Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

Journal: Biomedical engineering online
Published Date: Nov 16, 2016

Abstract

BACKGROUND: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined.

Authors

  • He-Hua Zhang
    Institute of Surgery Research, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
  • Liuyang Yang
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
  • Yuchuan Liu
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
  • Pin Wang
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
  • Jun Yin
    Institute of Surgery Research, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
  • Yongming Li
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, National Center for Respiratory Medicine, Guangzhou, China.
  • Mingguo Qiu
    Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China.
  • Xueru Zhu
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
  • Fang Yan
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.

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