Research On Atrial Fibrillation Recognition Algorithm Based On Machine Learning

Electrocardiogram(ECG)is one of the most commonly used clinical tests and it plays a crucial role in determining various cardiovascular diseases.With the rise of wearable and mobile ECG monitoring,a large amount of real-time ECG data is concentrated in the data center through the network.According to the doctor's manual interpretation method,it is not possible.The automatic interpretation of the ECG and the ECG event warning have become imminent.With the current use of various medical diagnostic systems,ECG monitoring algorithms also face the requirements of accuracy,accuracy,generalization ability and compatibility.After the algorithm is completed,it takes a lot of time to verify the performance of the algorithm.The realization of the high-quality evaluation of the algorithm has become a major problem faced by many universities,scientific research institutions and companies.How to effectively judge the performance of the algorithm,the current lack of a unified standard,but also the lack of an effective tool to save costs.This article addresses this issue and provides a standardized platform for ECG signal research.ECG standardized research platform has the characteristics of simple,reliable,and easy to expand.The platform displays electrocardiograms in a graphical manner.The source of the data can either directly select a standard database or a user's database that conforms to the universal format.The platform implements automatic loading and execution of MATLAB program files and automatic display of algorithm results.The user can choose to modify the inappropriate mark according to the display of the figure,the observation of the partial shape.The algorithm results are calculated on the accuracy,time domain,frequency domain,and nonlinear domain,giving the user a clear understanding.At the same time,a variety of index fusion algorithms are embedded to evaluate the signal quality and help users evaluate their own performance.Another important feature of this article is the use of embedded programs to perform ventricular premature beats on ECG waveforms to help users analyze ECG waveforms.At the same time,I hope to study the performance of the algorithm under different classification conditions.Users can also use their own classification algorithm to replace the existing classification algorithms of the current platform.At the same time,the standardization platform for ECG signal research is an extensible platform that can target other physiological signals as well as other required evaluation algorithms.Based on this,the user can conduct more in-depth real-time ECG detection research,dynamic electrocardiography and other physiological electrical signal analysis,and has important clinical development significance.