…In [27], segments of 20 minutes are chosen as close as possible to delivery, because during the last minutes major changes in foetal condition can occur. In [32] epochs of 256 heart beats by the use of three sections of 128 beats, each offset by 64 points are taken. In [30,31], each one of the epochs of the time variant analysis is of 32 seconds length, and in [29], epochs of 3 minutes are selected for time variant analysis.…”

Section: B Stationary Segments Extractionmentioning

confidence: 99%

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Survey on Cardiotocography Feature Extraction Algorithms for Foetal Welfare Assessment

Haritopoulos

Illanes

Nandi

2016

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016

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Abstract-Since its inception forty years ago as a way to control birth process, the cardiotocograph (CTG) has emerged over time and became the undisputed leader worldwide of noninvasive intrapartum foetal monitoring systems. The CTG signals conveying a lot of information, it is very difficult to interpret them and act accordingly even for specialists; hence, researchers have started looking for characteristics which could be correlated with a particular pathological state of the foetus. Thereby, many features appeared in the literature, ranging from the most common ones to artificially generated features, and computed using a wide variety of signal processing-based analysis tools: time scale, spectral or non-linear analysis, to name but a few. This survey paper, presents in a hierarchical order the most common processing steps of a CTG signal and focuses primarily on the feature extraction methods for foetal heart rate (FHR) analysis reported in the literature during the last decade. Also, some feature classification methods are reported before a brief discussion which concludes this work.

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“…The frequency-based features are usually computed from operations performed over the energy of different spectral components computed with FFT-based techniques [5], [6]. Spectral analysis allows estimating the effect of the interaction between sympathetic and parasympathetic systems on the alterations of the heart rate variation.…”

Section: Introductionmentioning

confidence: 99%

Fetal heart rate feature extraction from cardiotocographic recordings through autoregressive model's power spectral- and pole-based analysis

Illanes¹,

Haritopoulos²

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-The main objective of this work is to perform an autoregressive model (AR)-based power spectral analysis of the fetal heart rate (FHR) signal for the extraction of significant features for fetal welfare assessment. A group of features is directly computed from the AR-based spectrum while another group is computed from the poles representation. The presented method is applied to real cardiotocographic (CTG) signals and for different frequency bands, and the obtained results are very promising as they exhibit direct correlations between the extracted features and the fetal welfare in terms of umbilical pH.

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Specific change in spectral power of fetal heart rate variability related to fetal acidemia during labor: Comparison between preterm and term fetuses

Cited by 23 publications

References 25 publications

New perspectives in electronic fetal surveillance

New perspectives in electronic fetal surveillance

Survey on Cardiotocography Feature Extraction Algorithms for Foetal Welfare Assessment

Fetal heart rate feature extraction from cardiotocographic recordings through autoregressive model's power spectral- and pole-based analysis

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