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

TL;DR

This paper introduces TRLS, a novel framework that transforms medical signals into spectrograms and uses a specialized encoder to learn more robust and generalizable representations for medical signal classification.

Contribution

The paper proposes a spectrogram-based representation learning framework with a new encoder and augmentation strategy, improving generalization in medical signal processing.

Findings

TRLS outperforms existing frameworks on four real-world datasets.

Spectrogram transformation enhances feature robustness.

The TFRNN encoder captures multi-scale time-frequency features.

Abstract

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.