Bimodal Masked Autoencoders with internal representation connections for electrocardiogram classification

Abstract

Time series self-supervised methods have been widely used, with electrocardiogram (ECG) classification tasks also reaping their benefits. One mainstream paradigm is masked data modeling, which leverages the visible part of data to reconstruct the masked part, aiding in acquiring useful representations for downstream tasks. However, traditional approach predominantly attends to time domain information and places excessive demands on the encoder for reconstruction, thereby hurting model’s discriminative ability. In this paper, we present Bimodal Masked autoencoders with Internal Representation Connections (BMIRC) for ECG classification. On the one hand, BMIRC integrates the frequency spectrum of ECG into the masked pre-training process, enhancing the model’s comprehensive understanding of the ECG. On the other hand, it establishes internal representation connections (IRC) from the encoder to the decoder, which offers the decoder various levels of information to aid in reconstruction, thereby allowing the encoder to focus on modeling discriminative representations. We conduct comprehensive experiments across three distinct ECG datasets to validate the effectiveness of BMIRC. Experimental results demonstrate that BMIRC surpasses the competitive baselines across the majority of scenarios, encompassing both intra-domain (pre-training and fine-tuning on the same dataset) and cross-domain (pre-training and fine-tuning on different datasets) settings. The code is publicly available at https://github.com/Envy-Clouds/BMIRC.