Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease / 生物医学工程学杂志
J. biomed. eng
; Sheng wu yi xue gong cheng xue za zhi;(6): 942-948, 2018.
Article
de Zh
| WPRIM
| ID: wpr-771102
Bibliothèque responsable:
WPRO
ABSTRACT
Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.
Texte intégral:
1
Indice:
WPRIM
Type d'étude:
Prognostic_studies
langue:
Zh
Texte intégral:
J. biomed. eng
/
Sheng wu yi xue gong cheng xue za zhi
Année:
2018
Type:
Article