Tackling Low-Resource ECG Classification with Self-supervised Learning

• Rafael da Costa Silva USP
• Diego Furtado Silva USP

Resumo

Cardiovascular disease is one of the leading causes of worldwide death and an annual burden for European and American economies. The primary source of data to identify heart abnormalities is the electrocardiogram (ECG). However, its identification requires significant human efforts. In many cases, traditional rule-based diagnosis is inefficient due to the data heterogeneity, motivating the use of deep neural network approaches. However, ECG data varies significantly between equipment, including the sampling rate. Self-supervised Representation Learning (SSL) has gained increasing attention due to its generalization capacity in scenarios with scarce availability of a large volume of labeled data. In this work, we investigate using SSL methods in a cross-dataset scenario. We raised different research questions supported by the assumption that high-resource data, with a large sampling rate and volume of records, is helpful for training models in an SSL manner to classify ECG in low-resource datasets. Besides, we evaluate different resampling methods to match the sampling rate between datasets, including a novel upsampler based on ESPCN. Our experimental evaluation shows that using the state-of-the-art SSL models TS-TCC and TS2Vec can improve the accuracy in the low-resource dataset. However, the best results were obtained using the same dataset in both pre-training and downstream task steps. These results indicate the need to develop more suitable pretext tasks for cross-dataset scenarios, which may use our experimental analysis to build new ideas.