Analysis and Prediction of Heart Diseases Using Inductive Logic and Image Segmentation

Abstract

The human heart’s electrical activity produces currents that radiate through the surrounding tissue to the skin. When electrodes are attached to the skin, they sense these electrical currents and transmit them to an electrocardiograph monitor. The currents are then transformed into waveforms that represent the heart’s depolarisation - repolarisation cycle. An electro cardiogram (ECG) shows the precise sequence of electrical events occurring in the cardiac cells throughout the process. It can be used to monitor phases of myocardial contraction and to identify rhythm and conduction disturbances. In this work an automated ECG analysis and prediction of the heart conditions are carried out using a continuous image segmentation phase using Inductive Logic Programming (ILP) system. ECG taken from the patients is fed to the system as input and it uses the background knowledge to predict the possible underlying heart disorders. A continuous image segmentation module runs through out the process which identifies the leads and graph distortions by eliminating the outliers. The extracted lead graphs are compared with the in store ILP system database and possible predictions are made. The system performance was automatically compared (by Brier score method) with the MIT-BIH ECG database (PhysioNet) and a prediction accuracy of 97.8% was obtained in lead-lead prediction.