A portable prototype for diagnosing fetal arrhythmia

Corona-Figueroa, Abril

doi:10.1016/j.imu.2019.100268

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Studies apply AI methods to ECG [ 78–84 ], magnetocardiography (MCG) [ 85 , 86 ] and cardiotocogram (CTG) [ 87–90 ]. In attempt to improve fetal signal extraction from ECG and MCG recordings, several supervised learning methodologies were proposed: adaptive neuro-fuzzy inference (ANFIS) [ 79 , 84 ] and time–frequency analysis [ 81 ]. Unsupervised learning techniques were applied: independent component analysis [ 78 , 83 , 85 , 86 ], principle component analysis [ 46 , 91 ] and Kalman filtering [ 82 ].…”

Section: Discussionmentioning

confidence: 99%

“…A portable prototype expert system detected modeled fetal arrhythmias with 88% accuracy and could be applied to scenarios where there is an insufficient number of experts and a large number of patients [ 84 ]. The prototype uses ANFIS to extract fECG from thoracic and abdominal signals, extracts the FHR and then detects fetal arrhythmia through an expert system based on production rules.…”

Section: Discussionmentioning

confidence: 99%

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Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Davidson

Boland

2021

Briefings in Bioinformatics

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Objective Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes. Materials and methods We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes. Results We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9). Conclusions Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Davidson

Boland

2021

Briefings in Bioinformatics

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“…Signals and the accuracy of detection were evaluated by a specialist (physician). The Prototype for Diagnosing Fetal Arrhythmia was designed by Corona-Figueroa et al [ 75 ]. In addition to determining mHR and fHR, the device can also detect fetal arrhythmias (bradycardia, sinus bradycardia, tachycardia, sustained supraventricular tachycardia, atrial flutter, accelerations, decelerations and prolonged decelerations).…”

Section: Past and Present Of Fetal Monitoringmentioning

confidence: 99%

Pregnancy in the time of COVID-19: towards Fetal monitoring 4.0

Kahánková

Barnova

Jaroš

et al. 2023

BMC Pregnancy Childbirth

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On the outbreak of the global COVID-19 pandemic, high-risk and vulnerable groups in the population were at particular risk of severe disease progression. Pregnant women were one of these groups. The infectious disease endangered not only the physical health of pregnant women, but also their mental well-being. Improving the mental health of pregnant women and reducing their risk of an infectious disease could be achieved by using remote home monitoring solutions. These would allow the health of the mother and fetus to be monitored from the comfort of their home, a reduction in the number of physical visits to the doctor and thereby eliminate the need for the mother to venture into high-risk public places. The most commonly used technique in clinical practice, cardiotocography, suffers from low specificity and requires skilled personnel for the examination. For that and due to the intermittent and active nature of its measurements, it is inappropriate for continuous home monitoring. The pandemic has demonstrated that the future lies in accurate remote monitoring and it is therefore vital to search for an option for fetal monitoring based on state-of-the-art technology that would provide a safe, accurate, and reliable information regarding fetal and maternal health state. In this paper, we thus provide a technical and critical review of the latest literature and on this topic to provide the readers the insights to the applications and future directions in fetal monitoring. We extensively discuss the remaining challenges and obstacles in future research and in developing the fetal monitoring in the new era of Fetal monitoring 4.0, based on the pillars of Healthcare 4.0.

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Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals

Magesh

Rajakumar

2023

Measurement: Sensors

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A portable prototype for diagnosing fetal arrhythmia

Cited by 5 publications

References 12 publications

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Pregnancy in the time of COVID-19: towards Fetal monitoring 4.0

Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals

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