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Representation learning frameworks in unlabeled time series have beenproposed for medical signal processing. Despite the numerous excellentprogresses have been made in previous works, we observe the representationextracted for the time series still does not generalize well. In this paper, wepresent a Time series (medical signal) Representation Learning framework viaSpectrogram (TRLS) to get more informative representations. We transform theinput time-domain medical signals into spectrograms and design a time-frequencyencoder named Time Frequency RNN (TFRNN) to capture more robust multi-scalerepresentations from the augmented spectrograms. Our TRLS takes spectrogram asinput with two types of different data augmentations and maximizes thesimilarity between positive ones, which effectively circumvents the problem ofdesigning negative samples. Our evaluation of four real-world medical signaldatasets focusing on medical signal classification shows that TRLS is superiorto the existing frameworks.

TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing