Group invariant pattern recognition
Pattern Recognition
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Efficient spline interpolation
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Pattern identification in dynamical systems via symbolic time series analysis
Pattern Recognition
Statistical models of reconstructed phase spaces for signal classification
IEEE Transactions on Signal Processing - Part I
A sequential procedure for individual identity verification using ECG
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Expert Systems with Applications: An International Journal
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An invariant pattern recognition framework for classification of phase space trajectories of nonlinear dynamical systems is presented. Using statistical shape theory, known external influences can be discriminated from true changes of the system. The external effects are modeled as a transformation group acting on the phase space, and variation of the trajectories not explained by the transformations is accounted for using principal component analysis. The approach suggested is highly adaptable to a wide range of situations and individual differences. The methodology presented is applied to detect abnormalities in electrocardiograms. Results based on measured data indicate that the model developed is resistant to the effects of respiration and body position changes, which are abundant in ambulatory conditions and cause significant morphological artifacts in the signal. The results also show that the detection of an artificially induced acute myocardial infarction is achieved with high performance. Due to its low computational complexity, the method developed can be implemented in real-time. The method developed also adapts to morphological changes caused by various heart conditions.