Research on Parkinson’s disease recognition algorithm based on sample enhancement_Journal of Biomedical Engineering

Authors：

ZHANG Zihao ¹ ,  ZHAO Dechun ² , WANG Ziqiong ² , Wei Li ²

1. Automation College, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China;
2. Biomedical Information College, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China;

Corresponding author：

ZHAO Dechun, Email: zhaodc@cqupt.edu.cn

Keywords：

Parkinson's disease; Deep learning; Sample enhancement; Double self-attention mechanism; Speech spectrogram

DOI：

10.7507/1001-5515.202304011

Video：

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Parkinson’s disease patients have early vocal cord damage, and their voiceprint characteristics differ significantly from those of healthy individuals, which can be used to identify Parkinson's disease. However, the samples of the voiceprint dataset of Parkinson's disease patients are insufficient, so this paper proposes a double self-attention deep convolutional generative adversarial network model for sample enhancement to generate high-resolution spectrograms, based on which deep learning is used to recognize Parkinson’s disease. This model improves the texture clarity of samples by increasing network depth and combining gradient penalty and spectral normalization techniques, and a family of pure convolutional neural networks (ConvNeXt) classification network based on Transfer learning is constructed to extract voiceprint features and classify them, which improves the accuracy of Parkinson’s disease recognition. The validation experiments of the effectiveness of this paper’s algorithm are carried out on the Parkinson’s disease speech dataset. Compared with the pre-sample enhancement, the clarity of the samples generated by the proposed model in this paper as well as the Fréchet inception distance (FID) are improved, and the network model in this paper is able to achieve an accuracy of 98.8%. The results of this paper show that the Parkinson’s disease recognition algorithm based on double self-attention deep convolutional generative adversarial network sample enhancement can accurately distinguish between healthy individuals and Parkinson’s disease patients, which helps to solve the problem of insufficient samples for early recognition of voiceprint data in Parkinson’s disease. In summary, the method effectively improves the classification accuracy of small-sample Parkinson's disease speech dataset and provides an effective solution idea for early Parkinson's disease speech diagnosis.

Citation： ZHANG Zihao, ZHAO Dechun, WANG Ziqiong, Wei Li. Research on Parkinson’s disease recognition algorithm based on sample enhancement. Journal of Biomedical Engineering, 2024, 41(1): 17-25, 33. doi: 10.7507/1001-5515.202304011 Copy

1.	陈芝君, 马建, 唐娜, 等. 中国帕金森病疾病负担变化趋势分析及预测. 中国慢性病预防与控制, 2022, 30(9): 649-654..
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4.	Zhang Y, Du H, Chen H, et al. Characteristic of voice in Parkinson disease. Journal of Audiology and Speech Pathology, 2001, 9(2): 84-86..
5.	王娟, 徐志京. HR-DCGAN方法的帕金森声纹样本扩充及识别研究. 小型微型计算机系统, 2019, 40(9): 2026-2032..
6.	Hireš M, Gazda M, Drotár P, et al. Convolutional neural network ensemble for Parkinson's disease detection from voice recordings. Computers in Biology and Medicine, 2022, 141: 105021..
7.	Sakar B E, Isenkul M E, Sakar C O, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE Journal of Biomedical and Health Informatics, 2013, 17(4): 828-834..
8.	Daoudi K, Das B, de Saint Victor S M, et al. A comparative study on vowel articulation in Parkinson’s disease and multiple system atrophy//The 23rd Annual Conference of the International Speech Communication Association, 2022: 1-26..
9.	Almeida J S, Rebouças Filho P P, Carneiro T, et al. Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recognition Letters, 2019, 125: 55-62..
10.	Benba A, Jilbab A, Hammouch A, et al. Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease//2015 International conference on electrical and information technologies. IEEE, 2015: 300-304..
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13.	Liu Z, Mao H, Wu C Y, et al. A Convnet for the 2020s//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans: IEEE, 2022: 11976-11986..
14.	Karani N, Erdil E, Chaitanya K, et al. Test-time adaptable neural networks for robust medical image segmentation. Medical Image Analysis, 2021, 68: 101907..
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17.	陈英, 林洪平, 张伟, 等. 医学图像数据集扩充方法研究进展. 生物医学工程学杂志, 2023, 40(1): 185-192..
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20.	黄宏宇, 谷子丰. 一种基于自注意力机制的文本图像生成对抗网络. 重庆大学学报, 2020, 43(3): 55-61..
21.	李秋丽, 马力. 结合频谱规范化与自注意力机制的DCGAN研究. 计算机应用与软件, 2021, 38(2): 227-232,290..
22.	甘岚, 沈鸿飞, 王瑶, 等. 基于改进DCGAN的数据增强方法. 计算机应用, 2021, 41(5): 1305-1313..
23.	祝俊辉, 周贤勇, 徐明升, 等. 改进DCGAN数据增强的番茄叶子病害图像识别. 无线电工程, 2023, 53(6): 1235-1241..
24.	Xie S, Qian W. Multi-head mutual self-attention generative adversarial network for texture synthesis//7th International Conference on Intelligent Computing and Signal Processing. IEEE, 2022: 1484-1487..
25.	MiyatoT, Ktaoka T, Koyama M, et al. Spectral normalization for generative adversarial network. arXiv preprint, 2018, arXiv: 1802.05957..
26.	Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks//International Conference on Machine Learning. PMLR, 2017: 214-223..
27.	Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of Wasserstein GANs// Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17), 2017: 5767-5777..
28.	于耀淋, 张景异, 雎付佳. 基于生成式对抗网络的人脸图像生成. 沈阳理工大学学报, 2022, 41(5): 29-33..
29.	Zhang H, Goodfellow I, Metaxas D, et al. Self-attention generative adversarial networks//International Conference on Machine Learning. PMLR, 2019: 7354-7363..
30.	李祚林, 李晓辉, 马灵玲, 等. 面向无参考图像的清晰度评价方法研究. 遥感技术与应用, 2011, 26(2): 239-246..

