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

• Published in: Sadhana (Bangalore) Vol. 44; no. 4; pp. 1 - 12
• 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: Journal Article
• Language: English
• Published: New Delhi Springer India 01.04.2019; Springer Nature B.V
• Subjects: Anesthesia; Classification; Decision making; Electroencephalography; Engineering; Machine learning; Model accuracy; Pain; Principal components analysis; Signal processing; Support vector machines; pain assessment; uterine contraction; Electroencephalography (EEG); Principal Component Analysis (PCA); first stage of labour; Support Vector Machine (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...