A 12-Lead ECG signal correlation analysis in multiple domains

This paper is devoted to correlation analysis of 12-lead electrocardiogram (ECG) signals, crucial for understanding cardiac function from multiple leads. Accurate synchronization via QRS detection facilitates the comparison of signals in time and discrete orthogonal transformation (DOT) domains. However, noise, muscle interference, and other signal distortions challenge QRS detection accuracy. We propose an enhanced method for Pan-Tomkin's detector, based on the statistical parameters of multi-lead ECG, to adjust QRS detections. This ensures a uniform number of QRS detections across all leads, resulting in synchronized correlation analysis. Correlation analysis, conducted on the Lobachewsky University ECG database, is evaluated in the time and sparse domains of discrete cosine transform and discrete wavelet transform. The study aims to reveal lead relationships in multiple domains, and their sparsity which can pave the way for usage of compression techniques.