Multi-Label Classification of Arrhythmia for Long-Term Electrocardiogram Signals With Feature Learning

Abstract

Arrhythmia is a kind of cardiovascular disease that seriously threats human health. Intelligent analysis of electrocardiogram (ECG) is an effective method for the early prevention and precise treatment to arrhythmia. In clinical ECG waveforms, it is common to see the multi-label phenomenon that one patient would be labeled with multiple types of arrhythmia. However, the current research is mainly to use the multiclass methods to solve the multi-label problem, ignoring the correlations between diseases and causing information loss. Therefore, this article aims: 1) to propose a multi-label feature selection method based on ECG (MS-ECG) and design an evaluation criterion of ECG features based on kernelized fuzzy rough sets so as to choose the optimal feature subset and optimize ECG feature space and 2) to propose the multi-label classification algorithm of arrhythmia based on ECG (MC-ECG) by establishing a multiobjective optimization model. This algorithm based on sparsity constraint explores the correlations between arrhythmia diseases and analyzes the mapping relationship between ECG features and arrhythmia diseases, so that one ECG signal would be automatically and accurately given multiple labels. Through sufficient experiments to prove the feasibility of our methods, we obtain the selected feature subset composed of 23 ECG features by MS-ECG. For the six evaluation criterions of MC-ECG, average precision is 0.8462, hamming loss is 0.1041, ranking loss is 0.1313, one-error is 0.2023, coverage is 0.4015, and micro-F1 is 0.6088. The outcome presents optimal to the current algorithms.