Inter-patient ECG arrhythmia heartbeat classification based on unsupervised domain adaptation

Electrocardiography (ECG) arrhythmia heartbeat classification is essential for automatic cardiovascular diagnosis system. However, the enormous differences of ECG signals among individuals and high price of labeled data have brought huge challenges for current classification algorithms based on deep neural networks and prevented these models from achieving satisfactory performance on new data. In order to build a classification system with better adaptability, we propose a novel Domain-Adaptative ECG Arrhythmia Classification (DAEAC) model based on convolutional network and unsupervised domain adaptation (UDA). Based on observation of clustering characteristics of data, we present two original objective functions to enhance the inter-patient performance. A Cluster-Aligning loss is presented to align the distributions of training data and test data. Simultaneously, a Cluster-Maintaining loss is proposed to reinforce the discriminability and structural information of features. The proposed method requires no expert annotations but a short period of unsupervised data in new records to make deep models more adaptive. Extensive experimental results on three public databases demonstrate that our method achieves competitive performance with other state-of-the-arts on the detection of ventricular ectopic beats (V), supraventricular ectopic beats (S) and fusion beats (F). The cross-dataset experimental results also verify the great generalization capability. CO 2021 Elsevier B.V. All rights reserved.

Automated summary

A novel Domain-Adaptative ECG Arrhythmia Classification (DAEAC) model is proposed based on convolutional network and unsupervised domain adaptation to enhance inter-patient performance, achieving competitive detection results for ventricular ectopic beats, supraventricular ectopic beats, and fusion beats.