Parkinson's disease voice data classification system based on sample and feature double transformation

Abstract

The invention relates to the technical field of voice classification, and particularly discloses a Parkinson's disease voice data classification system based on sample and feature double transformation, which comprises a sample input module, a sample transformation module, a data set division module, a feature transformation module, a model generation module and a voting module. The system is based on the characteristic that the number of existing PD voice samples is small, and transformation is particularly carried out on two dimensions of samples and features: for sample transformation, hierarchical structures of different PD voice samples are mined through an iterative mean value clustering method, and new samples are generated; for feature transformation, PD voice feature dimension transformation is carried out through different feature kernels. The sample transformation not only can reduce the influence of abnormal samples on the classifier boundary and the influence of the samples with high correlation on the training time and the storage space, but also can reflect the hierarchical structure information of the samples in the samples. Dimension reduction is carried out on PD voice samples through feature transformation, the complexity of a classification model is reduced, and high-performance classification is achieved.

Description

Technical field [0001] The present invention relates to the field of speech classification, and more particularly to a Passen Siden Voice Data Classification System based on samples and feature dual transformations. Background technique [0002] Parkinson Disease (PD) as a neurodegenerative disease of the central nervous system, the condition mainly includes tremor, stiffness, muscle control loss, slow exercise, poor equilibrium and speech problems. As of now, there is nearly 10 million affected global influence, and China is also a high-risk area of ​​Parkinson's disease. High quality biomarkers are the key to the diagnosis and treatment of Parkinson's disease. Unfortunately, reliable PD biomarkers have not yet been determined. However, recent studies have shown that voice signals (data) helps to identify Parkinson's disease from a healthy population, as most patients typically exert a pronunciation barrier. [0003] The number of PD speech samples has been the difficulty of PD ...

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Problems solved by technology

[0003] The small number of PD speech samples has always been a difficulty in the research of PD speech classification, and the lack of sample information leads to unsatisfactory training results

Moreover, the current Parkinson's speech classification technology does not consider the impact of abnormal samples on the classifier boundary, and the impact of highly correlated samples on training time and storage space, resulting in the possibility of highly correlated features in speech sample features Or noise and other features that have nothing to do with the target classification, the classification accuracy is not very high, and there is still more room for optimization

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