Key result
A novel feature selection method based on emotional state transition achieved 99.1% accuracy in stress detection when combining ECG, BVP, and GSR signals.
Why the study?
Conventional feature selection methods relied on differences between discrete emotional states and ignored the dynamic process of an individual's emotional stress in real life.
Does a feature selection method based on emotional state transition improve the accuracy of emotional stress assessment using physiological signals in healthy adults?
Does a feature selection method based on emotional state transition improve the accuracy of emotional stress assessment using physiological signals in healthy adults?
A novel feature selection method based on emotional state transition improves the accuracy of emotion recognition and psychological stress detection using ECG and PPG signals compared to conventional methods.
May advance wearable stress monitoring algorithms; leaves open clinical validation in patients.
The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.
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Li et al. (2023) studied Emotional stress (n=85). Feature selection method based on emotional state transition vs. Principal Component Analysis (PCA) and discrete emotional states feature selection methods was evaluated on Accuracy in stress detection using combined ECG, BVP, and GSR signals. A novel feature selection method based on emotional state transition achieved 99.1% accuracy in stress detection when combining ECG, BVP, and GSR signals.
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