Sparse Classifiers For Automated Heart Wall Motion Abnormality Detection

Coronary Heart Disease is the single leading cause of death world-wide, with lack of early diagnosis being a key contributory factor This disease can be diagnosed by measuring and scoring regional motion of the heart wall in echocardiography images of the left ventricle (LV) of the heart. We describe a completely automated and robust technique that detects diseased hearts based on automatic detection and tracking of the endocardium and epicardium of the LV We describe a novel feature selection technique based on mathematical programming that results in a robust hyperplane-based classifier The classifier depends only on a small subset of numerical feature extracted from dual-contours tracked through time. We verify the robustness of our system on echocardiograms collected in routine clinical practice at one hospital, both with the standard cross-validation analysis, and then on a held-out set of completely unseen echocardiography images.