Detection of fetal distress though a support vector machine based on fetal heart rate parameters

Lunghi, F.; Magenes, Giovanni; Pedrinazzi, L.; Signorini, M.G.

doi:10.1109/cic.2005.1588083

Cited by 19 publications

(25 citation statements)

References 11 publications

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“…Variability within 1-min interval due to acceleration and decelarations Lunghi et al, 2005;Fanelli et al, 2013;Signorini et al, 2014 Interval Index (II)…”

Section: Training/validation Methodologymentioning

confidence: 99%

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Valderrama

Marzbanrad

Hall-Clifford

et al. 2020

Front. Artif. Intell.

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In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low-and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.

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“…Variability within 1-min interval due to acceleration and decelarations Lunghi et al, 2005;Fanelli et al, 2013;Signorini et al, 2014 Interval Index (II)…”

Section: Training/validation Methodologymentioning

confidence: 99%

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Valderrama

Marzbanrad

Hall-Clifford

et al. 2020

Front. Artif. Intell.

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show abstract

“…Fetal distress denotes the possibility of fetal hypoxia and/or metabolic acidosis [2]. It is usually a complication of the labor [3]. Adequate fetal oxygenation is essential for a healthy neonate [4], and efficient and effective diagnosis on fetal distress is an important issue.…”

Section: Introductionmentioning

confidence: 99%

Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network

Huang¹,

Hsu²

2012

JBiSE

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Fetal distress is one of the main factors to cesarean section in obstetrics and gynecology. If the fetus lack of oxygen in uterus, threat to the fetal health and fetal death could happen. Cardiotocography (CTG) is the most widely used technique to monitor the fetal health and fetal heart rate (FHR) is an important index to identify occurs of fetal distress. This study is to propose discriminant analysis (DA), decision tree (DT), and artificial neural network (ANN) to evaluate fetal distress. The results show that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78%, respectively

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“…Chronic state means abnormalities in fetal nutrition uptake leading to improper neural development and restricted fetal growth also referred as Intrauterine Growth Restriction (IUGR). Acute state such as Asphyxia of fetus can further lead to severe hypoxic ischemic organ damage in fetus followed by severe life-long pathologies [1]. IUGR occurs when fetus is deficient of necessary nutrition's or because of infections.…”

Section: Introductionmentioning

confidence: 99%

N on - Invasive Feto - Maternal Well - Being Monitoring: A Review of Methods

Queyam¹,

Pahuja²,

Singh³

2017

JESTR

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Monitoring of fetal and maternal well-being is extremely critical during pregnancy and during labor to reduce the occurrence of fetal and maternal distress in high risk pregnancies. There are many approaches to identify and quantify the real-time feto-maternal well-being by measuring physiological parameters viz. fetal movement, fetal temperature, fetal respiration rate, fetal kick count, fetal heart rate, maternal ECG acquired from both chest and abdomen region, uterine contraction, blood SpO2 concentration, amniotic fluid pH etc. In this paper, different techniques to acquire and measure these physiological parameters non-invasively are evaluated and compared for the purpose of monitoring fetomaternal well-being.

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Detection of fetal distress though a support vector machine based on fetal heart rate parameters

Abstract: Abstract

Cited by 19 publications

References 11 publications

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network

N on - Invasive Feto - Maternal Well - Being Monitoring: A Review of Methods

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