基于样本增强的帕金森病识别算法研究  

Research on Parkinson’s disease recognition algorithm based on sample enhancement

作　　者：张子豪 赵德春[2] 王子琼 韦莉 ZHANG Zihao;ZHAO Dechun;WANG Ziqiong;Wei Li(Automation College,Chongqing University of Posts and Telecommunications,Chongqing 400065,P.R.China;Biomedical Information College,Chongqing University of Posts and Telecommunications,Chongqing 400065,P.R.China)

机构地区：[1]重庆邮电大学自动化学院,重庆400065 [2]重庆邮电大学生物信息学院,重庆400065

出　　处：《生物医学工程学杂志》2024年第1期17-25,33,共10页Journal of Biomedical Engineering

基　　金：重庆市自然科学基金(cstc2019jcyj-msxmX0275);重庆市研究生科研创新项目(CYS22460)。

摘　　要：帕金森病患者早期存在声带损伤,其声纹特征与健康人存在明显差异,可以利用该差异识别帕金森病,但帕金森病患者声纹数据样本不足,因此本文提出双自注意力深度卷积生成对抗网络模型进行样本增强,生成高分辨率的语谱图,进而采用深度学习方法进行帕金森病识别。该模型通过增加网络深度并结合梯度惩罚、频谱归一化技术改进样本的纹理清晰度,并且构建一个基于迁移学习的纯粹的卷积神经网络家族(ConvNeXt)作为分类网络,以此提取声纹特征并进行分类,提升了帕金森病识别准确率。在帕金森病语音数据集上进行本文算法有效性验证实验,对比样本增强前,本文所提模型生成的样本清晰度以及弗雷谢起始距离(FID)均得到提高,并且本文网络模型能够获得98.8%的准确率。本文研究结果表明,基于双自注意力深度卷积生成对抗网络样本增强的帕金森病识别算法能够准确区分健康人和帕金森病患者,有助于解决帕金森病早期识别声纹数据样本不足的问题。综上,本文方法有效提高小样本帕金森病语音数据集分类准确率,为早期帕金森病语音诊断提供了一种有效的解决思路。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 selfattention 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.

关 键 词：帕金森病 深度学习 样本增强 双自注意力机制 语谱图 

分 类 号：R742.5[医药卫生—神经病学与精神病学] TP183[医药卫生—临床医学]