Feature Selection Framework for XGBoost Based on Electrodermal Activity in Stress Detection

Abstract

Since stress has a strong influence on human’s health, it is necessary to automatically detect stress in our daily life. In this paper, we aim to improve the performance and obtain the dominant features in stress detection based on Electrodermal Activity (EDA). Compared to the methods in Wearable Stress and Affect Dataset (WESAD), we propose several enhancements to get higher f1-scores, including less overlapped signal segmentation, more signal processing features, and extreme gradient boosting classification algorithm (XGBoost). Furthermore, we select dominant features according to their importance in classifier and correlation among other features while keeping high performance. Experiment results show that with 9 dominant features in XGBoost, we can achieve 92.38% (+ 17.87%) and 89.92% (+14.58%) f1-scores compared to WESAD on chest-and wrist-based EDA signal respectively. The features we choose suggest that the magnitude of low frequency and the complexity of high frequency EDA signal contain the most significant information in stress detection.