一种基于人工智能的新方法来对帕金森病进行分类,使用复杂和大声特征
Rahul Nijhawan1, Mukul Kumar2, Sahitya Arya3
1Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India.
Biomimetics (Basel, Switzerland)
|August 25, 2023
概括
本研究介绍了一种使用语音分析检测帕金森病 (PD) 的AI模型,其性能优于现有的方法. 基于变压器的方法为早期PD识别提供了更准确,更有效的系统.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 语音科学 语言科学
背景情况:
- 帕金森病 (PD) 是一种影响运动功能的神经退行性疾病,在老年人群中患病率增加.
- 早期发现PD对于管理症状和改善患者的治疗结果至关重要.
- 目前的诊断方法可能具有侵入性或缺乏早期检测的敏感性.
研究的目的:
- 开发一个准确的人工智能 (AI) 模型,以使用语音录音来早期检测帕金森病.
- 研究基于变压器的神经网络的疗效,用于分析PD的声音生物标志物.
- 将拟议的人工智能模型的性能与最先进的方法进行比较.
主要方法:
- 开发了一个基于变压器的神经网络来分析从语音录音中提取的失声度.
- 基于XGBoost的特征选择方法和完全连接的神经网络层被用于持续性听障的测量.
- 拟议模型的性能与传统的机器学习技术 (MLP,SVM,随机森林) 和梯度增强决策树 (GBDT) 相比进行了评估.
主要成果:
- 基于变压器的AI模型与包括GBDT在内的最先进方法相比,实现了更高的性能,在曲线下的面积 (AUC) 中至少有1%的改善.
- 该模型显示了对帕金森病检测的提高精度和回忆得分.
- 该研究强调了变压器网络与更简单的多层感知器 (MLP) 网络相比,对增加深度的弹性.
结论:
- 开发的基于变压器的AI模型通过语音分析提供了一个精确而高效的系统,用于通过语音分析早期检测帕金森病.
- 基于神经网络的方法有利于分析语音特征,并为多式联络诊断解决方案提供潜力.
- 这项研究推进了深度学习的应用,用于分析失声症的措施,以改善神经系统疾病的诊断.
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