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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

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Simultaneous optimized orthogonal matching pursuit with application to ECG compression.

Laura Rebollo-Neira¹

¹Department of Applied Mathematics and Data Science, Aston University, Birmingham, United Kingdom.

|June 11, 2025

Summary

A new greedy strategy, Simultaneous Optimized Orthogonal Matching Pursuit, efficiently approximates similar signals. This approach significantly improves electrocardiogram compression compared to existing methods.

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Area of Science:

Signal Processing
Machine Learning
Biomedical Engineering

Background:

Approximating sets of similar signals is crucial in various fields.
Existing methods like Optimized Orthogonal Matching Pursuit have limitations in handling multiple signals simultaneously.
Efficient signal approximation is key for data compression and analysis.

Purpose of the Study:

To propose a novel greedy pursuit strategy for approximating a set of similar signals.
To extend existing methods to select a common subspace for approximating multiple signals.
To evaluate the effectiveness of the proposed method for signal compression, specifically for electrocardiograms.

Main Methods:

A greedy pursuit strategy is developed, extending Optimized Orthogonal Matching Pursuit.
The method, Simultaneous Optimized Orthogonal Matching Pursuit (SOOMP), iteratively minimizes the mean square error norm for all signals.
The strategy selects a common basis subspace for approximating the entire set of signals.

Main Results:

The proposed Simultaneous Optimized Orthogonal Matching Pursuit (SOOMP) method demonstrates stepwise optimality.
Application to electrocardiogram (ECG) compression on the MIT-BIH Arrhythmia dataset shows significant gains.
SOOMP outperforms other transformation-based compression techniques for ECG data.

Conclusions:

Simultaneous Optimized Orthogonal Matching Pursuit (SOOMP) offers an effective approach for approximating sets of similar signals.
The method provides superior performance in electrocardiogram compression compared to existing techniques.
SOOMP has potential applications in efficient signal processing and data reduction.