In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.
In the field of artificial intelligence (AI) medical imaging, data annotation is a key factor in all AI development. In the traditional manual annotation process, there are prominent problems such as difficult data acquisition, high manual labor intensity, strong professionalism and low labeling quality. Therefore, an intelligent multimodal medical image annotation system is urgently needed to meet the requirements of labeling. Based on the image cloud, West China Hospital of Sichuan University collected the multimodal image data of hospital and allied hospitals, and designed a multi-modal image annotation system through information technology, which integrated various image processing algorithms and AI models to simplify the image data annotation. With the construction of annotation system, the efficiency of data labeling in the hospitals is improved, which provides necessary data support for the AI image research and related industry construction in the hospital, so as to promote the implementation of artificial intelligence industry related to medical images in the hospital.