What question did this study set out to answer?

The central aim is to enhance pain detection in newborns by classifying EEG signals during painful events.

February 8, 20260 citations

Feature selection for electrical brain activity classification in newborns in case of painful events

VBV. R. BondarevNational Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”KIK.O. IvankoNational Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”APA.O. PopovNational Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Key Points

The central aim is to enhance pain detection in newborns by classifying EEG signals during painful events.
Analyzed EEG signals for time and time-frequency domains during noxious stimuli
Applied feature extraction techniques combined with forward feature selection
Compared six machine learning algorithms for classification accuracy
Conducted binary classification between EEG signals before and after painful stimuli
Distinguished between signals during painful events and without pain
Support vector machines achieved 93.5% accuracy for pre- and post-painful stimulus classification
Perfect classification of pre-painful EEG segments at 100% accuracy
Linear discriminant showed 84% accuracy for distinguishing painful from non-painful events
Achieved 76.9% accuracy for identifying EEG segments containing painful events
91.6% accuracy for classifying EEG segments without painful events

Abstract

The understanding of pain mechanisms in infants is critically important because newborns lack verbal communication abilities to report their pain experiences. This study focuses on analyzing electrical brain activity features in time and time-frequency domains using electroencephalographic (EEG) signals collected during clinically required noxious stimuli in newborns. Different feature extraction techniques are explored by applying a combined feature selection approach with forward feature selection and statistical measures involved. Six machine learning algorithms, namely Logistic Regression, Linear Discriminant, K-Nearest Neighbors, Support Vector Machines, Random Forest, and Gaussian Naive Bayes, were used and compared with the purpose of painful events recognition in newborns. Two binary classification tasks were considered: the first task was to distinguish between EEG signals before painful stimulus and after it (painless and painful state of the patient) and the second task was to distinguish between EEG signals on the background of the painful event (heel lance for blood sampling in neonates) and signals without painful events (audio stimulation). In the task of EEG signals classification into pre- and post-painful stimulus segments, the support vector machines classifier showed the best accuracy estimate of 93.5% with the pre-painful EEG segments classification accuracy of 100% and post-painful segment classification accuracy of 86.9%. In the task of distinguishing between EEG signals containing painful events as heel lance and signals without painful events, the linear discriminant algorithm showed the best accuracy estimate of 84% with 76.9% correctly determined EEG segments containing painful events and 91.6% correctly determined EEG segments without painful events. Results demonstrate the potential of using features that focus on spectral power in alpha, beta, and gamma frequency bands and machine learning techniques for advancing pain detection in neonates.

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