Abstract: Objective emotion recognition is significantly important for some fields such as physiological health, healthcare and education. From the perspectives of signal acquisition difficulty, cost and user acceptance, the electrocardiogram (ECG) signals are appropriate biomarkers for achieving objective emotion recognition. As it is difficult for the deep-learning based methods to extract and fuse the spatio-temporal features of one-dimensional ECG signals, a 1D-2D transformation method based on wavelet packet decomposition was proposed first, which converts one-dimensional ECG signals to "two-dimensional images". Subsequently, the ResNet18 was used as the backbone network and the "two-dimensional images" were used as its input, where a FusionBlock module was designed to improve the backbone network's spatio-temporal feature extraction and fusion capabilities. Finally, extensive experiments were implemented for the emotion recognition task on the WESAD and SWELL-KW datasets. In comparison with the suboptimal method, the experimental results demonstrate that the emotion recognition method proposed in this paper improved the performance in both metrics of Average Accuracy and F1 scores by 2.19 and 4.48 percent respectively, which may provide the technical support for the objective emotion recognition.