Enhancement of the spatial resolution of ECG using multi-scale Linear Regression

Spatial resolution of ECG can be increased using the information available from a subset of standard 12-lead ECG. This is usually achieved by learning a model between the standard 12-lead and its reduced lead subset. Since ECG signal contains significant amount of diagnostic information, it is important to learn a model which preserves this information. In this work, a patient specific model is proposed which utilizes the inter lead correlation in the transformed domain. The model is learned over Wavelet domain using Linear Regression. Performance of the model is evaluated using standard distortion measures such as correlation coefficient and root mean square error along with wavelet energy based diagnostic distortion. An analysis is also performed over the derived signal to quantify the loss of diagnostic information. The results show that the proposed model performs better in preserving diagnostic information in comparison to the existing linear models.

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