Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been globally accepted and widely applied. Recently, great improvements have been made in the development of machine learning for EEG-based emotion detection. However, there are still some major disadvantages in previous studies. Firstly, traditional machine learning methods require extracting features manually which is time-consuming and rely heavily on human experts. Secondly, to improve the model accuracies, many researchers used user-dependent models that lack generalization and universality. Moreover, there is still room for improvement in the recognition accuracies in most studies. Therefore, to overcome these shortcomings, an EEG-based novel deep neural network is proposed for emotion classification in this article. The proposed 2D CNN uses two convolutional kernels of different sizes to extract emotion-related features along both the time direction and the spatial direction. To verify the feasibility of the proposed model, the pubic emotion dataset DEAP is used in experiments. The results show accuracies of up to 99.99% and 99.98 for arousal and valence binary classification, respectively, which are encouraging for research and applications in the emotion recognition field.

Objective . The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to develop an algorithm that can automatically identify 30 arrhythmias by using varying-dimensional ECG signals. Approach . In this paper, we firstly proposed a novel multi-scale 2D CNN that can effectively capture pathological information from small-scale to large-scale from ECG signals to identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs. Secondly, we explored the effects of varying convolution kernels sizes and branch subnetworks on the model’s performance for each arrhythmia. Thirdly, we introduced the weighted focal loss to alleviate the positive-negative class imbalance problem in the multi-label arrhythmias classification. Fourthly, we explored the utility of reduced-lead ECGs in detecting arrhythmias by comparing the performances of models on varying-dimensional ECGs. Main results . As a follow-up entry after the PhysioNet/Computing in Cardiology Challenge (2021), our proposed approach achieved the official test scores of 0.52, 0.47, 0.53, 0.51, and 0.50 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs on the hidden test set (comparable to that of 6th, 11th, 4th, 5th, and 7th out of 39 teams in the Challenge). Significance . A multi-scale framework capable of detecting 30 arrhythmias from varying-dimensional ECGs was proposed in our work. We preliminarily verified that the multi-scale perception fields may be necessary to capture more comprehensive pathological information for arrhythmias detection. Besides, we also verified that the weighted focal loss may alleviate the positive–negative class imbalance and improve the model’s generalization performance on the cross-dataset. In addition, we observed that some reduced-lead models, such as the 4-lead and 3-lead models, can even achieve performance that is almost comparable to that of the 12-lead model. The excellent performance of our proposed framework demonstrates its great potential in detecting a wide range of arrhythmias.