Comparison Metrics for Multi-Step Biomedical Signal Prediction

Abstract

Prediction of changes in biomedical signals, such as vital signs, is useful for many clinical applications. Several signal prediction (forecasting) tools were developed, but their evaluation and applicability to a specific clinical use is context dependent. In this work, we propose a novel method for evaluation and comparison of vital sign predictors for intervention based clinical studies. Specifically, we study and compare nine deep learning and statistics based predictive models for multi-step prediction of bradycardia events in preterm infants, but the proposed method could be applied to other biomedical signals. Our results on testing sets with several days of vital sign recordings show that simple statistical predictors could outperform state-of-the-art deep learning architectures for low-dimensional signals.

Keywords: Deep learning, Biomedical Signal, Predictor Evaluation, Time Series Forecasting