Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes

TL;DR

This paper presents an adaptive transfer learning algorithm for multi-channel event segmentation that is robust to data distribution shifts, enabling improved health monitoring from non-stationary temporal data.

Contribution

It introduces a novel unsupervised adaptive transfer learning approach combining multivariate HMM and FLDA for event segmentation in non-stationary data.

Findings

Outperforms existing event classification methods in simulations.

Extracts sleep/wake features predictive of infection and onset time.

Robustly handles temporal shifts in data distribution.

Abstract

$\textbf{Objective}$: To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. $\textbf{Methods}$: We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher's linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring $\textit{a priori}$ information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to a H1N1 influenza pathogen. $\textbf{Results}$: Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time. $\textbf{Conclusion}$: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring. $\textbf{Significance}$: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications.