TRLS: A Time Series Representation Learning Framework via Spectrogram
  for Medical Signal Processing
Luyuan Xie Cong Li Xin Zhang Shengfang Zhai Yuejian Fang Qingni Shen Zhonghai Wu
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.