论文标题
帕金森氏病使用AI和自然语言知识转移
Parkinson's disease diagnostics using AI and natural language knowledge transfer
论文作者
论文摘要
在这项工作中，解决了帕金森氏病（PD）诊断的问题，该诊断使用了非侵入性反典礼技术。提出了一种对诊断为PD患者的原始语音记录分类的深度学习方法。提出的方法的核心是使用验证的自然语言模型的知识转移的音频分类器，即\ textit {wav2vec 2.0}。在一组38名PD患者和10名健康的人中测试了方法。构建了使用智能手机录音机获取的语音录音数据集，并将记录标记为PD/非PD，并使用Hoehn-Yahr量表进行了严重程度的疾病。将录音切成2141个样本，其中包括句子，音节，元音和持续发音。分类器得分高达97.92 \％的交叉验证精度。此外，论文介绍了人级绩效评估问卷的结果，该问卷已与神经病学专业人员进行了咨询
In this work, the issue of Parkinson's disease (PD) diagnostics using non-invasive antemortem techniques was tackled. A deep learning approach for classification of raw speech recordings in patients with diagnosed PD was proposed. The core of proposed method is an audio classifier using knowledge transfer from a pretrained natural language model, namely \textit{wav2vec 2.0}. Method was tested on a group of 38 PD patients and 10 healthy persons above the age of 50. A dataset of speech recordings acquired using a smartphone recorder was constructed and the recordings were label as PD/non-PD with severity of the disease additionally rated using Hoehn-Yahr scale. The audio recordings were cut into 2141 samples that include sentences, syllables, vowels and sustained phonation. The classifier scores up to 97.92\% of cross-validated accuracy. Additionally, paper presents results of a human-level performance assessment questionnaire, which was consulted with the neurology professionals
