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Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes
- Publication Year :
- 2020
- Publisher :
- arXiv, 2020.
-
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.
- Subjects :
- Signal Processing (eess.SP)
Multivariate statistics
Technology
Covariate shift
Computer science
domain adaptation
0206 medical engineering
Feature extraction
digital health
Biomedical Engineering
02 engineering and technology
CLASSIFICATION
Engineering
Influenza A Virus, H1N1 Subtype
Discriminative model
0903 Biomedical Engineering
HIDDEN MARKOV MODEL
Outcome Assessment, Health Care
0801 Artificial Intelligence and Image Processing
FOS: Electrical engineering, electronic engineering, information engineering
Humans
Segmentation
early detection of viral infection
Electrical Engineering and Systems Science - Signal Processing
Hidden Markov model
Engineering, Biomedical
Event (probability theory)
human viral challenge study
Science & Technology
Adaptive algorithm
business.industry
wearable sensors
Pattern recognition
Signal Processing, Computer-Assisted
PERFORMANCE
Linear discriminant analysis
020601 biomedical engineering
SLEEP
Temporal database
0906 Electrical and Electronic Engineering
Artificial intelligence
business
Algorithms
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....fa7cae609a43c9d11cfafcdcb1bdf4e6
- Full Text :
- https://doi.org/10.48550/arxiv.2008.09215