CLINICALLY RELEVANT UNSUPERVISED ONLINE REP-RESENTATION LEARNING OF ICU WAVEFORMS

Abstract

Univariate high-frequency time series with real-time state changes are prominent in medical, economic and environmental applications. In the intensive care unit, for example, changes and intracranial pressure waveforms can indicate whether a patient is developing decreased blood perfusion to the brain during a stroke. However, most representation learning to resolve states is conducted in an offline, batch-dependent manner. In high frequency time-series, high intra-state and inter-sample variability makes offline, batch-dependent learning a relatively difficult task. Hence, we propose Spatial Resolved Temporal Networks (SpaRTeN), a novel composite deep learning model for online, unsupervised representation learning through a spatially constrained latent space. SpaRTeN maps waveforms to states, and learns time-dependent representations of each state. Our key contribution is that we generate clinically relevant representations of each state for intracranial pressure waveforms.

1. INTRODUCTION

Univariate high-frequency time series arise in several domains including economics, medicine, and environmental studies Thorsen-Meyer et al. (2020) ; Shakeel & Srivastava (2021) ; Yao et al. (2019) . High-frequency time series often exhibit different states. For example, in a patient suffering from a stroke, an intracranial pressure (ICP) waveform normally flucturates somewhat. However, a transition to a state where it is persistently elevated may lead to blindness and other neurological problems Mollan et al. (2016) . Early detection of this state transition may enable physicians to intervene appropriately for better outcomesLlwyd et al. (2022) . 2018). However, in time series, dimensionality is proportional to series length. As a result, state determination requires encoding time series into fixed-length vectors, followed by clustering algorithms like kmeans. These methods can capture long-range dependencies but rely on non-differentiable function fitting. Also, these methods are often offline, in that they learn from an entire training dataset at once, before being evaluated and deployed. This can be problematic, especially in the context of dataset shift or high inter-sample variability. Every time a new batch of data is received, the entire model needs to be retrained. High-frequency time series data like waveforms are often encountered in scenarios more suitable for online learning, wherein a learner attempts to tackle some predictive task by learning a sequence of data in the order they are received Hoi et al. (2021) . Extraction of states or state-transitions from a high-frequency time series requires online unsupervised representation learning, a relatively understudied field. Fuzzy neural networks create a set of modifiable rules Luo et al. ( 2019), but successive rule changes makes state inference relatively volatile and inconclusive. Another example of the state-of-the-art time series forecasting method is



Many algorithms like shapelets, hierarchical latent factor models, hidden Markov Model-like methods, change point and anomaly detection techniques, and N-Beats are dedicated towards disentangling time series into their respective subcomponents Li et al. (2021); Grabocka et al. (2015); Oreshkin et al. (2019a); Blazquez-Garcia & Conde (2022); Aminikhanghahi & Cook (2017); Van Den Oord & Vinyals (2017) but few are dedicated towards disentangling states within a single time series Franceschi et al. (2019) or predicting future state transitions. For high-dimensional datasets, unsupervised methods like t-SNE, UMAP, and SOMs can be used to project samples into lower dimensions with spatial relationships Van der Maaten & Hinton (2008); McInnes et al. (

