反褶积
人工智能
模式识别(心理学)
计算机科学
分解
支持向量机
Boosting(机器学习)
随机森林
主成分分析
补品(生理学)
分解法(排队论)
信号处理
机器学习
算法
数学
数字信号处理
统计
心理学
计算机硬件
生态学
神经科学
生物
作者
Sriram Kumar P,Praveen Kumar Govarthan,Nagarajan Ganapathy,Jac Fredo Ar
标识
DOI:10.1142/s0219519423400432
摘要
This study analyzed five decomposition algorithms for separating electrodermal activity (EDA) into tonic and phasic components to identify different emotions using machine learning algorithms. We used EDA signals from the Continuously Annotated Signals of Emotion dataset for this analysis. First, we decomposed the EDA signals into tonic and phasic components using five decomposition methods: continuous deconvolution analysis, discrete deconvolution analysis, convex optimization-based EDA, nonnegative sparse deconvolution (SparsEDA), and BayesianEDA. We extracted time, frequency, and time-frequency domain features from each decomposition method’s tonic and phasic components. Finally, various machine learning algorithms such as logistic regression (LR), support vector machine, random forest, extreme gradient boosting, and multilayer perceptron were applied to evaluate the performance of the decomposition methods. Our results show that the considered decomposition methods successfully split the EDA signal into tonic and phasic components. The SparsEDA decomposition method outperforms the other decomposition methods considered in the study. In addition, LR with features extracted from the tonic component of the SparsEDA achieved highest average classification accuracy of 95.83%. This study can be used to identify the optimal decomposition methods suitable for emotion recognition applications.
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