Wave2Vec: Learning Deep Representations for Biosignals

Abstract

Time series data mining has gained increasing attention in health domain. Recently, researchers attempt to employ Natural Language Processing (NLP) to health data mining, in order to learn proper representations of discrete medical concepts from Electronic Health Records (EHRs). However, existing models do not take continuous physiological records into account, which are naturally existed in EHRs. The major challenges for this task are to model non-obvious representations from observed high dimensional biosignals, and to interpret the learned features. To address these issues, we propose Wave2Vec, an end-to-end deep learning model, to bridge the gap between biosignal processing and language modeling. Wave2Vec jointly learns both inherent and embedding representations of biosignals at the same time. To evaluate the performance of our model in clinical task, we carry out experiments on two real world benchmark biosignal datasets. Experimental results show that the proposed Wave2Vec model outperforms the six feature leaning baselines in biosignal processing.

Ähnliche Arbeiten