Staff View: Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM

Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM

This study aimed to assess the utility of electroencephalography (EEG) as an objective marker of pain during the first stage of labour. EEG and cardiotocography (CTG) data were obtained from 10 parturient women during their first stage of labour. The study subjects reported the extent of their pain...

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Main Authors:	Sai, Chong Yeh, Mokhtar, Norrima, Yip, Hing Wa, Bak, Lindy Li Mei, Hasan, Mohd Shahnaz, Arof, Hamzah, Cumming, Paul, Mat Adenan, Noor Azmi
Format:	Article
Published:	Springer 2019
Subjects:	R Medicine TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/23585/ https://doi.org/10.1007/s12046-019-1058-4
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spelling	my.um.eprints.235852020-01-28T00:36:13Z http://eprints.um.edu.my/23585/ Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM Sai, Chong Yeh Mokhtar, Norrima Yip, Hing Wa Bak, Lindy Li Mei Hasan, Mohd Shahnaz Arof, Hamzah Cumming, Paul Mat Adenan, Noor Azmi R Medicine TK Electrical engineering. Electronics Nuclear engineering This study aimed to assess the utility of electroencephalography (EEG) as an objective marker of pain during the first stage of labour. EEG and cardiotocography (CTG) data were obtained from 10 parturient women during their first stage of labour. The study subjects reported the extent of their pain experienced due to uterine contractions, which were recorded by the CTG tracing. Simultaneous 16-channel EEG traces were obtained for spectral analysis and a subsequent machine learning classification using Support Vector Machine (SVM) aiming to predict the pain experienced in relation to uterine contractions. It was found that pain due to uterine contraction correlated positively with relative delta and beta band activities and negatively with relative theta and alpha band activities of the EEG signals. SVM using the spectral activities, statistical and non-linear features of the EEG classified the state of pain with 83% accuracy using a classification model generalizable across subjects. Furthermore, dimension reduction using Principal Component Analysis (PCA) successfully reduced the number of features used in the classification while achieving a maximum classification accuracy of 84%. Continuous EEG affords the means to assess objectively maternal pain experienced during the active contraction phase of the first stage of labour. Monitoring of the pain experience using EEG signals may complement the clinical decision-making process behind administration of epidural anaesthesia during labour. We envision future studies to investigate EEG markers of pain in other clinical states, aiming to generalize the use of EEG as an objective method of pain assessment. © 2019, Indian Academy of Sciences. Springer 2019 Article PeerReviewed Sai, Chong Yeh and Mokhtar, Norrima and Yip, Hing Wa and Bak, Lindy Li Mei and Hasan, Mohd Shahnaz and Arof, Hamzah and Cumming, Paul and Mat Adenan, Noor Azmi (2019) Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM. Sadhana, 44 (4). p. 87. ISSN 0256-2499 https://doi.org/10.1007/s12046-019-1058-4 doi:10.1007/s12046-019-1058-4
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	R Medicine TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	R Medicine TK Electrical engineering. Electronics Nuclear engineering Sai, Chong Yeh Mokhtar, Norrima Yip, Hing Wa Bak, Lindy Li Mei Hasan, Mohd Shahnaz Arof, Hamzah Cumming, Paul Mat Adenan, Noor Azmi Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM
description	This study aimed to assess the utility of electroencephalography (EEG) as an objective marker of pain during the first stage of labour. EEG and cardiotocography (CTG) data were obtained from 10 parturient women during their first stage of labour. The study subjects reported the extent of their pain experienced due to uterine contractions, which were recorded by the CTG tracing. Simultaneous 16-channel EEG traces were obtained for spectral analysis and a subsequent machine learning classification using Support Vector Machine (SVM) aiming to predict the pain experienced in relation to uterine contractions. It was found that pain due to uterine contraction correlated positively with relative delta and beta band activities and negatively with relative theta and alpha band activities of the EEG signals. SVM using the spectral activities, statistical and non-linear features of the EEG classified the state of pain with 83% accuracy using a classification model generalizable across subjects. Furthermore, dimension reduction using Principal Component Analysis (PCA) successfully reduced the number of features used in the classification while achieving a maximum classification accuracy of 84%. Continuous EEG affords the means to assess objectively maternal pain experienced during the active contraction phase of the first stage of labour. Monitoring of the pain experience using EEG signals may complement the clinical decision-making process behind administration of epidural anaesthesia during labour. We envision future studies to investigate EEG markers of pain in other clinical states, aiming to generalize the use of EEG as an objective method of pain assessment. © 2019, Indian Academy of Sciences.
format	Article
author	Sai, Chong Yeh Mokhtar, Norrima Yip, Hing Wa Bak, Lindy Li Mei Hasan, Mohd Shahnaz Arof, Hamzah Cumming, Paul Mat Adenan, Noor Azmi
author_facet	Sai, Chong Yeh Mokhtar, Norrima Yip, Hing Wa Bak, Lindy Li Mei Hasan, Mohd Shahnaz Arof, Hamzah Cumming, Paul Mat Adenan, Noor Azmi
author_sort	Sai, Chong Yeh
title	Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM
title_short	Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM
title_full	Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM
title_fullStr	Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM
title_full_unstemmed	Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM
title_sort	objective identification of pain due to uterine contraction during the first stage of labour using continuous eeg signals and svm
publisher	Springer
publishDate	2019
url	http://eprints.um.edu.my/23585/ https://doi.org/10.1007/s12046-019-1058-4
_version_	1657488231140163584
score	13.211869

Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM

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