Real-time electrocardiogramclassification method based on random projection

Abstract

The invention requests to protect a real-time electrocardiogram classification method based on random projection, and aims to solve the problems of data acquisition and computation and power consumption transmission with which a remote electrocardiogram monitoring system faces and the problem that electrocardiogram cannot be classified in real time. Five types of heartbeat are classified into normal pulsation, atrial premature beat, ventricular premature beat, left bundle branch block and right bundle branch block. The method comprises the following steps: (1) data preprocessing; (2) characteristic extraction: on the basis of a compressed sensing principle, compressing data, calculating an RR interval and an RR weight, and splicing characteristic vectors to form second characteristics; (3) classification: dividing secondary characteristic data into training data and test data, wherein the training data and the test data are independently used for modeling ant testing; and (4) decision classification: carrying out multiple-lead classification result data fusion. The step of data preprocessing comprises the following specific steps: 1) filtering an electrocardiosignal, and removing interference; 2) carrying out waveform detection and segmentation; 3) carrying out data standardization. The electrocardiogram data classification method provided by the invention is accurate in a classification result, and improves data processing capability.

Description

technical field [0001] The invention relates to the interdisciplinary field of biomedicine and computer science, in particular to a method for classifying dynamic electrocardiographic data. Background technique [0002] The latest statistics from the World Health Organization (WHO) show that heart disease has been the leading cause of death. Although the traditional ECG monitoring system can effectively reduce the mortality of heart disease patients, it cannot monitor the ECG signals of patients in real time because it cannot be monitored remotely. However, heart disease is hidden and latent, and it is difficult to show it on the ECG when it is not onset. The onset is short-lived, often lasting only tens of seconds. , ECG returned to normal. Doctors cannot diagnose patients in time and delay treatment of diseases. Therefore, some patients need to carry a 24-hour Holter to collect ECG data 24 hours a day. Moreover, with the current development of information science and m...

AI Technical Summary

Problems solved by technology

[0004] The present invention is to provide a method for classifying ECG signals to solve the contradiction between the complexity and accuracy of feature extraction and the problem that it cannot be classified in real time

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