Key result
A dictionary-based Gaussian decomposition approach for ECG traces provided compact representations and enhanced comparability to support machine learning-based diagnostics.
Why the study?
Accurate ECG representation with limited complexity and standardized comparability across different traces and subjects was needed to support machine learning-based diagnostics.
A novel dictionary-based Gaussian decomposition method for ECG traces enables compact representation and facilitates machine learning-based diagnostics.
An approach based on dictionary-based Gaussian decomposition of electrocardiogram (ECG) traces is presented and characterized, and its performance potential is demonstrated using traces from the MIT-BIH Arrythmia Database. A Gaussian model is employed to describe ECG morphology. Parameters are estimated using a dictionary-based approach, that is purposely designed to obtain accurate representations with limited complexity and ensure comparability among different traces and subjects. The standardized Gaussian dictionary allows compact representations, enhances comparability and provides the support for machine learning-based diagnostics of ECG traces. Data-oriented large-scale medical analyses of ECG data are made possible, allowing the investigation of elusive cardiac phenomena and personalized diagnostics.
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Galli et al. (2022) studied Arrhythmia. Dictionary-based Gaussian decomposition was evaluated on Accurate representations with limited complexity and comparability among different traces. A dictionary-based Gaussian decomposition approach for ECG traces provided compact representations and enhanced comparability to support machine learning-based diagnostics.
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