An ECG Classification Algorithm Based on Heart Rate and Deep Learning
Li Huihui
Jin Linpeng
Abstract：Objective To study the electrocardiogram (ECG) normality vs.abnormality classification algorithm applicable to remote medical service system,physical examination center and clinical application.Methods First,the abnormal data was eliminated through the calculation of heart rate and the ECG with normal heart rate was selected.For the ECG with normal heart rate,normality vs.abnormality classification was carried out with lead convolution neural network (LCNN),and then the classification results were fused.Results The test of over 150,000 clinical records showed that the accuracy was 84.77％,the sensitivity was 85.19％ and the specificity was 84.45％.Conclusion The result is better than the literature.It can serve as a reference for the computer-aided analysis method in remote medical and physical examination center.
Keywords：telemedicineclassification algorithmelectrocardiogramheart ratelead convolution neural network
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)