Research On ECG Anomaly Detection Algorithm Based On Deep Transfer Learning

Cardiovascular disease has become one of the main diseases threatening human life and health.Clinically,cardiovascular disease is often accompanied by arrhythmia.Some arrhythmias may cause symptoms of heart disease,including dizziness,fainting,and shortness of breath.Other types of arrhythmias,such as atrial fibrillation and ventricular fibrillation,may cause stroke and cardiac arrest.Therefore,timely and accurate detection of arrhythmia is urgent and necessary.Electrocardiogram(ECG),as a physiological signal that characterizes the condition of the heart,is of great significance to the detection and diagnosis of arrhythmia.However,due to the particularity of medical data,the cost of labeling is high.Many data are incompletely labeled,and the model cannot obtain enough training data.Due to individual differences,data collection sources and collection methods are different,ECG data inevitably has data distribution differences.At the same time,with the development of wearable devices for long-term monitoring,light-weight models are needed to meet the needs in real-time detection scenarios.In response to the above problems,this paper proposes an ECG signal classification method based on adversarial domain adaptation and an end-to-end ECG signal classification method based on domain adaptation to realize automatic identification and diagnosis of arrhythmia.The work content is as follows:(1)According to the characteristics of the ECG data set used,it is preprocessed to improve the data quality.In this paper,noise reduction processing based on band-pass filter and discrete wavelet transform is carried out,heartbeat segmentation and unified processing based on sliding window or fixed window are carried out,and data normalization operation based on Z-score method is carried out.Based on the time feature extraction of traditional methods and for the problem of unbalanced data set categories,the data is expanded through methods such as SMOTE algorithm and modified loss function to obtain the best balanced data set effect.Appropriate preprocessing steps will help the accuracy of feature extraction in subsequent models.(2)In view of the small number of high-quality labeled samples,the model cannot obtain enough training data;the differences in individual functions and collection equipment cause individual differences in sample data and different data distributions.An adaptive approach based on adversarial domains is proposed.This method solves the problem of less labeled training data by adversarial domain adaptive learning,improves the phenomenon that models cannot be reused directly due to different sample data distributions in different domains,and improves the efficiency of training classification models;simultaneously uses multiple feature extractors to extract multi-scale features combine time features with deep learning model extraction features to enhance the richness of features;finally,a highly applicable classification model is trained through these features.(3)Aiming at the requirements of wearable devices for the simplicity and real-time of classification algorithms and the problem of model reusability caused by different data distributions,an end-to-end ECG signal classification method based on domain adaptation is proposed.The main advantages of this method are: 1)The end-to-end method uses deep neural networks(DNN)for feature extraction and heartbeat classification.The end-to-end model avoids the need to manually process features and simplifies the signal classification process.2)Using domain adaptation ideas to improve the distribution difference between data sets,the model trained through the training set is better applied to the test set.3)A shallow model with only three convolutional network blocks and two fully connected modules is constructed,which has fast processing speed and meets the needs of real-time classification,and can be applied to real-time detection and classification of wearable devices.