波纹散射转换基于多普勒信号的分类
Ab Waheed Lone1, Nizamettin Aydin1
1Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey.
Computers in biology and medicine
|November 1, 2023
概括
波形散射转换有效地分类跨的多普勒信号,用于检测大脑栓塞,这是主要的中风原因. 这种方法达到98.89%的准确性,为预防中风提供了一个有前途的工具.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 大脑血管中的血块 (血栓) 破坏了血液的流动,导致中风.
- 跨骨多普勒 (TCD) 信号可以表明栓塞的存在.
- 目前用于TCD分析的信号处理和机器学习方法存在局限性.
研究的目的:
- 为了评估波纹散射变形 (WST) 用于分类TCD信号.
- 评估WST在识别栓塞,多普勒斑点和文物方面的有效性.
- 将基于WST的分类与传统方法进行比较.
主要方法:
- 利用波纹散射转换,一种翻译不变算法,用于从TCD信号中提取特征.
- 训练有素的机器学习分类器包括支持向量机 (SVM),k-最近邻居和天真贝叶斯在300个TCD信号的数据集上.
- 与手工制作的连续波纹变形 (CWT) 特性进行了比较分析.
主要成果:
- 使用SVM的波纹散射变换实现了98.89%的分类准确度.
- WST表现出强度和有效性,特别是在小型数据集上.
- 使用WST特征的高斯过程回归使用零顺序系数产生了34.95%的预测损失.
结论:
- WST是一种非常有效的工具,用于分类TCD信号以检测大脑栓塞.
- 该方法显示了改善中风诊断和预防策略的巨大潜力.
- 与传统的CWT功能相比,WST在处理信号变化和小数据集方面具有优势.
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