Investigating Phase Space Reconstruction of ECG for Prediction of Malignant Ventricular Arrhythmia

Abstract

Prediction of malignant ventricular arrhythmia is imperative to enable early diagnosis and prevent sudden cardiac death. There are a wide variety of methods employed in previous works with box counting of phase space reconstruction diagrams achieving the highest performance. However, there is no follow-up work investigating or improving this method. This work is performed to objectively assess the feasibility of box counting technique for prediction of malignant ventricular arrhythmia and to validate the prediction technique across larger data sets, including the widely acceptable MIT-BIH database. Box counting using different windowing methods, data representation methods of phase portrait and testing database are investigated for performance and versatility. By using windows of RR segments and linking phase portraits, this modified box counting method has higher resistance to signal noises, which are common in the electrocardiogram. It is verified using four Physionet databases (CUDB, SDDB, PTBDB and NSRDB) which contain either arrhythmic or control records. High prediction accuracy of 94.12%, sensitivity of 88.46% and specificity of 100% are achieved by the coefficient of variation derived from this method. This technique is proven useful in predicting malignant ventricular arrhythmia and its implementation is envisaged to enable early detection and diagnosis.