Study On Convolutional Neural Networks For Classification And Reconstruction Of Biomedical Signal

Biomedical signals are the most direct descriptions of living organisms that can be collected through instruments and devices.They usually come from a variety of sources and are widely used,and they are key research objects in biology,informatics,medicine,and other disciplines.With the rapid development of biomedical related disciplines,scientific researchers and medical workers are increasingly demanding such signal-related processing and analysis technologies.Efficient biological signal processing methods can effectively enhance researchers' exploration of life mechanisms,so as to better reveal the relationship between physiological structure and function,and then to promote the emergence of major discoveries.A high-precision medical signal analysis strategy can share the pressure of the doctors' diagnosis to a certain extent and assist them in formulating a plan that is more conducive to disease prevention and treatment,thereby reducing the patient's physical and mental pain and improving the health of the community.Traditional signal analysis methods have been difficult to meet the growing demand for biomedical signal processing.Using advanced machine learning techniques to effectively model and analyze the issues involved has become a research hotspot in this field.Deep learning refers to machine learning method using multi-layer neural networks to obtain the characteristic representation of data for further analyzing.As the focus of research in the field of machine learning today,deep learning is leading a new round of artificial intelligence trends.With its powerful nonlinear feature representation capabilities,convolutional neural networks are the most widely used and most comprehensive network structures in many deep learning models,achieving a series of unprecedented breakthroughs in many tasks represented by pattern recognition.Inspired by the success of convolutional neural networks in other fields,considering characteristics of specific biomedical signals(mammography and electroencephalogram),this paper presents a number of biomedical signal classification and reconstruction methods based on convolutional neural networks.The main innovations of this article are as follows:1.In the classification task of breast masses based on mammography X-ray films,designing visual features that can be effectively described and easily differentiated between benign and malignant masses in the feature space is the key to complete high-precision computer-aided diagnosis.The existing manual visual features can only describe the visual characteristics of breast masses from a single visual level,and neglect the synergistic effects of different levels of visual features in the diagnosis of breast cancer.And the ability to express a single visual feature is limited.Traditional multi-feature fusion methods require complex feature selection processes.In view of the above problems,this paper proposes a breast mass classification method based on convolutional neural network feature representation.Specifically,the method first constructs and trains feature representation networks based on natural images and mammographic images.Then,referring to the doctor's actual diagnostic experience,this network was used to obtain different levels of feature descriptions suitable for the classification of breast masses.Finally,a decision mechanism for the characteristics of breast masses is proposed to complete the judgment of benign and malignant breast masses.The experimental results show that the algorithm can achieve high precision classification of benign and malignant breast masses.2.In the computer-aided diagnosis of breast diseases,atypical samples with indistinct visual features are often difficult to be dealt with.In the traditional handcrafted visual feature space and convolutional neural network feature space,neither of these types of samples can be effectively described.Through the feature space transformation operation,mapping the samples that cannot be effectively described in the original feature space to an easily distinguishable feature space is an effective way to solve such problems.For this reason,aiming at the classification of breast masses,this paper proposes an improved convolutional neural network model based on large margin metric learning.Specifically,first,by introducing a large margin metric learning loss function,the mapping relationship from the original convolutional neural network feature space to the new feature space is learned.Furthermore,from this mapping relationship,the characteristics of masses with more compact intraclass distributions and more discrete distributions between classes are obtained.In addition,through the continuous provision of new error samples to the network,a network training improvement strategy focusing on difficult cases was proposed.Experimental results show that the large interval metric learning layer of the algorithm can improve the feature distinguishing degree and the network classification accuracy.The improved network training strategy can further improve the performance of the network in benign and malignant lump discrimination tasks.3.In the task of neural decoding based on EEG signals,classification of EEG signals evoked by different types of stimuli is one of the basic goals in the field of research.EEG signals contain abundant spatio-temporal information.Traditional EEG signal feature description methods can only represent one of these features and cannot provide more effective feature representation for subsequent classification operations.In view of the above problems,this paper proposes an EEG signal classification method based on spatio-temporal fusion convolutional neural network.Specifically,first of all,through different generation methods of EEG activation maps,EEG activation maps focusing on spatial information and those focusing on temporal information are generated.Then,two convolutional neural networks for different kinds of activation maps is designed and trained separately to obtain a convolutional neural network feature representation of the brain electrical signals.Finally,the two methods of feature stitching and feature selection are used to achieve the classification of EEG signals.Experimental results show that the feature representation part of the algorithm can obtain differentiated EEG feature representations,and subsequent feature fusion strategies can effectively improve the classification accuracy of EEG signals.4.The reconstruction of visual stimuli based on EEG signals is a neural decoding task based on the classification of high-precision EEG signals.This task is usually composed of highprecision EEG signal classification and feature representation,as well as visual stimulus generation in two phases.The traditional method based on EEG features in cognitive space is limited by the limit of human cognitive level and the error of neural signal acquisition,and it is difficult to obtain great improvement in classification accuracy and efficiency.Inspired by the fact that current convolutional neural networks achieve visual results beyond human performance,this paper proposes a visual feature-guided classification method of EEG signals.This method achieves higher-precision classification of EEG signals by mapping the feature representation of EEG signals into visual space.Then,based on the representation of EEG signals guided by visual features,this paper proposes an improved generational confrontation network model for generating visual stimuli.The experimental results of these two phases of EEG signal classification and visual stimuli generation show that the algorithm can effectively improve the classification accuracy of EEG signals and the quality of visual stimuli reconstruction results.