Classification of labour contractions using KNN classifier

R. Jyothi, Sujit Hiwale, Parvati V. Bhat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Uterine contractions of desired strength and frequency are one of the important requirements for the smooth progress of cervical dilation and delivery of baby. Presently, Tocograph is used to monitor the strength, duration and frequency of uterine contractions. One of the major drawbacks of Tocography is subjectivity in interpretation. Therefore, there is a need for an objective method for classification of uterine contractions. In this paper, a K Nearest Neighbor based classification method is presented for automated classification of different types of uterine contractions during labour. For the study, CTG signals from Physionet database were used. The signals were annotated by an expert Doctor in three categories: mild (n=33), moderate (n=64) and strong (n=96). After processing of signals, eight features were extracted, followed by implementation of an appropriate classifier. K Nearest Neighbor and Rule based K Nearest Neighbor classifiers were used to classify the uterine contractions into mild, moderate and strong. We achieved an accuracy of 90.91%, 85% and 85.71% for classification using Rule based K Nearest Neighbor classification method.

Original languageEnglish
Title of host publication2016 International Conference on Systems in Medicine and Biology, ICSMB 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages110-115
Number of pages6
ISBN (Electronic)9781467376662
DOIs
Publication statusPublished - 28-04-2017
Event2016 International Conference on Systems in Medicine and Biology, ICSMB 2016 - Kharagpur, India
Duration: 04-01-201607-01-2016

Conference

Conference2016 International Conference on Systems in Medicine and Biology, ICSMB 2016
CountryIndia
CityKharagpur
Period04-01-1607-01-16

Fingerprint

Uterine Contraction
Classifiers
Personnel
Uterine Monitoring
Dilatation
Databases
Processing

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Health Informatics
  • Radiology Nuclear Medicine and imaging
  • Assessment and Diagnosis
  • Human-Computer Interaction

Cite this

Jyothi, R., Hiwale, S., & Bhat, P. V. (2017). Classification of labour contractions using KNN classifier. In 2016 International Conference on Systems in Medicine and Biology, ICSMB 2016 (pp. 110-115). [7915100] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSMB.2016.7915100
Jyothi, R. ; Hiwale, Sujit ; Bhat, Parvati V. / Classification of labour contractions using KNN classifier. 2016 International Conference on Systems in Medicine and Biology, ICSMB 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 110-115
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Jyothi, R, Hiwale, S & Bhat, PV 2017, Classification of labour contractions using KNN classifier. in 2016 International Conference on Systems in Medicine and Biology, ICSMB 2016., 7915100, Institute of Electrical and Electronics Engineers Inc., pp. 110-115, 2016 International Conference on Systems in Medicine and Biology, ICSMB 2016, Kharagpur, India, 04-01-16. https://doi.org/10.1109/ICSMB.2016.7915100

Classification of labour contractions using KNN classifier. / Jyothi, R.; Hiwale, Sujit; Bhat, Parvati V.

2016 International Conference on Systems in Medicine and Biology, ICSMB 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 110-115 7915100.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Jyothi R, Hiwale S, Bhat PV. Classification of labour contractions using KNN classifier. In 2016 International Conference on Systems in Medicine and Biology, ICSMB 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 110-115. 7915100 https://doi.org/10.1109/ICSMB.2016.7915100