Sisporto 2.0: A program for automated analysis of cardiotocograms

Ayres‐de‐Campos, Diogo; Bernardes, João; Garrido, António; Marques-de-sá, Joaquim; Pereira-Leite, L.

doi:10.1002/1520-6661(200009/10)9:5<311::aid-mfm12>3.0.co;2-9

Cited by 195 publications

(52 citation statements)

References 11 publications

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“…Namely, with this study we intended to assess whether linear and nonlinear HR analysis was able to discriminate acute anoxia from baseline in rats and if this was consistent with human foetal and adult studies. The ultimate goal is a better understanding of the meaning of these methods, regarding their application in clinical research and practice, namely in the field of computerized FHR analysis (Bernardes et al 1991, Ayres-de-Campos et al 2000.…”

Section: Introductionmentioning

confidence: 99%

Linear and nonlinear heart-rate analysis in a rat model of acute anoxia

et al. 2008

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The objective of this study was the assessment of heart-rate (HR) dynamics with linear and nonlinear methods during episodes of mechanical ventilation and acute anoxia in rats. Namely, to assess whether linear and nonlinear HR analysis was able to discriminate acute anoxia from baseline in rats and if this was consistent with human foetal and adult studies. Five HR segments of 1 min duration, during baseline recording, mechanical ventilation and first, second and third minutes of induced acute anoxia, were analysed in ten adult Wistar rats. Linear time and frequency domain and nonlinear methods were used, namely mean HR (mHR), long-term irregularity (LTI), interval index (II), low frequency (LF) and high frequency (HF), approximate entropy (ApEn) and sample entropy (SampEn). New parameters for the entropy indices are proposed for the analysis of rats' HR. Bootstrap percentile confidence intervals and nonparametric statistical tests were used in the evaluation of the differences between segments. During mechanical ventilation a clear spectral band was detectable at the ventilation rate, but mHR, II and the 'new' entropy indices were the only significantly changed indices. In the transition from baseline-mechanical-ventilation to mechanical-ventilationinduced anoxia, a statistically significant decrease of mHR, II and entropy indices was observed, clearly discriminating these two instances, whereas most linear indices increased. With continued anoxia, most linear indices decreased significantly, whereas entropy remained stably low. These results are consistent with other foetal human and non-human studies and evidence that the rat model may be used for further research on linear and nonlinear analysis of heart-rate dynamics. The transition from baseline to acute anoxia was encompassed by signs of increased activation of the autonomic nervous system sympathetic branch, and decreased or blunted activity of the HR complexity regulatory centres.

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Section: Introductionmentioning

confidence: 99%

Linear and nonlinear heart-rate analysis in a rat model of acute anoxia

et al. 2008

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“…The system closely follows the International Federation of Gynaecology and Obstetrics (FIGO) guidelines for fetal monitoring [13–16] and has been extensively validated both in the ante [16, 17] and intrapartum periods [15, 17–19]. Analysis is carried out without signal reduction or averaging (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…1). The baseline is estimated using a complex algorithm based on histogram and STV analysis, aimed at finding the HR level of stable segments with normal variability, within the physiological limits of 110–150 bpm [14–16]. STV is determined as the difference between two adjacent HR beats, and considered abnormal when lower than one beat per minute.…”

Section: Methodsmentioning

confidence: 99%

“…Accelerations and decelerations are detected as HR deviations, above or below baselines, with at least 15 bpm of amplitude and 15 s duration. LTV is estimated in HR segments that do not display accelerations or decelerations, as the difference between the highest and lowest values, in a sliding window of 1 min, and is classified as abnormal when <5 bpm [13–16]. All of these basic FHR features are then used by the system to elicit an overall tracing classification, as green or blue (normal tracing), yellow (suspicious tracing) or red (pathologic tracing)) [13, 14].…”

Section: Methodsmentioning

confidence: 99%

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Improvements in fetal heart rate analysis by the removal of maternal-fetal heart rate ambiguities

Pinto

Costa-Santos

Gonçalves

et al. 2015

BMC Pregnancy Childbirth

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BackgroundMisinterpretation of the maternal heart rate (MHR) as fetal may lead to significant errors in fetal heart rate (FHR) interpretation. In this study we hypothesized that the removal of these MHR-FHR ambiguities would improve FHR analysis during the final hour of labor.MethodsSixty-one MHR and FHR recordings were simultaneously acquired in the final hour of labor. Removal of MHR-FHR ambiguities was performed by subtracting MHR signals from their FHR counterparts when the absolute difference between the two was less or equal to 5 beats per minute. Major MHR-FHR ambiguities were defined when they exceeded 1 % of the tracing. Maternal, fetal and neonatal characteristics were evaluated in cases where major MHR-FHR ambiguities occurred and computer analysis of FHR recordings was compared, before and after removal of the ambiguities.ResultsSeventy-two percent of tracings (44/61) exhibited episodes of major MHR-FHR ambiguities, which were not significantly associated with any maternal, fetal or neonatal characteristics, but were associated with MHR accelerations, FHR signal loss and decelerations. Removal of MHR-FHR ambiguities resulted in a significant decrease in FHR decelerations, and improvement in FHR tracing classification.ConclusionsFHR interpretation during the final hour of labor can be significantly improved by the removal of MHR-FHR ambiguities.

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“…Computerized CTG analysis systems were developed [14-16] adhering to the guidelines issued by the International Federation of Obstetrics and Gynecology (FIGO) [17]. A non-linear back propagation artificial neural network (ANN) was introduced for the interpretation of NST records [18] and around the same time a software program to predict fetal acidosis at birth was also developed [19].…”

Section: Introductionmentioning

confidence: 99%

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

et al. 2011

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BackgroundCardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'.MethodsThe FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples.ResultsFor the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684.ConclusionsBased on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals.

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Sisporto 2.0: A program for automated analysis of cardiotocograms

Cited by 195 publications

References 11 publications

Linear and nonlinear heart-rate analysis in a rat model of acute anoxia

Linear and nonlinear heart-rate analysis in a rat model of acute anoxia

Improvements in fetal heart rate analysis by the removal of maternal-fetal heart rate ambiguities

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

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