Visualisation of Long-Term ECG Signals Applied to Post-Intensive Care Patients

Abstract

A visualisation method for automatically clustering heart beats in single channel electrocardiography (ECG) signals was developed and applied to a dataset of post-intensive care patients who received long-term continuous monitoring. We first segmented the ECG signal into individual beats using an R-peak detection algorithm. A matrix was constructed by storing the segmented ECG beats in row-wise format. Singular value decomposition (SVD) was applied to remove sparse invalid detected R peaks, thus smoothing the matrix. Treating the matrix of ECG beat values as an image, an edge detection algorithm was applied, resulting in a binary matrix containing traces of heart beats with the contiguous and discontiguous components extracted. We considered each component to be a cluster of heart beats. This method was robust to signal noise by exploiting detected R peaks and ECG raw cycles represented in a matrix format for estimation of heart beats. The algorithm also eliminated the effect of underestimated R peaks in the estimation of heart beats, and minimised the effects of overestimated R peaks using the SVD algorithm. This method allows clusters of beats to be visualized, which may assist clinicians in estimating the components of long-term ECG signals.