Supervised threshold-based heart sound classification algorithm.

Journal: Physiological measurement
Published Date: Nov 30, 2018

Abstract

OBJECTIVE: Deep classification networks have been one of the predominant methods for classifying heart sound recordings. To satisfy their demand for sample size, the most commonly used method for data augmentation is that which divides each heart sound instance into a number of segments, with each segment labelled as the same category as its origin and used as a new sample for training or forecasting. However, performing this poses a crucial issue as to how to determine the category of a predicted heart sound instance from its segments' prediction results.

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.
  • Zuyuan Yang
    School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address: yangzuyuan@aliyun.com.
  • Jun Lu
    School of Acupuncture-moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, 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.

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