Representation learning frameworks in unlabeled time series have been
proposed for medical signal processing. Despite the numerous excellent
progresses have been made in previous works, we observe the representation
extracted for the time series still does not generalize well. In this paper, we
present a Time series (medical signal) Representation Learning framework via
Spectrogram (TRLS) to get more informative representations. We transform the
input time-domain medical signals into spectrograms and design a time-frequency
encoder named Time Frequency RNN (TFRNN) to capture more robust multi-scale
representations from the augmented spectrograms. Our TRLS takes spectrogram as
input with two types of different data augmentations and maximizes the
similarity between positive ones, which effectively circumvents the problem of
designing negative samples. Our evaluation of four real-world medical signal
datasets focusing on medical signal classification shows that TRLS is superior
to the existing frameworks.

通过使用频谱图，本文提出了一种基于时间序列（医学信号）的表示学习框架（TRLS），它能够从输入的时域医学信号中提取更具信息量的表示，通过时间频率 RNN（TFRNN）捕捉来自增强频谱图的更可靠的多尺度表示，同时通过最大化正样本之间的相似性来规避设计负样本的问题，实证表明 TRLS 优于现有的框架。