Cardiovascular disease (CVD) is considered a significant public health concern around the world. Automated early diagnostic tools for CVDs can provide substantial benefits, especially in low-resource countries. In this study, we propose a time-domain Hilbert envelope feature (HEF) extraction scheme that can effectively distinguish among different cardiac anomalies from heart sounds even in highly noisy recordings. The method is motivated by how a cardiologist listens to the heart murmur configuration, e.g., the intensity of the heart sound envelope over a cardiac cycle. The proposed feature is invariant to the heart rate, the position of the first and second heart sounds, and robust in extracting the murmur configuration pattern in the presence of respiratory noise. Experimental evaluations are performed compared to two different state-of-the-art methods in the presence of respiratory noise with signal-to-noise ratio (SNR) values ranging from 0-15dB. The proposed HEF, fused with standard acoustic and Resnet features, yields an average accuracy, sensitivity, specificity, and F1-score of, 94.78%(±2.63), 87.48%(±6.07), 96.87%(±1.51) and 87.47%(±5.94), respectively, while using a random forest (RF) classifier. Compared to the best-performing baseline model, this feature-fusion scheme provides a significant performance improvement (p < 0.05), notably achieving an absolute improvement of 6.16% in averaged sensitivity. In the case of 0dB SNR, the proposed feature alone provides a 9.2% absolute improvement in sensitivity compared to the top baseline system demonstrating the robustness of the HEF. The developed methods can significantly impact computer-aided auscultation (CAA) systems when deployed in noisy conditions, especially in low-resource settings.