Intelligent Method For Data Analysis Of Electrocardiogram Monitoring System

As the first cause of death of non-communicable diseases in the world,cardiovascular disease has been a serious threat to human life and health.The remote ECG monitoring system plays an important role in the prevention,detection and treatment of cardiovascular diseases with its convenient and efficient diagnosis mode.However,how to achieve intelligent analysis of ECG signals with high efficiency and accuracy is the main challenge faced by remote ECG monitoring system.Traditional ECG analysis methods have problems such as low diagnostic efficiency,low accuracy,poor adaptability,weak generalization ability,and low level of intelligence,which cannot meet the analysis requirements for the diversity and variability of ECG signals.The Remote ECG monitoring system is a kind of CPS.It is the frontier research of ECG monitoring to enhance the system’s function by using artificial intelligence technology.Artificial intelligence(AI)technology is data-driven and does not require external data assumptions,which has strong capabilities of feature extraction generalization.AI provides powerful technical support and tools for promoting the development of new smart medical systems.Aiming at key tasks such as noise reduction,dielineation,arrhythmia classification in the remote ECG analysis process,this paper studies the application of AI technology in the intelligent analysis of ECG signals from the level of algorithm and model,thus improving the intelligent level of the remote ECG analysis process.The main work and academic contributions of this paper are as follows.(1)A method of noise reduction for ECG signals based on generative adversarial network(GAN)is proposed.Aiming at the problem that ECG signals collected in the remote environment contain various types of noise,this paper applies the distribution feature learning ability of the GAN model to the noise reduction study of the ECG signal based on the adversarial perspective,which helps to learn the complex distribution features of various noise,and realizes the intelligent denoising for multiple types of noise contained in ECG signals.Furthermore,this paper designs a new loss function to stabilize the training process of the GAN model and capture the important local and global features of ECG signals,so that the denoised ECG signal can retain its medical value without distortion.Experimental results based on MIT-BIH Arrhythmia database show that the proposed method is superior to other methods,and the SNR is improved by about 62% on average.The proposed method has good noise reduction ability and strong adaptability.(2)A deep learning method for ECG signal delineation based on encoder-decoder model is proposed.In view of the diversity and variability of ECG signals,this paper uses the encoder-decoder model with superior feature extraction capability to extract the hidden features in ECG signals and generate the corresponding rectangular wave to delineate both the onset and offset of the P wave,the QRS complex,and the T wave in ECG signals.Furthermore,this paper designs a knowledge encoding and alignment method to integrate different types of medical knowledge into the ECG delineation process,which improves the delineation performance of the model for ECG signals and the adaptability to different individual ECG signals.An evaluation conducted on the QT database demonstrates that the proposed method can obtain,on average,high performance with sensitivity of 99.62% and positive predictivity of 99.81%.The evaluation also shows that the method can obtain a sensitivity and a positive predictivity above 90% in most noisy cases.(3)An interpretable arrhythmia classification method with human-machine collaborative knowledge representation is proposed.Aiming at the problem of automatic recognition and diagnosis of multi-class arrhythmias,an interpretable arrhythmia classification method suitable for ECG signal characteristics is proposed.Aiming at the end-to-end “black box”problem of the deep classification model,a human-machine collaborative knowledge representation is designed to improve the interpretability of the model for automatic classification of arrhythmia.Combining supervised learning and unsupervised learning,the proposed approach designs a two-task learning method based on an Auto Encoder(AE)to encode ECG signals into human-machine collaborative knowledge representation.Aiming at the problem of poor interaction of the classification model,a human-in-the-loop(HIL)mechanism is established to allow human intervention with the inference of the neural network to improve the model’s interactive ability and classification performance.Experiments and evaluation on the MIT-BIH arrhythmia database demonstrate that our new approach not only can effectively classify arrhythmia while offering interpretability,but also can improve the interaction and classification accuracy by adjusting the hand-encoding knowledge with the HIL mechanism.The intelligent analysis method of ECG signals in the telemedicine environment proposed in this paper can effectively improve the work efficiency of doctors and alleviate the problems caused by the telemedicine environment.It is of great significance for the early prevention,detection and treatment of cardiovascular diseases,improving the intelligent level of the remote ECG monitoring system,and alleviating the lack of uneven medical resources and the imbalance of the medical system in our country.