Towards Domain Invariant Heart Sound Abnormality Detection Using Learnable Filterbanks.
Journal:
IEEE journal of biomedical and health informatics
Published Date:
Jan 31, 2020
Abstract
OBJECTIVE: Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these systems is affected by numerous factors including the stethoscope/sensor, environment, and data collection protocol. This article studies the adverse effect of domain variability on heart sound abnormality detection and develops strategies to address this problem.