Wave2Vec: Deep representation learning for clinical temporal data

Representation learning for time series has gained increasing attention in healthcare domain. The recent advancement in semantic learning allows researcher to learn meaningful deep representations of clinical medical concepts from Electronic Health Records (EHRs). However, existing models cannot deal with continuous physiological records, which are often included in EHRs. The major challenges for this task are to model non-obvious representations from observed high-resolution biosignals, and to interpret the learned features. To address these issues, we propose Wave2Vec, an end-to-end deep representation learning model, to bridge the gap between biosignal processing and semantic learning. Wave2Vec not only jointly learns both inherent and temporal representations of biosignals, but also allows us to interpret the learned representations reasonably over time. We propose two embedding mechanisms to capture the temporal knowledge within signals, and discover latent knowledge from signals in time-frequency domain, namely component-based motifs. To validate the effectiveness of our model in clinical task, we carry out experiments on two real-world benchmark biosignal datasets. Experimental results demonstrate that the proposed Wave2Vec model outperforms six feature learning baselines in biosignal processing. Analytical results show that the proposed model can incorporate both motif co-occurrence information and time series information of biosignals, and hence provides clinically meaningful interpretation.