Pattern recognition method based on portable sensor has broad application prospects in electrocardiograph（ ECG） diagnostic areas

there are a lot of research and application in arrhythmia

myocardial infarction

ventricular hypertrophy recognition

but there is no relevant research about pattern recognition method in atrial cardiac hypertrophy. Atrial hypertrophy ECG data is insufficient

which cause significant obstacles for research

and part of classifier can’t adapt to the classification of small sample case. Studies of small sample training is focused on

compare different classification methods

it shows great application potential of support vector machine（ SVM） based on statistical pattern recognition in atrial hypertrophy. In addition

as different individuals

atrial cardiac hypertrophy is different

in actual application environment

SVM has generalization problem

and there is medical risk of category misclassified. Aiming at category misclassification cases

using classifier fusion method to improve classifier

adding rejection region classifier at the output end of SVM classifier（ SVM-R） is proposed. Experimental results show that SVM-R has higher classification accuracy and diagnostic confidence.