Code for Intra-subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Speech Data of Parkinson's Disease
- Citation Author(s):
-
Yongming Li (Department of Communication Engineering, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China)Yiwen Wang (School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China)Fan Li (School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China)Xiaoheng Zhang (School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China)Ping Wang (School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China)
- Submitted by:
- Yongming Li
- Last updated:
- DOI:
- 10.21227/qw2a-8m11
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Abstract
The code contains two public Parkinson's speech datasets, a self-collected Parkinson's speech dataset, some common public datasets. It also contains the MATLAB code for Intra-subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Speech Data of Parkinson's Disease( JTEHM-00114-2022).
Instructions:
Sakar, MaxLittle and the self-collected PD speech dataset (named SelfData) are used as the target datasets for the validation of the proposed algorithm. These three datasets are highly representative, Sakar and MaxLittle are mainly used for diagnosis, and SelfData is mainly used for efficacy assessment. Sakar and MaxLittle are public PD speech datasets based on European and American patients, and SelfData is a self-harvested PD speech dataset based on Chinese patients. Thus, these three datasets cover a representative range of patients from distinct applications and different regions.
