Supervised and Unsupervised Learning of Fetal Heart Rate Tracings with Deep Gaussian Processes

Feng, Guanchao; Quirk, J. Gerald; Djurić, Petar M.

doi:10.1109/neurel.2018.8586992

Cited by 23 publications

(15 citation statements)

References 24 publications

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“…The results indicate that they cannot be generated from a common manifold, i.e., the FHR and UA encode different information about fetal well-being. This is also consistent with our observations in [29], where the performance of classification of FHR can be improved using features from UA.…”

Section: Real Data: Ctg Segmentsupporting

confidence: 92%

Inference about Causality from Cardiotocography Signals Using Gaussian Processes

Feng

Quirk

Djurić

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a novel and simple method for discovery of Granger causality from noisy time series using Gaussian processes. More specifically, we adopt the concept of Granger causality, but instead of using autoregressive models for establishing it, we work with Gaussian processes. We show that information about the Granger causality is encoded in the hyperparameters of the used Gaussian processes. The proposed approach is first validated on simulated data, and then used for understanding the interaction between fetal heart rate and uterine activity in the last two hours before delivery and of interest in obstetrics. Our results indicate that uterine activity affects fetal heart rate, which agrees with recent clinical studies.

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Section: Real Data: Ctg Segmentsupporting

confidence: 92%

Inference about Causality from Cardiotocography Signals Using Gaussian Processes

Feng

Quirk

Djurić

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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“…(3) Deep Learning approaches-Finally, FHR analysis would benefit from the wider availability of large, documented and shared databases that permit robust evaluation of detection/performance of different machine learning methods. In particular, such data could be used with approaches based on 'deep learning' strategies 36,37 . These are powerful, next generation artificial intelligence approaches, where the computers independently seek what is important in the data and do not rely on human-derived features/characteristics.…”

Section: Signal Processing Methods For Data-driven Computerized Ctgmentioning

confidence: 99%

“…Finally, transabdominal electrophysiological monitoring can also provide information on uterine activity during labor. Conventional CTG interpretation considers variations in FHR in presence/absence of uterine contractions and recent studies have shown that also computerized CTG interpretation benefits from reliable information on uterine activity 36,37 .…”

Section: Trans-abdominal Fetal Electrocardiogram Monitoringmentioning

confidence: 99%

Computer‐based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK)

Georgieva

Abry

Chudáček

et al. 2019

Acta Obstet Gynecol Scand

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The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH<7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis 'mimicking' expert clinicians and those derived from 'data-driven' analyses; a critical review of the results from two randomized controlled trials

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“…Within this category, (n = 6) studies applied some form of data preparation: feature selection [61]- [63], SMOTE [64], and removal of noisy data [65]. One study [66] gathered CTG data from the CTU-UHB Intrapartum Cardiotocography Database, consisting of a total of 552 CTG recordings and applied feature selection techniques for data preparation. In [67], data were collected from the Daisy database in the form of fetal ECG recordings, and data used in [68] was gathered from the Massachusetts Institute of Technology (MIT) database in the form of maternal abdominal ECG recordings.…”

Section: B Fetal Statementioning

confidence: 99%

“…Supervised learning classification tasks have been performed by all articles in this group, and [61], [62], [65] and [66] also applied unsupervised learning in the form of DR techniques. Clustering techniques (also unsupervised learning) have been reported in [69].…”

Section: B Fetal Statementioning

confidence: 99%

Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

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Artificial Intelligence has been widely applied to a majority of research areas, including health and medicine. Certain complications or disorders that can appear during pregnancy can endanger the life of both mother and fetus. There is enough scientific literature to support the idea that emotional aspects can be a relevant risk factor in pregnancy (such as anxiety, stress or depression, for instance). This paper presents a scoping review of the scientific literature from the past 12 years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) to identify which methodologies, techniques, algorithms and frameworks are used in Artificial Intelligence and Affective Computing for pregnancy health and well-being. The methodology proposed by Arksey and O'Malley, in conjunction with PRISMA-ScR framework has been used to create this review. Despite the relevance that emotional status can have as a risk factor during pregnancy, one of the main findings of this study is that there is still not a significant amount of literature on automatic analysis of emotion. Health enhancement and well-being for pregnant women can be achieved with artificial intelligence or affective computing based devices, hence future work on this topic is strongly suggested.

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Supervised and Unsupervised Learning of Fetal Heart Rate Tracings with Deep Gaussian Processes

Cited by 23 publications

References 24 publications

Inference about Causality from Cardiotocography Signals Using Gaussian Processes

Inference about Causality from Cardiotocography Signals Using Gaussian Processes

Computer‐based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK)

Artificial Intelligence in Pregnancy: A Scoping Review

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