Heart sound classification using the SNMFNet classifier

Objective : Heart sound classification still suffers from the challenges involved in achieving high
accuracy in the case of small samples. Dimension reduction attempts to extract low-dimensional
features with more discriminability from high-dimensional spaces or raw data, and is popular in
learning predictive models that target small sample problems. However, it can also be harmful to
classification, because any reduction has the potential to lose information containing category
attributes. Approach : For this, a novel SNMFNet classifier is designed to directly associate the
dimension reduction process with the classification procedure used for promoting feature dimension
reduction to follow the approach that is beneficial for classification, thus making the
low-dimensional features more distinguishable and addressing the challenge facing heart sound
classification in small samples. Main results : We evaluated our method and representative methods
using a publ...

Source: Physiological Measurement - October 29, 2019 Category: Physiology Authors: Wei Han, Shengli Xie, Zuyuan Yang, Songbin Zhou and Haonan Huang Source Type: research