论文标题
使用机器学习和心电图信号的情感识别
Emotion Recognition using Machine Learning and ECG signals
论文作者
论文摘要
各种情绪可以产生心电图（ECG）信号的变化，可以通过ECG信号的不同变化来区分不同的情绪。这项研究是关于使用心电图信号识别情绪的。收集了四种情绪的数据，即快乐，令人兴奋，镇定和紧张。然后，使用有限的脉冲滤波器对原始数据进行解除。我们使用离散的余弦变换（DCT）从获得的数据中提取特征，以提高情绪识别的准确性。探索了分类器支持向量机（SVM），随机森林和K-NN。为了找到SVM分类器的最佳参数，使用了粒子群优化（PSO）技术。这些分类方法的比较结果表明，SVM方法在情感识别方面具有更大的精度，可以在实践中应用
Various emotions can produce variations in electrocardiograph (ECG) signals, distinct emotions can be distinguished by different changes in ECG signals. This study is about emotion recognition using ECG signals. Data for four emotions, namely happy, exciting, calm, and tense, is gathered. The raw data is then de-noised with a finite impulse filter. We use the Discrete Cosine Transform (DCT) to extract characteristics from the obtained data to increase the accuracy of emotion recognition. The classifiers Support Vector Machine (SVM), Random Forest, and K-NN are explored. To find the optimal parameters for the SVM classifier, the Particle Swarm Optimization (PSO) technique is used. The results of the comparison of these classification methods demonstrate that the SVM approach has a greater accuracy in emotion recognition, which may be applied in practice
