[An algorithm study on telecardiogram diagnosis based on multivariate autoregressive model and two-lead ECG signals].
OBJECTIVE
In view of the time delay caused by reconstruction of signals at remote sites, a direct classification method with high accuracy suitable for telediagnosis of electrocardiogram (ECG) signals is studied.


METHODS
The data for analysis and classification was obtained from MIT-BIH database, including 300 samples each of normal sinus rhythm (NSR), atria premature contraction (APC), premature ventricular contraction (PVC), ventricular tachycardia (VT), ventricular fibrillation (VF) and superventricular tachycardia (SVT). An multivariate autoregressive (MAR) model based technique that could combine the signals of two ECG leads was presented to classify the ECGs directly, including MAR modeling performed on ECGs, and quadratic discrimination function (QDF) based classification by using MAR coefficients and K-L MAR coefficients.


RESULTS
Besides quick and convenient diagnosis, the accuracy of the proposed classification algorithm was as high as 98.3%-100%.


CONCLUSIONS
The MAR modeling based technique is suitable for telecardiogram diagnosis. Comparing with single-lead ECGs, better classification results can be obtained through the combination of two-lead ECG signals.
Ge
Ding-fei
DF
Department of Information & Electrical Engineering, Zhe-jiang University of Science and Technology, Hangzhou, China. gedingfei@hotmail.com
Shao
Yu-quan
YQ
Jiang
Hui-zhong
HZ
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography; D015233:Models, Statistical; D015999:Multivariate Analysis; D012044:Regression Analysis; D019114:Remote Consultation; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D017216:Telemedicine