Multi-Label ECG Signal Classification Based on Ensemble Classifier

Zhanquan Sun,Chao Yan,Yangyang Zhao,Chaoli Wang

doi:10.1109/access.2020.3004908

Zhanquan Sun, Chao Yan + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3004908

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 29	License type: CC BY 4.0

Affiliation: University of Shanghai for Science and Technology

Abstract

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.

Highlights

Electrocardiogram (ECG) can measure and record the electrical activities of the heart, which has been widely applied in the diagnosis of all kinds of cardiovascular diseases because that it is effective, simple, non-invasive and low cost [1]
Cardiac diseases auto identification based on ECG signals has been widely studied
For resolving the practical multi-label ECG classification problems, an ensemble classifier is proposed in this paper

Summary

INTRODUCTION

Electrocardiogram (ECG) can measure and record the electrical activities of the heart, which has been widely applied in the diagnosis of all kinds of cardiovascular diseases because that it is effective, simple, non-invasive and low cost [1]. Few research works related to multi-label ECG classification have been done. Many multi-label classification (MLC) methods have been proposed. Z. Sun et al.: Multi-Label ECG Signal Classification Based on Ensemble Classifier. Some efficient algorithm adoption methods have been proposed, such as multi-label kNN (MLkNN), multi-label SVM (ML-SVM) and so on These approaches differ from one another in their capability to capture the intrinsic properties, such as label correlation, local invariance and so on. Based on the extracted ECG features, different classifiers are adopted to perform ECG signal auto classification. For improving the classification performance, an ensemble multi-label classifier is proposed to realize high performance ECG signal classification.

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