基于层级化分数阶语谱图的帕金森病构音障碍分类

doi:10.3969/j.issn.0258-8021.2025.01.003

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Abstract Dysphonia is one of the early symptoms of Parkinson′s disease. Most of the existing deep learning-based classifications of dysphonia in Parkinson′s disease are based on spectrogram and convolutional neural network, but both of them suffer from deficiencies such as single angle and restricted receptive field, respectively, which lead to insufficient information extraction. This paper proposed a classification method for Parkinson′s disease based on hierarchical fractional spectrogram. Firstly, by adding angle rotation factors, the dysphonia signal was transformed into the fractional spectrogram to enhance the ability of extracting energy information from different angles. Then the parameters of the Swin Transformer network pre-trained on ImageNet were transferred and fine-tuned to solve the problem of small data size. Finally, the combination of hierarchical structure and shifted window-based self-attention mechanism expanded the receptive field and realized multi-scale information fusion, which effectively improved the Parkinson′s disease classification accuracy. The results on Database-1 (240 samples collected by the Department of Neurology of Medicine, Istanbul University) and Database-2 (1 404 samples collected by Tangshan Workers′ Hospital and Kailuan Mental Health Center) showed good stability of the proposed method and achieved accuracy of 97.80 % and 98.75 % on the two datasets, respectively, with better performance than all compared advanced methods. Our proposed method provides a new perspective for analyzing articulation disorders in Parkinson′s disease.

Key words： Parkinson′s disease dysphonia fractional spectrogram parameter transfer hierarchical structure

Received: 11 January 2024

PACS:

R318

Corresponding Authors: *E-mail: zhtao@ysu.edu.cn

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	Articles by authors
	Xue Zaifa
	Lu Huibin
	Lin Liqin
	Zhang Tao

Cite this article:

Xue Zaifa,Lu Huibin,Lin Liqin, et al. Classification of Dysphonia in Parkinson′s Disease Based on Hierarchical Fractional Spectrogram[J]. Chinese Journal of Biomedical Engineering, 2025, 44(1): 21-33.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.01.003 OR http://cjbme.csbme.org/EN/Y2025/V44/I1/21

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