JLeNeT: Jaccard LeNet用于在物联网环境中使用语音信号检测帕金森病和严重程度分类
Sundaresan Pragadeeswaran1, Subramanian Kannimuthu2
1School of computing, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
Network (Bristol, England)
|March 12, 2025
概括
这项研究介绍了帕金森病的Jaccard LeNet (JLeNet).
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 帕金森病 (PD) 是一种流行的神经退行性疾病.
- 早期检测和严重程度分类对于管理PD至关重要.
- 语音分析为PD评估提供了一种非侵入性方法.
研究的目的:
- 开发和评估一种使用语音信号检测和严重程度分类帕金森病的新方法.
- 将这种方法集成到物联网 (IoT) 环境中,用于模拟数据处理.
- 优化数据路由和信号处理,以提高诊断准确度.
主要方法:
- 语音信号被采集并使用适应式卡尔曼波器进行预处理.
- 进行了特征提取和选择,利用和平均相似性.
- 帕金森病的检测和分类是使用拟议的贾卡德LeNet (JLeNet) 模型实现的.
- 在物联网模拟中使用混合黑猩猩野生算法 (ChWGA) 进行高效的路由.
主要成果:
- 在能量和延迟指标上,ChWGA算法表现出卓越的性能 (分别为0.309 J和0.434 ms).
- JLeNet模型在PD检测和分类方面实现了0.910的高精度.
- 该系统显示真正阳性率 (TPR) 为0.903,真负率 (TNR) 为0.918.
结论:
- 拟议的JLeNet模型,结合物联网设置中的ChWGA路由,为从语音信号中检测帕金森病和严重程度分类提供了有效的方法.
- 该方法显示了远程和可访问的PD监控的巨大潜力.
- 集成先进的算法提高了神经退行性疾病诊断的效率和准确性.
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