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Comprehensive Study of Fetal Monitoring Methods for Detection of Fetal Compromise

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Machine Learning for Predictive Analysis

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 141))

Abstract

Fetal monitoring usually refers to monitoring fetal heart rate (FHR) for detection fetal well-being. This is important activity carried out by doctors during prepartum, intrapartum phase as per health requirement of patient. Fetal monitoring is required to reduce chances of fetal to become hypoxic (when fetal is deprived from sufficient oxygen) that can cause fetal brain injury and even fetal death. Fetal monitoring plays important role in reducing mortality and morbidity rate. The most common noninvasive fetal monitoring device is cardiotocograph (CTG). CTG captures FHR based on Doppler ultrasound principle and uterine contractions (UC) based on pressure transducers. The present study highlights accuracy limitations of CTG and proposes more accurate noninvasive fetal ECG (NIfECG) as data acquisition methods to acquire FHR and electrohysterogram (EHG) to capture UC. CTG interpretation is one of the decision-making parameters used by doctors for early intervention like caesarian section. CTG interpretation often suffers from inter-observer and intra-observer agreement of CTG patterns which are non-reassuring. To overcome this limitation, computerized analysis can be useful. The present study also discusses usage of machine learning to detect fetal compromise. Further, in addition to FHR and UC analysis, we also propose to use ST waveform analysis to improve results.

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Authors and Affiliations

  1. School of Computer Engineering and Technology, MITWPU, Pune, India

    Vidya Sujit Kurtadikar & Himangi Milind Pande

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  1. Vidya Sujit Kurtadikar
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  2. Himangi Milind Pande
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Correspondence to Vidya Sujit Kurtadikar .

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Editors and Affiliations

  1. Global Knowledge Research Foundation, Gujarat, India

    Amit Joshi

  2. University of Osaka, Suita, Japan

    Mahdi Khosravy

  3. School of Engineering and Computer Science, Oakland University, Rochester Hills, MI, USA

    Neeraj Gupta

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Kurtadikar, V.S., Pande, H.M. (2021). Comprehensive Study of Fetal Monitoring Methods for Detection of Fetal Compromise. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_15

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