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

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, DOI https://doi.org/10.1007/s12046-019-1058-4.

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Official URL: https://doi.org/10.1007/s12046-019-1058-4


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.

Item Type: Article
Funders: Ministry of Higher Education, Malaysia, and University of Malaya through HIR Grant (UM.C/HIR/MOHE/ENG/16 Account Code: D000016-16001) and PPP Grant (PG260-2015B)
Uncontrolled Keywords: Electroencephalography (EEG); first stage of labour; pain assessment; Principal Component Analysis (PCA); Support Vector Machine (SVM); uterine contraction
Subjects: R Medicine
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 28 Jan 2020 00:36
Last Modified: 28 Jan 2020 00:36
URI: http://eprints.um.edu.my/id/eprint/23585

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