Electrocardiogram (ECG) signal is one of the most effective sources of
information mainly employed for the diagnosis and prediction of cardiovascular
diseases (CVDs) connected with the abnormalities in heart rhythm. Clearly,
single modality ECG (i.e. time series) cannot convey its complete
characteristics, thus, exploiting both time and time-frequency modalities in
the form of time-series data and spectrogram is needed. Leveraging the
cutting-edge self-supervised learning (SSL) technique on unlabeled data, we
propose SSL-based multimodality ECG classification. Our proposed network
follows SSL learning paradigm and consists of two modules corresponding to
pre-stream task, and down-stream task, respectively. In the SSL-pre-stream
task, we utilize self-knowledge distillation (KD) techniques with no labeled
data, on various transformations and in both time and frequency domains. In the
down-stream task, which is trained on labeled data, we propose a gate fusion
mechanism to fuse information from this http URL evaluate the effectiveness
of our approach, ten-fold cross validation on the 12-lead PhysioNet 2020
dataset has been conducted.

本论文提出了基于自监督学习技术的多模态心电图分类方法，利用单模态 ECG 的时间序列和频谱信息，以及 SSL-pre-stream 任务和 down-stream 任务结合的方式实现。在评估这种方法的有效性时，使用 12 导联 PhysioNet 2020 数据集进行了十折交叉验证。