ECG Classification Based on Non-cardiology Feature

Abstract

As for ECG auto-diagnosis, Classification accuracy is a vital factor for providing diagnosis decision support in remote ECG diagnosis. The final accuracy depends on ECG preprocessing process, feature extraction, feature selection and classification. However, different heart diseases are with different ECG wave shapes, in addition, there is large numbers of heart diseases, so it is hard to accurately extract cardiology features from diverse ECG wave forms. Also the extracted cardiology features are always with large error which to some extent influence the classification accuracy. To deal with these problems, we propose a feature extraction method of PCA and ICA approach. We calculate a adaptive basis with ICA and PCA for the given disease type ECG and extract the coefficients in the respect of trained basis which will be used as the classification features combined with cardiology features. To prevent the dimension disaster problem brought by the additional ICA and PCA feature, a minimal redundancy maximal relevance feature selection method is adapted to reduce the dimension of feature vector. Experiment shows that our method can effectively exclude the influence of not accurate cardiology features and greatly improve the classification accuracy for heart diseases.