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:
Tue, 09/13/2022 - 04:05
DOI:
10.21227/qw2a-8m11
License:
0
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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.

Comments

;mm

Submitted by Harini KR on Thu, 10/27/2022 - 02:14