A Novel Channel Selection Approach for Human Neonate’s Pain EEG Data Analysis

Abstract

Quantitative measurement of pain using the Electroencephalogram (EEG) signals has received much attention, recently. Pain EEG data processing is associated with complexity and high computational cost. This study aims to propose a new method for selecting efficient EEG channels to determine the area of the scalp that contains the most information about brain activity during acute pain in neonates. Also, selecting relevant channels in pain assessment reduces computational costs. In this study, a new channel selection approach is proposed, which is a combination of filter and wrapper methods. A new pseudo-Sequential Forward Feature Selection (pseudo-SFFS) method is presented to reduce the computational complexity of wrapper methods. We preprocessed data by applying a bandpass filter. We used wavelet transform to extract features. After extracting the features, we applied two feature selection steps. In the first step, we applied the T-test to the extracted features. In the second step, we selected the effective channels based on the output of the applied pseudo-SFFS algorithm into Support Vector Machine (SVM), Decision Tree (DT), and Gaussian Naive Bayesian (GNB) classifiers. Using the proposed method two channels of the sensorimotor cortex including Cz and C4 channels have been selected from 18 EEG channels for pain stimulation through the left heel of neonates. Also, the results show that most of the acute pain information of neonates is related to the delta and theta frequency bands.