Heart sound classification using the SNMFNet classifier.

Journal: Physiological measurement
Published Date: Oct 30, 2019

Abstract

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.

Authors

  • Wei Han
    Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, The Key Laboratory of New Drug Pharmacology and Toxicology, Ministry of Education, Collaborative Innovation Center of Hebei Province for Mechanism, Diagnosis and Treatment of Neuropsychiatric Diseases, Hebei Medical University, Shijiazhuang, Hebei, China.
  • Shengli Xie
    School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address: shlxie@gdut.edu.cn.
  • Zuyuan Yang
    School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address: yangzuyuan@aliyun.com.
  • Songbin Zhou
    Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, Guangdong Province, China.
  • Haonan Huang

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