Acta Scientific Computer Sciences

Review Article Volume 3 Issue 12

Use of Machine Learning and Sensors for Monitoring Pregnancy

Meenal Kamlakar1* and Dipti D Patil2

1Department of Computer Engineering, Savitribai Phule Pune University, India
2Department of Information Technology, MKSSS’s Cummins College of Engineering for women, Pune

*Corresponding Author: Meenal Kamlakar, Department of Computer Engineering, Savitribai Phule Pune University, India.

Received: October 21, 2021; Published: November 10, 2021

Abstract

The experience of pregnancy and birthing is different for every mother and her baby. Unfortunately this experience may not always be very smooth due to various complications that may occur during delivering the baby. Premature babies even if kept in neonatal care are susceptible to a lot of hazardous health conditions. Underdeveloped brain and lungs, smaller or weaker babies resulting in feeding inabilities and being prone to diseases, are a few problems babies may face if born early. Also mothers can face problems due to high blood pressure, gestational or preexisting diabetes, infection or other medical conditions. Some mothers may face problems if induced with labour before time such as rupturing of placenta, and increased chances of C section [16].

There is thus a need to identify or if possible predict the risk associated with pregnancy and the mode of delivery. Different medical indicators or parameters can be used for monitoring pregnancy, predicting delivery time and mode of delivery [16]. These can be studied and can be analyzed using machine learning algorithms. The analysis can be used to predict the appropriate time and mode of childbirth along with the risks and it will be less error-prone. To support this wearable sensor device which senses abnormalities can be used remotely to monitor the patients health and take necessary actions in case of emergencies. The devices worn during delivery time can indicate or predict abnormalities while in labour to help prevent unfortunate events.

Keywords: Lactic Acid; Pregnancy; MRI Scans

Bibliography

  1. A Wearable Device For Monitoring Labor During Childbirth (Form 2 The Patent Act 1970 (39 Of 1970) and The Patent Rules, 2003 Complete Specification (Koninklijke Philips N.V., a Dutch Company, High Tech Campus 5, 5656 AE Eindhoven, The Netherlands.).
  2. Kok Beng Gan., et al. “Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results”. IEEE Transactions on Biomedical Engineering 8 (2009): 2075-2082.
  3. Mohammad Reza Mohebbian., et al. “Fetal ECG Extraction from Maternal ECG using Attention-based Cycle GAN”. IEEE Journal of Biomedical and Health Informatics (2021).
  4. Maha Messawa., et al. “The role of doppler ultrasound in high risk pregnancy: A comparative study”. Department of Obstetrics and Gynaecology, Hera General Hospital, Makkah, Saudi Arabia Journal of the Nigeria Medical Association 53.3 (2012): 116-120.
  5. Jonathan Lai., et al. “Performance of a wearable acoustic system for fetal movement discrimination”. PLoS One5 (2018): e0195728.
  6. Xin Zhaoa., et al. “A wearable system for in-home and long-term assessment of fetal movement”. The ENSAIT Textile Institute, Roubaix, Franceb INSERM CIC-IT 1403, Maison de Rgionale de la Recherche Clinique, CHRU de Lille, Lille, France.
  7. Navita Aggarwal and G L Sharma. “Fetal ultrasound parameters: Reference values for a local perspective”. Indian Journal of Radiology and Imaging2 (2020).
  8. Miha Lucovnik., et al. “Use of uterine electromyography to diagnose term and preterm labor”. Acta Obstetricia et Gynecologica Scandinavica 2 (2011): 150-157.
  9. Lise Loerup., et al. “Trends of blood pressure and heart rate in normal pregnancies: a systematic review and meta-analysis”. BMC Medicine 17 (2019): 167.
  10. S Datta., et al. Obstetric Anesthesia Handbook, C× Springer Science+Business Media, LLC 2006 (2010).
  11. Sarah Marshall, Adam Husney, Kathleen Romito and Kirtly Jones.
  12. Beeram Sumalatha., et al. “Electrocardiographic Changes during Normal Pregnancy”. Indian Journal of Cardiovascular Disease in Women-WINCARS 2 (2017): 35-38.
  13. R K Yadav., et al. “Lactic acid as an adjuvant marker in pregnancy-associated sepsis”. South African Journal of Obstetrics and Gynaecology 1 (2018).
  14. Shyamal Patel., et al. “Review of wearable sensors and systems with application in rehabilitation”. Journal of NeuroEngineering and Rehabilitation 9 (2012): 21.
  15. Wehle HD. “Machine Learning, Deep Learning and AI: What’s the Difference?” (2017).
  16. Meenal Kamlakar., et al. “Multi-Criteria Decision making based on PROMETHEE to prioritize hospital admission of pregnant women affected by COVID-19”. The PROMETHEE Days (2021).

Citation

Citation: Meenal Kamlakar and Dipti D Patil. “Use of Machine Learning and Sensors for Monitoring Pregnancy". Acta Scientific Computer Sciences 3.12 (2021): 38-41.

Copyright

Copyright: © 2021 Meenal Kamlakar and Dipti D Patil. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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