Analysis of long-term electrocardiographic data in a rabbit model of heart failure

The goal of this research is to explore techniques with which long-term physiologic time-series data can be analyzed, so that relevant changes in physiological signals, particularly the electrocardiogram signal, can be captured, processed, quantified and stored. A new experimental model was developed such that the electrocardiogram can be monitored continuously over thirteen weeks. Cardiotoxicity was progressively induced with doxorubicin in a rabbit model, and electrocardiographic progressions from normal state to diseased state were continuously tracked. Automated methods for analyzing the data were developed to manage and control the extensive electrocardiogram dataset. A significant challenge to this work is the sheer mass of data. This experiment generated 180 megabytes per day per rabbit, totaling around 66 gigabytes for the entire study. Classical ECG parameters significant for the evaluation of heart rate variability were calculated by computer for the entire period of the recordings, and visualized with several different methods.