# Multi-feature classifiers for burst detection in single EEG channels from preterm infants

## TL;DR

This study compares machine learning classifiers for detecting EEG bursts in preterm infants aged 36 weeks or more, demonstrating high accuracy and potential for monitoring brain maturation with a single EEG channel.

## Contribution

It introduces and evaluates multiple machine learning classifiers, highlighting logistic regression as an efficient and accurate method for EEG burst detection in older preterm infants.

## Key findings

- kNN, SVM, and LR classifiers achieved about 95% accuracy
- LR classifier had the highest agreement with human labels (Cohen's kappa = 0.71)
- Long EEG bursts correlate with higher postmenstrual age

## Abstract

The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA $\geq$ 36 weeks) using multi-feature classification on a single EEG channel. Five EEG burst detectors relying on different machine learning approaches were compared: Logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36 - 41 weeks PMA. The most performing classifiers reached about 95\% accuracy (kNN, SVM and LR) whereas Th obtained 84\%. Compared to human-automatic agreements, LR provided the highest scores (Cohen's kappa = 0.71) and the best computational efficiency using only three EEG features. Applying this classifier in a test database of 21 infants $\geq$ 36 weeks PMA, we show that long EEG bursts and short inter-bust periods are characteristic of infants with the highest PMA and weights. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.