ECG Signal Analysis And Assisted Diagnosis Technology Based On Deep Learning

Electrocardiogram(ECG)can reflect the human's heart health and is widely used in the early diagnosis and treatment of cardiovascular diseases.However,the current interpretation of ECG data in clinical practice is basically based on the clinical experience of doctors.Due to the large and tedious ECG data,limited medical resources are even more tense.Since the middle of the 20th century,artificial intelligence-based auxiliary disease diagnosis systems have been widely used in clinical disease diagnosis and treatment of modern medicine.The artificial intelligence-assisted diagnostic system relies on modern technology to simulate the various diagnostic thinking and inferential disease judgment processes of experts in the clinical disease diagnosis process and practice.Clinical disease diagnosis decision-making and medical support.It has gradually developed into an effective technical aid for the diagnosis and treatment of clinical diseases.For the analysis and diagnosis tasks of ECG signals,a new data preprocessing method for ECG signals is proposed in this paper.A variety of deep neural network models with different structures is established to give multi-class diagnosis results by analyzing and processing the ECG signals.A model based on ECG signal analysis and corresponding auxiliary diagnosis conclusions is implemented.In addition,data augmentation and transfer learning techniques is used to solve the problem of insufficient data in ECG samples.The effectiveness of data augmentation and the feasibility of the application of transfer learning technology in ECG signal analysis model is verified through experiments.The main work of this paper is as follows:(1)A preprocessing algorithm for ECG data is proposed,which processed the main noise and motion noise in the ECG data,and the data is processed into a certain length of sample data to facilitate model input and training.Experiments show that the algorithm has low computational complexity,convenient and fast processing of data,and the processed sample contains enough information to meet the requirements of model training and parameter learning.(2)Based on the ECG signal data,an end-to-end deep neural network model is used to analyze and process it.Finally,multiple aspects of diagnosis conclusions are given,including heart rate values and premature beats.More comprehensive analysis results are convenient for assisting doctors to do Further diagnostic conclusions.(3)Based on two different deep neural networks,fully connected neural networks and fully convolutional neural networks,a variety of different parameter configurations are designed.And a best-performing full convolutional neural network model is obtained.Finally,this paper experimented with the application of data augmentation technology and transfer learning in the ECG signal analysis model,and considered and prospected its further research directions..