Classification of Valvular Regurgitation Using Echocardiography

Abstract

Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular heart disease for its ability to detect types of cardiac regurgitation. Regurgitation represents irregularities in cardiac function and the early detection of regurgitation is necessary to avoid invasive cardiovascular surgery. In this paper, we focussed on the classification of regurgitations from videographic echo images. Three different types of regurgitation are considered in this work, namely, aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR). From the echo images, texture features are extracted, and classification is performed using Random Forest (RF) classifier. Extraction of keyframe is performed from the video file using two approaches: a reference frame keyframe extraction technique and a redundant frame removal technique. To check the robustness of the model, we have considered both segmented and nonsegmented frames. Segmentation is carried out after keyframe extraction using the Level Set (LS) with Fuzzy C-means (FCM) approach. Performances are evaluated in terms of accuracy, precision, recall, and F1-score and compared for both reference frame and redundant frame extraction techniques. K-fold cross-validation is used to examine the performance of the model. The performance result shows that our proposed approach outperforms other state-of-art machine learning approaches in terms of accuracy, precision, recall, and F1-score.

Keywords

• videographic echo
• regurgitation
• level set
• random forest