Ferreira Jacqueline, Brás Susana, Silva Carlos F, Soares Sandra C
Center for Health Technology and Services Research, Department of Education & Psychology, University of Aveiro, Aveiro, Portugal.
Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
Psychophysiology. 2017 Apr;54(4):620-627. doi: 10.1111/psyp.12808. Epub 2016 Dec 31.
The electrocardiogram (ECG) signal has been widely used to study the physiological substrates of emotion. However, searching for better filtering techniques in order to obtain a signal with better quality and with the maximum relevant information remains an important issue for researchers in this field. Signal processing is largely performed for ECG analysis and interpretation, but this process can be susceptible to error in the delineation phase. In addition, it can lead to the loss of important information that is usually considered as noise and, consequently, discarded from the analysis. The goal of this study was to evaluate if the ECG noise allows for the classification of emotions, while using its entropy as an input in a decision tree classifier. We collected the ECG signal from 25 healthy participants while they were presented with videos eliciting negative (fear and disgust) and neutral emotions. The results indicated that the neutral condition showed a perfect identification (100%), whereas the classification of negative emotions indicated good identification performances (60% of sensitivity and 80% of specificity). These results suggest that the entropy of noise contains relevant information that can be useful to improve the analysis of the physiological correlates of emotion.
心电图（ECG）信号已被广泛用于研究情绪的生理基础。然而，寻找更好的滤波技术以获得质量更高且包含最大相关信息的信号，仍然是该领域研究人员面临的一个重要问题。信号处理在很大程度上用于心电图分析和解读，但这个过程在描绘阶段可能容易出错。此外，它可能导致重要信息的丢失，这些信息通常被视为噪声，因此在分析中被丢弃。本研究的目的是评估心电图噪声是否能够用于情绪分类，同时将其熵作为决策树分类器的输入。我们从25名健康参与者那里收集了心电图信号，当时他们观看了引发负面（恐惧和厌恶）情绪和中性情绪的视频。结果表明，中性状态显示出完美的识别率（100%），而负面情绪的分类显示出良好的识别性能（敏感性为60%，特异性为80%）。这些结果表明，噪声的熵包含相关信息，这可能有助于改进对情绪生理相关性的分析。
