Real-Time Cardiac Abnormality Monitoring and Nursing for Patient Using Electrocardiographic Signals

Abstract

Introduction Cardiovascular disease care is a critical clinical application that necessitates real-time monitoring models. Previous models required the use of multi-lead signals and could not be customized as needed. Traditional methods relied on manually designed supervised algorithms, based on empirical experience, to identify waveform abnormalities and classify diseases, and were incapable of monitoring and alerting abnormalities in individual waveforms. Methods This research reconstructed the vector model for arbitrary leads using the phase space time delay method, enabling the model to arbitrarily combine signals as needed while possessing adaptive denoising capabilities. After employing automatically constructed machine learning algorithms and designing for rapid convergence, the model can identify abnormalities in individual waveforms and classify diseases, as well as detect and alert on abnormal waveforms. Result Effective noise elimination was achieved, obtaining a higher degree of loss function fitting. Afterwards, the detail differences of the electrocardiogram signal were amplified using a single-lead three-dimensional model. A cropping algorithm was used to remove waveforms severely interfered by external factors. Then, automatic neural network recognition was used. The automatic network generation model was designed effectively for different data types. The accuracy of patient identification is 98.2%, and the accuracy for healthy patients is 99.2%. Conclusion The elastic wavelet neural network can automatically denoise. Through the three-dimensional model, the detailed changes of electrocardiogram signals of different diseases can be observed. The cropping algorithm effectively identified the interfered and destroyed waveforms. The automatic neural network is capable of carrying out disease type classification and patient identity classification.