Related Experiment Video
Updated: Nov 23, 2025
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
20.3K
Cross-recurrence analysis for pattern matching of multidimensional physiological signals.
Adam Meyers1, Mohammed Buqammaz1, Hui Yang1
1Complex Systems Monitoring, Modeling and Control Laboratory, The Pennsylvania State University, University Park, Pennsylvania 16801, USA.
Chaos (Woodbury, N.Y.)
|December 31, 2020
Summary
Cross-recurrence quantification analysis (CRQA) effectively clusters physiological signals. A novel method using CRQA dissimilarity measures accurately distinguishes healthy from diseased patients based on vectorcardiogram data.
Area of Science:
- Nonlinear dynamics
- Time series analysis
- Biomedical signal processing
Background:
- Cross-recurrence quantification analysis (CRQA) quantifies nonlinear interrelationships in time series.
- CRQA is valuable for data mining but underexplored for multidimensional physiological signals.
Purpose of the Study:
- To develop and evaluate a novel CRQA-based methodology for clustering multidimensional physiological signals.
- To assess the performance of CRQA statistics as dissimilarity measures for patient stratification.
Main Methods:
- Utilized CRQA statistics as dissimilarity measures between pairs of signals.
- Applied clustering to 3D spatiotemporal vectorcardiogram (VCG) signals from healthy and diseased patients.
- Evaluated Lmax and a novel measure, Rτmax, for their clustering performance.
Main Results:
- The Lmax measure achieved clustering that closely matched ground truth patient diagnoses.
- The proposed Rτmax measure demonstrated superior performance in matching diagnoses after signal rescaling.
- CRQA effectively identified patterns in complex spatiotemporal VCG data.
Conclusions:
- CRQA-based dissimilarity measures offer a powerful approach for clustering physiological time series.
- The Lmax and Rτmax metrics show promise for non-invasive patient diagnosis and stratification.
- This methodology advances the application of CRQA in biomedical signal analysis.
