Research of electroencephalography representational emotion recognition based on deep belief networks / 生物医学工程学杂志

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.

Automatic recognition of pulmonary nodules images based on deep belief networks / 国际生物医学工程杂志

Objective To propose a method based on deep belief network (DBN) to automatically identify pulmonary nodules so as to improve the detection accuracy of pulmonary nodules.Methods To meet the training sample requirements of DBN,a database of 4 000 lung nodule images identified by professional doctors was established,and the sample database was expanded using virtual sample technology.In this technology,new samples of the database were generated from the manually recognized region of interest (ROI) by rotation,scaling and panning,or by a series of combinations of two or more operations of panning,scaling,rotation,and compositing.Finally,some samples from the sample database were input into the convolutional neural network classifier,and the ROI of the suspected pulmonary nodule was output by optimizing the network parameters.Result The sample size of the training sample database was expanded to 40 000 using the virtual sample expansion.Based on the training database obtained by this method,the detection accuracy of DBN for identifying pulmonary nodules was 90％,and the false positive rate was 0.4％.Conclusion Virtual sample technology can effectively improve the efficiency of training database establishment.The accuracy of using DBN-based CAD technology to detect pulmonary nodules is high,allowing doctors to focus only on areas where lung nodules are detected,thus effectively improving the efficiency of diagnosis.

Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction / 대한의료정보학회지

OBJECTIVES: Cardiovascular predictions are related to patients' quality of life and health. Therefore, a risk prediction model for cardiovascular conditions is needed. METHODS: In this paper, we propose a cardiovascular disease prediction model using the sixth Korea National Health and Nutrition Examination Survey (KNHANES-VI) 2013 dataset to analyze cardiovascular-related health data. First, statistical analysis was performed to find variables related to cardiovascular disease using health data related to cardiovascular disease. Second, a model of cardiovascular risk prediction by learning based on the deep belief network (DBN) was developed. RESULTS: The proposed statistical DBN-based prediction model showed accuracy and an ROC curve of 83.9% and 0.790, respectively. Thus, the proposed statistical DBN performed better than other prediction algorithms. CONCLUSIONS: The DBN proposed in this study appears to be effective in predicting cardiovascular risk and, in particular, is expected to be applicable to the prediction of cardiovascular disease in Koreans.

Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring / 대한의료정보학회지

OBJECTIVES: Stress management is related to public healthcare and quality of life; an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. METHODS: In this paper, we present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data. Statistical analysis was performed to identify the nine features of stress detection, and we evaluated the performance of the proposed stress classification by comparison with several stress detection models. The proposed model was also evaluated using Deep Belief Networks (DBN). RESULTS: We designed profiles depending on the number of hidden layers, nodes, and hyper-parameters according to the loss function results. The experimental results showed that the proposed model achieved an accuracy and a specificity of 66.23% and 75.32%, respectively. The proposed DBN model performed better than other classification models, such as support vector machine, naive Bayesian classifier, and random forest. CONCLUSIONS: The proposed model in this study was demonstrated to be effective in classifying stress detection, and in particular, it is expected to be applicable for stress prediction in stress monitoring systems.