Fractal Dimension-based Methodology for Sudden Cardiac Death Prediction

Sudden cardiac death (SCD) is a disease that can be regarded as one of the principal death causes in the society. Hence, if the SCD event can be predicted in the earliest stage possible, it will allow saving people lives because they will receive timely medical procedures. In this paper, a methodology to predict SCD of an automatic manner using ECG signals, fractal dimension (FD), and artificial neural networks is presented. Three FD methods are investigated, Higuchi fractal dimension, Box dimension, and Katz fractal dimension. The effectiveness of the proposed methodology for predicting a SCD event is demonstrated using a database of 38 patients, 20 with SCD and 18 normal, provided by MIT-BIH (Boston's Beth Israel Hospital). The results show an accuracy of 91.4% 14 minutes prior to SCD event.