Atrial Fibrillation Signal Processing And Community Heart Patient Monitoring System Is Developed

At present, multi-leads (12leads) ECG are chosen by most of AF signal extractionalgorithms in order to extract effective AF waves. However, it is not convenient forpatients’movements with a multi-lead ECG monitoring system. The Real-time andwireless Mobile has been the trend of ECG monitoring system for AF. The mainconcerns of the study here can be concluded extraction algorithm for single-lead ECG、classification of Paroxysmal AF and Persistent AF and development of remote ECGautomatic monitoring and diagnosis system.Although the traditional template matching method is for single-lead ECGextraction, it is less robust than blind source extraction algorithm, and may be affectedseverely by noise. In view of this, we put forth a new real-time algorithm for extractingAF from the single-lead ECG: Using non-stationary heartbeat series during AtrialFibrillation to extend dimension (segmentation), and then applying a blind sourceextraction algorithm to extract the effective AF signal. Experiment results show that thismethod can effectively extract AF signal from a single-lead ECG data. Therefore, it issuitable to apply this method to Wireless Monitoring System using single-lead ECG.Based on the research of AF signal extraction algorithm, we have studied theclassification of Paroxysmal Atrial and Persistent Atrial herein so as to obtain a deeperunderstanding of the spontaneous termination mechanism of AF which may help toimprove the therapy of Persistent Atrial. In view of this, we propose a new type ofclassification: Extracting AF signal from the single-lead ECG in accordance with theprincipal component analysis before computing the features of such AF signal based onwhich the Paroxysmal Atrialand Persistent Atrial can be classified with SVM techniquein the end, and for the first time, we have described the complexity of AF undulationwith characterization of complexity. The experimental results show that the generalaccuracy rate of the prediction is90%, and for the1,000random prediction experiments,the maximum accurate rate is92%, with an average accuracy rate of77.12%, whichproves that this method performs well in classification of the Paroxysmal Atrial andPersistent Atrial, and can serve as guidance to some extent when predicting the spontaneous termination of AF. With the progress of medical treatment reform,community-based medical has become the development trend of ECG monitoringsystem. In view of this, the research and development of remote ECG automaticmonitoring and diagnosis system has been of great significance. The main concerns ofthe study here can be concluded as follows: firstly, to detect QRS complexs and Twave.secondly, to display ECG waveform and communicate between client andserver.finally, to build database management module and the patient’s case managementsystem.