1. 陈芝君, 马建, 唐娜, 等. 中国帕金森病疾病负担变化趋势分析及预测. 中国慢性病预防与控制, 2022, 30(9): 649-654..
2. Rusz J, Tykalová T, Klempíř J, et al. Effects of dopaminergic replacement therapy on motor speech disorders in Parkinson’s disease: longitudinal follow-up study on previously untreated patients. Journal of Neural Transmission, 2016, 123(4): 379-387..
3. Walsh B, Smith A. Basic parameters of articulatory movements and acoustics in individuals with Parkinson's disease. Movement Disorders, 2012, 27(7): 843-850..
4. Zhang Y, Du H, Chen H, et al. Characteristic of voice in Parkinson disease. Journal of Audiology and Speech Pathology, 2001, 9(2): 84-86..
5. 王娟, 徐志京. HR-DCGAN方法的帕金森声纹样本扩充及识别研究. 小型微型计算机系统, 2019, 40(9): 2026-2032..
6. Hireš M, Gazda M, Drotár P, et al. Convolutional neural network ensemble for Parkinson's disease detection from voice recordings. Computers in Biology and Medicine, 2022, 141: 105021..
7. Sakar B E, Isenkul M E, Sakar C O, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE Journal of Biomedical and Health Informatics, 2013, 17(4): 828-834..
8. Daoudi K, Das B, de Saint Victor S M, et al. A comparative study on vowel articulation in Parkinson’s disease and multiple system atrophy//The 23rd Annual Conference of the International Speech Communication Association, 2022: 1-26..
9. Almeida J S, Rebouças Filho P P, Carneiro T, et al. Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recognition Letters, 2019, 125: 55-62..
10. Benba A, Jilbab A, Hammouch A, et al. Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease//2015 International conference on electrical and information technologies. IEEE, 2015: 300-304..
11. Wu K, Zhang D, Lu G, et al. Learning acoustic features to detect Parkinson’s disease. Neurocomputing, 2018, 318: 102-108..
12. Xu Z, Wang R, Wang J, et al. Parkinson’s disease detection based on spectrogram-deep convolutional generative adversarial network sample augmentation. IEEE Access, 2020, 8: 206888-206900..
13. Liu Z, Mao H, Wu C Y, et al. A Convnet for the 2020s//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans: IEEE, 2022: 11976-11986..
14. Karani N, Erdil E, Chaitanya K, et al. Test-time adaptable neural networks for robust medical image segmentation. Medical Image Analysis, 2021, 68: 101907..
15. Chen C, Hammernik K, Ouyang C, et al. Cooperative training and latent space data augmentation for robust medical image segmentation//Medical Image Computing and Computer Assisted Intervention, Strasbourg: Springer, 2021: 149-159..
16. Goodfellow I, Pouget-abadie J, Mirza M, et al. Generative adversarial networks//Communications of the ACM, 2020, 63(11): 139-144..
17. 陈英, 林洪平, 张伟, 等. 医学图像数据集扩充方法研究进展. 生物医学工程学杂志, 2023, 40(1): 185-192..
18. Frid-Adar M, Diamant I, Klang E, et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 2018, 321: 321-331..
19. Chuquicusma M J M, Hussein S, Burt J, et al. How to fool radiologists with generative adversarial networks? a visual turing test for lung cancer diagnosis//15th International Symposium on Biomedical Imaging. IEEE, 2018: 240-244..
20. 黄宏宇, 谷子丰. 一种基于自注意力机制的文本图像生成对抗网络. 重庆大学学报, 2020, 43(3): 55-61..
21. 李秋丽, 马力. 结合频谱规范化与自注意力机制的DCGAN研究. 计算机应用与软件, 2021, 38(2): 227-232,290..
22. 甘岚, 沈鸿飞, 王瑶, 等. 基于改进DCGAN的数据增强方法. 计算机应用, 2021, 41(5): 1305-1313..
23. 祝俊辉, 周贤勇, 徐明升, 等. 改进DCGAN数据增强的番茄叶子病害图像识别. 无线电工程, 2023, 53(6): 1235-1241..
24. Xie S, Qian W. Multi-head mutual self-attention generative adversarial network for texture synthesis//7th International Conference on Intelligent Computing and Signal Processing. IEEE, 2022: 1484-1487..
25. MiyatoT, Ktaoka T, Koyama M, et al. Spectral normalization for generative adversarial network. arXiv preprint, 2018, arXiv: 1802.05957..
26. Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks//International Conference on Machine Learning. PMLR, 2017: 214-223..
27. Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of Wasserstein GANs// Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17), 2017: 5767-5777..
28. 于耀淋, 张景异, 雎付佳. 基于生成式对抗网络的人脸图像生成. 沈阳理工大学学报, 2022, 41(5): 29-33..
29. Zhang H, Goodfellow I, Metaxas D, et al. Self-attention generative adversarial networks//International Conference on Machine Learning. PMLR, 2019: 7354-7363..
30. 李祚林, 李晓辉, 马灵玲, 等. 面向无参考图像的清晰度评价方法研究. 遥感技术与应用, 2011, 26(2): 239-246..

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