Heart disease diagnostic graphical user interface using fractal dimension

Heart diseases are among the main causes of death in the world. Therefore, it is necessary to have proper methods to determine the cardiac condition of the patient. ECG signals of the heart being a self-similar object; can well be considered for fractal analysis. In this paper the Fractal Dimension method was used to distinguish and analyze three specific heart diseases namely Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC), and Atrial Fibrillation from the normal ECG signal. The ECG signals used were taken from three databases, the MIT-BIH Arrhythmia Database, the MIT-BIH Normal Sinus Rhythm Database, and the Intracardiac Atrial Fibrillation Database. Rescaled range method was used to determine the specific range of fractal dimension for each disease. The obtained range of fractal dimension for a healthy person was 1.73-1.81, for PVC patients was 1.34-1.44, for PAC patients was 1.49-1.69 and for Atrial Fibrillation patients was 1.11-1.30. A Graphical User Interface (GUI) was designed using MATLAB program to calculate the Fractal Dimension, to distinguish between the ECG signals of a healthy person and patients with the three specific heart diseases from the raw ECG data. The results showed that the methodological analysis does provide a significant clinical advantage, and matches the doctor's opinion.