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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Published on: July 24, 2019

[Research on Parkinson's disease recognition algorithm based on sample enhancement].

Zihao Zhang¹, Dechun Zhao², Ziqiong Wang²

¹Automation College, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|February 25, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep learning model to enhance voiceprint data for early Parkinson's disease (PD) detection. The enhanced data significantly improves the accuracy of identifying PD patients from their speech patterns.

Keywords:

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

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Area of Science:

Neurology
Artificial Intelligence
Signal Processing

Context:

Parkinson's disease (PD) affects vocal cord function, altering voiceprint characteristics.
Early detection of PD is crucial for effective management.
Existing voiceprint datasets for PD are often limited in size.

Purpose:

To develop an advanced deep learning model for enhancing limited Parkinson's disease voiceprint datasets.
To improve the accuracy of early Parkinson's disease recognition using enhanced speech spectrograms.

Summary:

A double self-attention deep convolutional generative adversarial network (GAN) was proposed for generating high-resolution speech spectrograms.
The model incorporates increased network depth, gradient penalty, and spectral normalization for enhanced sample clarity.
A ConvNeXt classification network utilizing transfer learning was employed for feature extraction and classification.

Impact:

The enhanced voiceprint samples demonstrated improved clarity and a better Fréchet inception distance (FID) score.
The Parkinson's disease recognition algorithm achieved a high accuracy of 98.8%.
This approach offers a viable solution for early Parkinson's disease diagnosis using speech analysis, particularly with small datasets.