Feature Selection Framework for XGBoost Based on Electrodermal Activity in Stress Detection
Journal
IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
Journal Volume
2019-October
Pages
330-335
ISBN
9.78173E+12
Date Issued
2019
Author(s)
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. © 2019 IEEE.
Subjects
electrodermal activity(EDA); extreme gradient boosting; feature selection; signal processing; Stress detection
SDGs
Other Subjects
Classification (of information); Electrodes; Signal processing; Silicon compounds; Stresses; Classification algorithm; Electrodermal activity; Gradient boosting; High frequency HF; Low-frequency; Selection framework; Signal segmentation; Stress detection; Feature extraction
Type
conference paper