Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes.

Journal Article (Journal Article)

OBJECTIVE: To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. 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 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 an H1N1 influenza pathogen. 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. 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. SIGNIFICANCE: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications.

Full Text

Duke Authors

Cited Authors

  • She, X; Zhai, Y; Henao, R; Woods, CW; Chiu, C; Ginsburg, GS; Song, PXK; Hero, AO

Published Date

  • August 2021

Published In

Volume / Issue

  • 68 / 8

Start / End Page

  • 2377 - 2388

PubMed ID

  • 33201806

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

Digital Object Identifier (DOI)

  • 10.1109/TBME.2020.3038652

Language

  • eng

Conference Location

  • United States