Abstract: Automatic electrocardiogram(ECG) abnormality detection is a multi-label classification problem,which is commonly solved by training a binary-relevance classifier for each abnormality.Due to the large number of abnormalities,the complex correlations between features and abnormalities,and those among different abnormalities,existing methods’ performance is not satis-fying.To make full use of the dependencies between features and abnormalities,this paper proposes a novel abnormality detection method based on label co-occurrence and feature local pertinence(LCFP).Firstly,we set up a consolidated feature space consisting of both the macro-features and micro-features based on the label co-occurrence and features’ pertineance.The macro-features are constructed with a clustering approach based on Dirichlet process mixture model(DPMM),thus distinguishing differentco-occurrence label sets.The micro-features are a subset of primitive features,which serves to distinguish between the labels in the same labelset.Next,we train a one-versus-all classifier which returns a relevance probability.Secondly,to make use of the diffe-rent correlation degrees among different abnormalities,we propose to differ the relevant labels from the irrelevant ones based on the sorting according to the probabilities given by the classifiers.In particular,we propose to exploit the Beta distribution to adaptively learn the anchor thresholds and correlation thresholds,thus determining the relevant labels of an instance.Our LCFP me-thod is a universal way to detect every possible ECG abnormalities,which effectively improves the detection accuracy.The results on two real datasets show that our method can achieves an improvement of 4% and 22.4%,respectively,in terms of F1,which proves the effectiveness of our method.

Key words: Electrocardiogram abnormality, Multi-label classification, Label co-occurrence, Dirichlet process mixture model, Beta distribution, Anchor thresholds