Assessing fetal health

Jones, Nia; Bugg, George; Gribbin, Clare; Raine‐Fenning, Nick

doi:10.1016/j.ogrm.2008.04.001

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…At the present time, targeted ultrasound scans and fetal echocardiograms are not applicable to all pregnancies due to various economical reasons and the shortage of trained manpower. In these prevailing conditions, fetal arrhythmias detected on routine auscultation, although rare, are oftentimes that which prompt further investigation 17,18 . With this in mind, the development of FECG for clinical use may serve as an additional but indispensable step in prenatal low‐risk screening and in overall diagnostic algorithms for CHD.…”

Section: Discussionmentioning

confidence: 99%

Understanding congenital heart defects through abdominal fetal electrocardiography: Case reports and clinical implications

Velayo

Sato

Ito

et al. 2011

J of Obstet and Gynaecol

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Section: Discussionmentioning

confidence: 99%

Understanding congenital heart defects through abdominal fetal electrocardiography: Case reports and clinical implications

Velayo

Sato

Ito

et al. 2011

J of Obstet and Gynaecol

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“…However, most pregnant women are unaware of the health status of the fetus in their womb between pregnancy and delivery. Some fetuses are at risk of developing complications that may result in future disease, handicap, or death [ 4 ]. For those at advanced maternal age and risk, FM detection can identify those complications that potentially alter the outcome of labor and help the practitioner to make timely interventions to avoid the development of stillbirth [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

Liang

Peng

2021

Journal of Healthcare Engineering

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Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.

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“…La evaluación del riesgo por sí sola, parece ser cada vez menos útil en la predicción de complicaciones en la gestación pues, aun sin importar los riesgos calculados, 90 a 95% de los embarazos dan como resultado un recién nacido vivo y sano, lo cual contrasta con un gran número de óbitos fetales que ocurren en embarazos identificados como de bajo riesgo [3]. A pesar de haber disminuido dramáticamente sus tasas de presentación en los últimos 40 años, todavía se presenta gran dificultad en la evaluación y en la identificación de factores de riesgo [3,4], como obesidad, ser el primer parto, edad materna avanzada en la primera gestación y las gestaciones múltiples.…”

Section: Introductionunclassified

Muerte fetal anteparto: ¿es una condición prevenible?

Molina

Alfonso

2009

Univ. Med.

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El síndrome de muerte fetal se define como la muerte del feto que se presenta después de la semana 20 de gestación, en ausencia de trabajo de parto instaurado. Constituye un tercio de toda la mortalidad fetal e infantil y más de 50% de todas las muertes perinatales en los países desarrollados. Las causas de muerte fetal raramente se reportan y la mayoría se desconoce. Se pretende con este artículo hacer una revisión que permita entender la fisiopatología de la entidad, evaluar los factores de riesgo y tratar de brindar a nuestras pacientes la posibilidad de un embarazo exitoso. Se realizó una revisión de la literatura existente de acuerdo con las bases de datos PubMed, EBSCO, Ovid y Proquest, desde el año 2000 hasta 2008. A pesar de la disminución significativa de los óbitos fetales en las últimas décadas, sigue siendo un serio problema de salud pública, no sólo por ser una condición prevalente, sino por el subregistro y la ausencia de reportes confiables.

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Assessing fetal health

Cited by 5 publications

References 9 publications

Understanding congenital heart defects through abdominal fetal electrocardiography: Case reports and clinical implications

Understanding congenital heart defects through abdominal fetal electrocardiography: Case reports and clinical implications

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

Muerte fetal anteparto: ¿es una condición prevenible?

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