School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Telangana, India.
Stud Health Technol Inform. 2023 Jun 29;305:40-43. doi: 10.3233/SHTI230418.
In this study, we attempted to classify categorical emotional states using Electrodermal Activity (EDA) signals and a configurable Convolutional Neural Network (cCNN). The EDA signals from the publicly available, Continuously Annotated Signals of Emotion dataset were down-sampled and decomposed into phasic components using the cvxEDA algorithm. The phasic component of EDA was subjected to Short-Time Fourier Transform-based time-frequency representation to obtain spectrograms. These spectrograms were input to the proposed cCNN to automatically learn the prominent features and discriminate varied emotions such as amusing, boring, relaxing, and scary. Nested k-Fold cross-validation was used to evaluate the robustness of the model. The results indicated that the proposed pipeline could discriminate the considered emotional states with a high average classification accuracy, recall, specificity, precision, and F-measure scores of 80.20%, 60.41%, 86.8%, 60.05%, and 58.61%, respectively. Thus, the proposed pipeline could be valuable in examining diverse emotional states in normal and clinical conditions.
在这项研究中，我们尝试使用皮肤电活动（EDA）信号和可配置的卷积神经网络（cCNN）对分类情绪状态进行分类。从公开的连续标注情感信号数据集获取 EDA 信号，并使用 cvxEDA 算法将其下采样并分解为相位分量。对 EDA 的相位分量进行基于短时傅里叶变换的时频表示，以获得频谱图。将这些频谱图输入到所提出的 cCNN 中，以自动学习突出的特征，并区分有趣、无聊、放松和可怕等不同的情绪。嵌套 k 折交叉验证用于评估模型的稳健性。结果表明，所提出的管道可以以高的平均分类准确性、召回率、特异性、精度和 F 度量分数分别为 80.20%、60.41%、86.8%、60.05%和 58.61%来区分所考虑的情绪状态。因此，该管道在正常和临床条件下检查不同的情绪状态可能具有价值。
