A novel ensemble multi-label classification model improved overall classification performance for analyzing clinical ECG signal datasets.
An ensemble multi-label classification model improves the performance of automatic ECG signal classification for multiple concurrent cardiovascular diseases.
Electrocardiogram (ECG) has been proved to be the most common and effective approach to investigate the cardiovascular disease because that it is simple, non-invasive and low cost. ECG signal automatic classification is a popular research topic and some efficient research work has been done on it. Most of current research work focuses on single ECG label classification, i.e. one ECG signal record corresponds to one label. In practice, one ECG signal usually embraces several cardiovascular diseases at the same time. It is more important to study multi-label ECG signal classification. To our knowledge, few research works have been done on the research topic. To resolve the multi-label ECG signal classification problems, we propose a novel ensemble multi-label classification model in this paper. The model combines several multi-label classification methods to generate a high performance classifier. Mutual information is used to measure the weight of each classifier. At last the ensemble multi-label classification model is used to analyze a clinic ECG signal dataset. The analysis results show that the overall classification performance is improved. It provides a feasible analysis method for multi-label ECG signal automatic classification.
Sun et al. (Wed,) conducted a other in Cardiovascular disease. Ensemble multi-label classification model was evaluated on Overall classification performance. A novel ensemble multi-label classification model improved overall classification performance for analyzing clinical ECG signal datasets.